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1
Daniele, Mario, and Elisa Raoli. "Early Warning Systems for financial crises prediction in private companies: Evidence from the Italian context." FINANCIAL REPORTING, no. 2 (December 2024): 133–61. https://doi.org/10.3280/fr2024-002006.
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Purpose: This study compares models for predicting business financial crises, fo-cusing on which are most effective. In light of the new European Directive on business failure, it highlights a trade-off between predictive accuracy and timeli-ness in static models and offers an alternative approach. Design/methodology/approach: This study examines the Italian early warning system (EWS), testing static alert indicators' predictive ability on a large sample of private companies. It then proposes a dynamic version of the EWS. Findings: The results show a trade-off between predictive ability and timeliness for static models. In contrast, a dynamic system is more accurate in predicting cri-sis events, allowing managers to take corrective actions. Originality: The results highlight the limitations of static prediction models and emphasize the potential of a simple dynamic model that is specifically designed for small- and medium-sized entities (SMEs). Practical implications: This study proposes a dynamic model tailored for SMEs, which are particularly vulnerable to financial crises. This insight can help managers and policymakers balance accurate predictions with timely interventions, especial-ly in European countries implementing crisis prediction models.
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Lin, Huan, Weiye Yu, and Zhan Lian. "Influence of Ocean Current Features on the Performance of Machine Learning and Dynamic Tracking Methods in Predicting Marine Drifter Trajectories." Journal of Marine Science and Engineering 12, no. 11 (October 28, 2024): 1933. http://dx.doi.org/10.3390/jmse12111933.
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Accurately and rapidly predicting marine drifter trajectories under conditions of information scarcity is critical for addressing maritime emergencies and conducting marine surveys with resource-limited unmanned vessels. Machine learning-based tracking methods, such as Long Short-Term Memory networks (LSTM), offer a promising approach for trajectory prediction in such scenarios. This study combines satellite observations and idealized simulations to compare the predictive performance of LSTM with a resource-dependent dynamic tracking method (DT). The results indicate that when driven solely by historical drifter paths, LSTM achieves better trajectory predictions when trained and tested on relative trajectory intervals rather than the absolute positions of individual trajectory points. In general, LSTM provides a more accurate geometric pattern of trajectories at the initial stages of forecasting, while DT offers superior accuracy in predicting specific trajectory positions. The velocity and curvature of ocean currents jointly influence the prediction quality of both methods. In regions characterized by active sub-mesoscale dynamics, such as the fast-flowing and meandering Kuroshio Current and Kuroshio Current Extension, DT predicts more reliable trajectory patterns but lacks precision in detailed position estimates compared to LSTM. However, in areas dominated by the fast but relatively straight North Equatorial Current, the performance of the two methods reverses. The two methods also demonstrate different tolerances for noise and sampling intervals. This study establishes a baseline for selecting machine learning methods for marine drifter prediction and highlights the limitations of AI-based predictions under data-scarce and resource-constrained conditions.
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Stoodley, Catherine J., and Peter T. Tsai. "Adaptive Prediction for Social Contexts: The Cerebellar Contribution to Typical and Atypical Social Behaviors." Annual Review of Neuroscience 44, no. 1 (July 8, 2021): 475–93. http://dx.doi.org/10.1146/annurev-neuro-100120-092143.
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Social interactions involve processes ranging from face recognition to understanding others’ intentions. To guide appropriate behavior in a given context, social interactions rely on accurately predicting the outcomes of one's actions and the thoughts of others. Because social interactions are inherently dynamic, these predictions must be continuously adapted. The neural correlates of social processing have largely focused on emotion, mentalizing, and reward networks, without integration of systems involved in prediction. The cerebellum forms predictive models to calibrate movements and adapt them to changing situations, and cerebellar predictive modeling is thought to extend to nonmotor behaviors. Primary cerebellar dysfunction can produce social deficits, and atypical cerebellar structure and function are reported in autism, which is characterized by social communication challenges and atypical predictive processing. We examine the evidence that cerebellar-mediated predictions and adaptation play important roles in social processes and argue that disruptions in these processes contribute to autism.
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Oh, Cheol, Stephen G. Ritchie, and Jun-Seok Oh. "Exploring the Relationship between Data Aggregation and Predictability to Provide Better Predictive Traffic Information." Transportation Research Record: Journal of the Transportation Research Board 1935, no. 1 (January 2005): 28–36. http://dx.doi.org/10.1177/0361198105193500104.
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Providing reliable predictive traffic information is a crucial element for successful operation of intelligent transportation systems. However, there are difficulties in providing accurate predictions mainly because of limitations in processing data associated with existing traffic surveillance systems and the lack of suitable prediction techniques. This study examines different aggregation intervals to characterize various levels of traffic dynamic representations and to investigate their effects on prediction accuracy. The relationship between data aggregation and predictability is explored by predicting travel times obtained from the inductive signature–based vehicle reidentification system on the I-405 freeway detector test bed in Irvine, California. For travel time prediction, this study employs three techniques: adaptive exponential smoothing, adaptive autoregressive model using Kalman filtering, and recurrent neural network with genetically optimized parameters. Finally, findings are discussed on suggestions for applying prediction techniques effectively.
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Siek, M., and D. P. Solomatine. "Nonlinear chaotic model for predicting storm surges." Nonlinear Processes in Geophysics 17, no. 5 (September 6, 2010): 405–20. http://dx.doi.org/10.5194/npg-17-405-2010.
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Abstract. This paper addresses the use of the methods of nonlinear dynamics and chaos theory for building a predictive chaotic model from time series. The chaotic model predictions are made by the adaptive local models based on the dynamical neighbors found in the reconstructed phase space of the observables. We implemented the univariate and multivariate chaotic models with direct and multi-steps prediction techniques and optimized these models using an exhaustive search method. The built models were tested for predicting storm surge dynamics for different stormy conditions in the North Sea, and are compared to neural network models. The results show that the chaotic models can generally provide reliable and accurate short-term storm surge predictions.
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Prasanna, Christopher, Jonathan Realmuto, Anthony Anderson, Eric Rombokas, and Glenn Klute. "Using Deep Learning Models to Predict Prosthetic Ankle Torque." Sensors 23, no. 18 (September 6, 2023): 7712. http://dx.doi.org/10.3390/s23187712.
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Inverse dynamics from motion capture is the most common technique for acquiring biomechanical kinetic data. However, this method is time-intensive, limited to a gait laboratory setting, and requires a large array of reflective markers to be attached to the body. A practical alternative must be developed to provide biomechanical information to high-bandwidth prosthesis control systems to enable predictive controllers. In this study, we applied deep learning to build dynamical system models capable of accurately estimating and predicting prosthetic ankle torque from inverse dynamics using only six input signals. We performed a hyperparameter optimization protocol that automatically selected the model architectures and learning parameters that resulted in the most accurate predictions. We show that the trained deep neural networks predict ankle torques one sample into the future with an average RMSE of 0.04 ± 0.02 Nm/kg, corresponding to 2.9 ± 1.6% of the ankle torque’s dynamic range. Comparatively, a manually derived analytical regression model predicted ankle torques with a RMSE of 0.35 ± 0.53 Nm/kg, corresponding to 26.6 ± 40.9% of the ankle torque’s dynamic range. In addition, the deep neural networks predicted ankle torque values half a gait cycle into the future with an average decrease in performance of 1.7% of the ankle torque’s dynamic range when compared to the one-sample-ahead prediction. This application of deep learning provides an avenue towards the development of predictive control systems for powered limbs aimed at optimizing prosthetic ankle torque.
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Bisola Oluwafadekemi Adegoke, Tolulope Odugbose, and Christiana Adeyemi. "Data analytics for predicting disease outbreaks: A review of models and tools." International Journal of Life Science Research Updates 2, no. 2 (April 30, 2024): 001–9. http://dx.doi.org/10.53430/ijlsru.2024.2.2.0023.
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The burgeoning field of data analytics has emerged as a pivotal force in the realm of public health, particularly in the context of predicting and mitigating disease outbreaks. This comprehensive review delves into the diverse landscape of models and tools employed in data analytics for disease outbreak prediction. With a focus on synthesizing existing knowledge, the paper aims to provide a nuanced understanding of the strengths, limitations, and future directions within this dynamic field. The review begins with an exploration of various models utilized for disease outbreak prediction, ranging from statistical approaches to machine learning models and epidemiological frameworks. Each model category is scrutinized for its efficacy in capturing the complexities inherent in infectious disease dynamics. Simultaneously, the paper investigates the array of tools and technologies leveraged in disease outbreak prediction, encompassing Geographic Information Systems (GIS), data visualization tools, and big data analytics platforms. A critical aspect of the review lies in the examination of diverse data sources contributing to predictive analytics. Epidemiological data, environmental factors, and the burgeoning influence of social media and web data are dissected for their roles in enhancing the accuracy and timeliness of outbreak predictions. Amidst the promises of data analytics, the paper navigates the challenges inherent in predicting disease outbreaks. Issues of data quality and availability, model complexity, interpretability, and ethical considerations are dissected, providing a holistic view of the hurdles that practitioners encounter. Drawing upon case studies and real-world applications, the review showcases instances where data analytics has proven successful in predicting disease outbreaks, shedding light on both triumphs and setbacks. The implications for public health, lessons learned from challenges, and the evolving role of data analytics in shaping global health preparedness are thoroughly discussed. As the paper unfolds, it illuminates future trends and innovations in the field, foreseeing the integration of advanced technologies, global collaboration for information sharing, and the adaptation of predictive analytics for emerging diseases. The review culminates in a comprehensive conclusion, summarizing key findings and emphasizing the potential transformative impact of data analytics on the landscape of disease outbreak prediction.
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Zhang, Xiaopeng. "Paris House Rental Price Index Prediction-A Classical Statistical Model Approach." Highlights in Science, Engineering and Technology 88 (March 29, 2024): 294–99. http://dx.doi.org/10.54097/q6kz2d72.
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The study focuses on predicting rental prices in Paris and aims to contribute to urban economics and data analytics. It analyzes a wide range of data sources, including historical rental prices, economic indicators, demographics, and regulations. The goal is to compare classical statistical models' prediction accuracy of these three models: ARIMA, dynamic regression, and random forest. The results reveal that the ARIMA model performs best, offering more accurate predictions. ARIMA relies on time series analysis, capturing complex patterns in rental prices, making it well-suited for dynamic real estate markets. The study also examines the impact of external factors like rent reference indices, house price indices, and unemployment rates on prediction accuracy. While these factors seem promising, the further analysis suggests they can introduce noise into predictions if not chosen carefully. In conclusion, this research contributes valuable insights for rental market stakeholders. The ARIMA model proves effective for rental price forecasting in Paris, emphasizing the importance of understanding intrinsic time series patterns. This study can guide decision-making for renters, landlords, and investors in Paris and similar urban areas, providing a better understanding of predictive modeling in housing markets.
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Nik Nurul Hafzan, Mat Yaacob, Deris Safaai, Mat Asiah, Mohamad Mohd Saberi, and Safaai Siti Syuhaida. "Review on Predictive Modelling Techniques for Identifying Students at Risk in University Environment." MATEC Web of Conferences 255 (2019): 03002. http://dx.doi.org/10.1051/matecconf/201925503002.
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Predictive analytics including statistical techniques, predictive modelling, machine learning, and data mining that analyse current and historical facts to make predictions about future or otherwise unknown events. Higher education institutions nowadays are under increasing pressure to respond to national and global economic, political and social changes such as the growing need to increase the proportion of students in certain disciplines, embedding workplace graduate attributes and ensuring that the quality of learning programs are both nationally and globally relevant. However, in higher education institution, there are significant numbers of students that stop their studies before graduation, especially for undergraduate students. Problem related to stopping out student and late or not graduating student can be improved by applying analytics. Using analytics, administrators, instructors and student can predict what will happen in future. Administrator and instructors can decide suitable intervention programs for at-risk students and before students decide to leave their study. Many different machine learning techniques have been implemented for predictive modelling in the past including decision tree, k-nearest neighbour, random forest, neural network, support vector machine, naïve Bayesian and a few others. A few attempts have been made to use Bayesian network and dynamic Bayesian network as modelling techniques for predicting at- risk student but a few challenges need to be resolved. The motivation for using dynamic Bayesian network is that it is robust to incomplete data and it provides opportunities for handling changing and dynamic environment. The trends and directions of research on prediction and identifying at-risk student are developing prediction model that can provide as early as possible alert to administrators, predictive model that handle dynamic and changing environment and the model that provide real-time prediction.
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Kim, Jeonghun, and Ohbyung Kwon. "A Model for Rapid Selection and COVID-19 Prediction with Dynamic and Imbalanced Data." Sustainability 13, no. 6 (March 11, 2021): 3099. http://dx.doi.org/10.3390/su13063099.
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The COVID-19 pandemic is threatening our quality of life and economic sustainability. The rapid spread of COVID-19 around the world requires each country or region to establish appropriate anti-proliferation policies in a timely manner. It is important, in making COVID-19-related health policy decisions, to predict the number of confirmed COVID-19 patients as accurately and quickly as possible. Predictions are already being made using several traditional models such as the susceptible, infected, and recovered (SIR) and susceptible, exposed, infected, and resistant (SEIR) frameworks, but these predictions may not be accurate due to the simplicity of the models, so a prediction model with more diverse input features is needed. However, it is difficult to propose a universal predictive model globally because there are differences in data availability by country and region. Moreover, the training data for predicting confirmed patients is typically an imbalanced dataset consisting mostly of normal data; this imbalance negatively affects the accuracy of prediction. Hence, the purposes of this study are to extract rules for selecting appropriate prediction algorithms and data imbalance resolution methods according to the characteristics of the datasets available for each country or region, and to predict the number of COVID-19 patients based on these algorithms. To this end, a decision tree-type rule was extracted to identify 13 data characteristics and a discrimination algorithm was selected based on those characteristics. With this system, we predicted the COVID-19 situation in four regions: Africa, China, Korea, and the United States. The proposed method has higher prediction accuracy than the random selection method, the ensemble method, or the greedy method of discriminant analysis, and prediction takes very little time.
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Liu, Liujun. "A Comparative Examination of Stock Market Prediction: Evaluating Traditional Time Series Analysis Against Deep Learning Approaches." Advances in Economics, Management and Political Sciences 55, no. 1 (December 1, 2023): 196–204. http://dx.doi.org/10.54254/2754-1169/55/20231008.
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The contemporary financial landscape is characterized by dynamic market behavior. Accurate predictions of stock price movements are not only of paramount importance for financial decision-makers but also pose a significant challenge due to the inherent complexities of financial markets. This research study delves into the realm of stock market prediction by employing a comprehensive approach that combines time series analysis and machine learning techniques. The main goal is to assess different models in predicting price trends, potentially reshaping stock market forecasts and emphasizing the need for tailored predictive approaches for individual stocks. The study focuses on the example of Apple Inc. (AAPL) stock data and aims to uncover the effectiveness of various models in forecasting its price trends. Our results emphasize that the LSTM model surpasses the conventional ARIMA model in terms of forecasting accuracy, suggesting a promising path for improving stock market predictions. This comparative exploration provides insights into the potential of machine learning models in refining stock market predictions and highlights the importance of tailoring predictive methodologies to individual stock behaviors.
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Judijanto, Loso, and Fristi Riandari. "Fuzzy logic framework for financial distress prediction: Enhancing corporate decision-making under uncertainty." International Journal of Basic and Applied Science 13, no. 1 (June 30, 2024): 1–13. http://dx.doi.org/10.35335/ijobas.v13i1.474.
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This research aims to develop an enhanced Fuzzy Logic Framework for Financial Distress Prediction to improve corporate decision-making under uncertainty. The primary objective is to address limitations in traditional fuzzy logic models, such as static rule bases and lack of adaptability to dynamic financial conditions. To achieve this, a time-dependent fuzzy logic system is proposed, incorporating real-time financial data and adaptive learning mechanisms to improve predictive accuracy over time. The research design involves creating a dynamic fuzzy rule base, assigning weights to rules based on predictive performance, and optimizing membership functions and rule weights using real-time data. The methodology applies the proposed framework to financial indicators such as liquidity, profitability, and leverage, with a numerical example demonstrating the system's effectiveness in predicting financial distress. The results show that the model can accurately predict financial distress levels, with a predicted distress value of 0.588 compared to an actual value of 0.6. The model’s ability to update rule weights and optimize predictions over time represents a significant improvement over static fuzzy logic models. This research fills a critical gap in financial distress prediction by introducing a dynamic, adaptive fuzzy logic framework that evolves with real-time data. The model offers significant implications for both academics and industry, providing a tool for more accurate risk assessment in volatile financial environments. However, further research is needed to refine the model’s computational efficiency and test its long-term predictive capabilities across different industries
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Yuan, Yihong, and Andrew Grayson Wylie. "Comparing Machine Learning and Time Series Approaches in Predictive Modeling of Urban Fire Incidents: A Case Study of Austin, Texas." ISPRS International Journal of Geo-Information 13, no. 5 (April 29, 2024): 149. http://dx.doi.org/10.3390/ijgi13050149.
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This study examines urban fire incidents in Austin, Texas using machine learning (Random Forest) and time series (Autoregressive integrated moving average, ARIMA) methods for predictive modeling. Based on a dataset from the City of Austin Fire Department, it addresses the effectiveness of these models in predicting fire occurrences and the influence of fire types and urban district characteristics on predictions. The findings indicate that ARIMA models generally excel in predicting most fire types, except for auto fires. Additionally, the results highlight the significant differences in model performance across urban districts, indicating an impact of local features on fire incidence prediction. The research offers insights into temporal patterns of specific fire types, which can provide useful input to urban planning and public safety strategies in rapidly developing cities. In addition, the findings also emphasize the need for tailored predictive models, based on local dynamics and the distinct nature of fire incidents.
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Lyu, Xiaozhong, Cuiqing Jiang, Yong Ding, Zhao Wang, and Yao Liu. "Sales Prediction by Integrating the Heat and Sentiments of Product Dimensions." Sustainability 11, no. 3 (February 11, 2019): 913. http://dx.doi.org/10.3390/su11030913.
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Online word-of-mouth (eWOM) disseminated on social media contains a considerable amount of important information that can predict sales. However, the accuracy of sales prediction models using big data on eWOM is still unsatisfactory. We argue that eWOM contains the heat and sentiments of product dimensions, which can improve the accuracy of prediction models based on multiattribute attitude theory. In this paper, we propose a dynamic topic analysis (DTA) framework to extract the heat and sentiments of product dimensions from big data on eWOM. Ultimately, we propose an autoregressive heat-sentiment (ARHS) model that integrates the heat and sentiments of dimensions into the benchmark predictive model to forecast daily sales. We conduct an empirical study of the movie industry and confirm that the ARHS model is better than other models in predicting movie box-office revenues. The robustness check with regard to predicting opening-week revenues based on a back-propagation neural network also suggests that the heat and sentiments of dimensions can improve the accuracy of sales predictions when the machine-learning method is used.
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Korbmacher, Raphael, and Antoine Tordeux. "Toward Better Pedestrian Trajectory Predictions: The Role of Density and Time-to-Collision in Hybrid Deep-Learning Algorithms." Sensors 24, no. 7 (April 8, 2024): 2356. http://dx.doi.org/10.3390/s24072356.
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Predicting human trajectories poses a significant challenge due to the complex interplay of pedestrian behavior, which is influenced by environmental layout and interpersonal dynamics. This complexity is further compounded by variations in scene density. To address this, we introduce a novel dataset from the Festival of Lights in Lyon 2022, characterized by a wide range of densities (0.2–2.2 ped/m2). Our analysis demonstrates that density-based classification of data can significantly enhance the accuracy of predictive algorithms. We propose an innovative two-stage processing approach, surpassing current state-of-the-art methods in performance. Additionally, we utilize a collision-based error metric to better account for collisions in trajectory predictions. Our findings indicate that the effectiveness of this error metric is density-dependent, offering prediction insights. This study not only advances our understanding of human trajectory prediction in dense environments, but also presents a methodological framework for integrating density considerations into predictive modeling, thereby improving algorithmic performance and collision avoidance.
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Halabi, Susan, Cai Li, and Sheng Luo. "Developing and Validating Risk Assessment Models of Clinical Outcomes in Modern Oncology." JCO Precision Oncology, no. 3 (December 2019): 1–12. http://dx.doi.org/10.1200/po.19.00068.
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The identification of prognostic factors and building of risk assessment prognostic models will continue to play a major role in 21st century medicine in patient management and decision making. Investigators often are interested in examining the relationship among host, tumor-related, and environmental variables in predicting clinical outcomes. We distinguish between static and dynamic prediction models. In static prediction modeling, variables collected at baseline typically are used in building models. On the other hand, dynamic predictive models leverage the longitudinal data of covariates collected during treatment or follow-up and hence provide accurate predictions of patients’ prognoses. To date, most risk assessment models in oncology have been based on static models. In this article, we cover topics related to the analysis of prognostic factors, centering on factors that are both relevant at the time of diagnosis or initial treatment and during treatment. We describe the types of risk prediction and then provide a brief description of the penalized regression methods. We then review the state-of-the art methods for dynamic prediction and compare the strengths and limitations of these methods. Although static models will continue to play an important role in oncology, developing and validating dynamic models of clinical outcomes need to take a higher priority. A framework for developing and validating dynamic tools in oncology seems to still be needed. One of the limitations in oncology that may constrain modelers is the lack of access to longitudinal biomarker data. It is highly recommended that the next generation of risk assessments consider longitudinal biomarker data and outcomes so that prediction can be continually updated.
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Jiang, Linxing Preston, and Rajesh P. N. Rao. "Dynamic predictive coding: A model of hierarchical sequence learning and prediction in the neocortex." PLOS Computational Biology 20, no. 2 (February 8, 2024): e1011801. http://dx.doi.org/10.1371/journal.pcbi.1011801.
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We introduce dynamic predictive coding, a hierarchical model of spatiotemporal prediction and sequence learning in the neocortex. The model assumes that higher cortical levels modulate the temporal dynamics of lower levels, correcting their predictions of dynamics using prediction errors. As a result, lower levels form representations that encode sequences at shorter timescales (e.g., a single step) while higher levels form representations that encode sequences at longer timescales (e.g., an entire sequence). We tested this model using a two-level neural network, where the top-down modulation creates low-dimensional combinations of a set of learned temporal dynamics to explain input sequences. When trained on natural videos, the lower-level model neurons developed space-time receptive fields similar to those of simple cells in the primary visual cortex while the higher-level responses spanned longer timescales, mimicking temporal response hierarchies in the cortex. Additionally, the network’s hierarchical sequence representation exhibited both predictive and postdictive effects resembling those observed in visual motion processing in humans (e.g., in the flash-lag illusion). When coupled with an associative memory emulating the role of the hippocampus, the model allowed episodic memories to be stored and retrieved, supporting cue-triggered recall of an input sequence similar to activity recall in the visual cortex. When extended to three hierarchical levels, the model learned progressively more abstract temporal representations along the hierarchy. Taken together, our results suggest that cortical processing and learning of sequences can be interpreted as dynamic predictive coding based on a hierarchical spatiotemporal generative model of the visual world.
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Mai, Weimin, Junxin Chen, and Xiang Chen. "Time-Evolving Graph Convolutional Recurrent Network for Traffic Prediction." Applied Sciences 12, no. 6 (March 10, 2022): 2842. http://dx.doi.org/10.3390/app12062842.
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Accurate traffic prediction is crucial to the construction of intelligent transportation systems. This task remains challenging because of the complicated and dynamic spatiotemporal dependency in traffic networks. While various graph-based spatiotemporal networks have been proposed for traffic prediction, most of them rely on predefined graphs from different views or static adaptive matrices. Some implicit dynamics of inter-node dependency may be neglected, which limits the performance of prediction. To address this problem and make more accurate predictions, we propose a traffic prediction model named Time-Evolving Graph Convolution Recurrent Network (TEGCRN), which takes advantage of time-evolving graph convolution to capture the dynamic inter-node dependency adaptively at different time slots. Specifically, we first propose a tensor-composing method to generate adaptive time-evolving adjacency graphs. Based on these time-evolving graphs and a predefined distance-based graph, a graph convolution module with mix-hop operation is applied to extract comprehensive inter-node information. Then the resulting graph convolution module is integrated into the Recurrent Neural Network structure to form an general predicting model. Experiments on two real-world traffic datasets demonstrate the superiority of TEGCRN over multiple competitive baseline models, especially in short-term prediction, which also verifies the effectiveness of time-evolving graph convolution in capturing more comprehensive inter-node dependency.
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Drisya, G. V., D. C. Kiplangat, K. Asokan, and K. Satheesh Kumar. "Deterministic prediction of surface wind speed variations." Annales Geophysicae 32, no. 11 (November 19, 2014): 1415–25. http://dx.doi.org/10.5194/angeo-32-1415-2014.
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Abstract. Accurate prediction of wind speed is an important aspect of various tasks related to wind energy management such as wind turbine predictive control and wind power scheduling. The most typical characteristic of wind speed data is its persistent temporal variations. Most of the techniques reported in the literature for prediction of wind speed and power are based on statistical methods or probabilistic distribution of wind speed data. In this paper we demonstrate that deterministic forecasting methods can make accurate short-term predictions of wind speed using past data, at locations where the wind dynamics exhibit chaotic behaviour. The predictions are remarkably accurate up to 1 h with a normalised RMSE (root mean square error) of less than 0.02 and reasonably accurate up to 3 h with an error of less than 0.06. Repeated application of these methods at 234 different geographical locations for predicting wind speeds at 30-day intervals for 3 years reveals that the accuracy of prediction is more or less the same across all locations and time periods. Comparison of the results with f-ARIMA model predictions shows that the deterministic models with suitable parameters are capable of returning improved prediction accuracy and capturing the dynamical variations of the actual time series more faithfully. These methods are simple and computationally efficient and require only records of past data for making short-term wind speed forecasts within practically tolerable margin of errors.
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Srinath, M. "Vehicular Traffic Flow Prediction Model Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (July 31, 2023): 109–12. http://dx.doi.org/10.22214/ijraset.2023.54576.
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Abstract: Efficient traffic flow prediction is crucial for effective traffic management and congestion reduction in urban areas. However, traditional statistical models often struggle to accurately capture the intricate dynamics of vehicular traffic flow, particularly under dynamic conditions. In this research project, we propose a novel approach that leverages deep learning techniques, specifically Long Short-Term Memory (LSTM) neural networks, AdaBoost, and gradient descent, to enhance the accuracy of traffic flow predictions .By harnessing historical traffic data, our model generates precise predictions for the next time step, empowering traffic managers to optimize signal timings and proactively reroute traffic. To boost the model's performance, we incorporate AdaBoost, which integrates LSTM predictions as additional input features. We evaluate the accuracy of our model using mean absolute error (MAE) and R2 score techniques, comparing the predicted traffic flow against the actual traffic flow .Experimental results demonstrate that our proposed model outperforms traditional statistical models, exhibiting lower MAE and higher R2 scores. This indicates its efficacy in accurately predicting traffic flow and presents a promising solution for traffic management and congestion reduction. Our research contributes to the advancement of traffic flow prediction models by offering a more reliable and accurate approach. Future work may explore the integration of real- time data streams and external factors, such as weather conditions and events, to further enhance prediction accuracy and effectively address dynamic traffic situations. By optimizing traffic management strategies, reducing congestion, and improving overall traffic flow efficiency, our proposed model holds significant potential for improving urban traffic conditions.
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Williams-Riquer, Francisco, Alexander Chmelnizkij, Diaa Alkateeb, and Jürgen Grabe. "Prediction of induced soil vibration during pile vibrodriving using Dynamic Mode Decomposition (DMD)." Journal of Physics: Conference Series 2909, no. 1 (December 1, 2024): 012002. https://doi.org/10.1088/1742-6596/2909/1/012002.
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Abstract This study investigates using the Dynamic Mode Decomposition (DMD) algorithm to perform approximations and time-ahead prediction of soil vibrations during the vibrodriving process. Geotechnical applications face challenges in modeling and predicting soil vibrations due to the soil’s heterogeneous nature. This study addresses this issue using a purely data-driven approach. Geophone data collected during pile installation using a vibrodriver were used to demonstrate the feasibility of the DMD algorithm. The research reveals that both the standard DMD and augmented DMD, which incorporate delay coordinates, can achieve accurate predictions, with the augmented DMD producing more accurate time-ahead predictions of the vibrations. The results emphasize the potential practical utility of data-driven methods for vibration prediction in geotechnical applications.
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Appiah, Rita, Alexander Heifetz, Derek Kultgen, Lefteri H. Tsoukalas, and Richard B. Vilim. "Dynamic Control of Sodium Cold Trap Purification Temperature Using LSTM System Identification." Energies 17, no. 24 (December 11, 2024): 6257. https://doi.org/10.3390/en17246257.
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This study investigates the dynamic regulation of the sodium cold trap purification temperature at Argonne National Laboratory’s liquid sodium test facility, employing long short-term memory (LSTM) system identification techniques. The investigation introduces an innovative hybrid approach by integrating model predictive control (MPC) based on first principles dynamic models with a multi-step time–frequency LSTM model in predicting the temperature profiles of a sodium cold trap purification system. The long short-term memory–model predictive controller (LSTM-MPC) model employs a sliding window scheme to gather training samples for multi-step prediction, leveraging historical data to construct predictive models that capture the non-linearities of the complex system dynamics without explicitly modeling the underlying physical processes. The performance of the LSTM-MPC and MPC were evaluated through simulation experiments, where both models were assessed on their capacity to maintain the cold trap temperature within predefined set-points while minimizing deviations and overshoots. Results obtained show how the data-driven LSTM-MPC model demonstrates stability and adaptability. In contrast, the traditional MPC model exhibits irregularities, particularly evident as overshoots around set-point limits, which can potentially compromise its effectiveness over long prediction time intervals. The findings obtained offer valuable insights into integrating data-driven techniques for enhancing real-time monitoring systems.
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Zhuang, Qian, and Lianghua Chen. "Dynamic Prediction of Financial Distress Based on Kalman Filtering." Discrete Dynamics in Nature and Society 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/370280.
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The widely used discriminant models currently for financial distress prediction have deficiencies in dynamics. Based on the dynamic nature of corporate financial distress, dynamic prediction models consisting of a process model and a discriminant model, which are used to describe the dynamic process and discriminant rules of financial distress, respectively, is established. The operation of the dynamic prediction is achieved by Kalman filtering algorithm. And a generaln-step-ahead prediction algorithm based on Kalman filtering is deduced in order for prospective prediction. An empirical study for China’s manufacturing industry has been conducted and the results have proved the accuracy and advance of predicting financial distress in such case.
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Xu, Ziqi, Jingwen Zhang, Jacob Greenberg, Madelyn Frumkin, Saad Javeed, Justin K. Zhang, Braeden Benedict, et al. "Predicting Multi-dimensional Surgical Outcomes with Multi-modal Mobile Sensing." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 8, no. 2 (May 13, 2024): 1–30. http://dx.doi.org/10.1145/3659628.
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Pre-operative prediction of post-surgical recovery for patients is vital for clinical decision-making and personalized treatments, especially with lumbar spine surgery, where patients exhibit highly heterogeneous outcomes. Existing predictive tools mainly rely on traditional Patient-Reported Outcome Measures (PROMs), which fail to capture the long-term dynamics of patient conditions before the surgery. Moreover, existing studies focus on predicting a single surgical outcome. However, recovery from spine surgery is multi-dimensional, including multiple distinctive but interrelated outcomes, such as pain interference, physical function, and quality of recovery. In recent years, the emergence of smartphones and wearable devices has presented new opportunities to capture longitudinal and dynamic information regarding patients' conditions outside the hospital. This paper proposes a novel machine learning approach, Multi-Modal Multi-Task Learning (M3TL), using smartphones and wristbands to predict multiple surgical outcomes after lumbar spine surgeries. We formulate the prediction of pain interference, physical function, and quality of recovery as a multi-task learning (MTL) problem. We leverage multi-modal data to capture the static and dynamic characteristics of patients, including (1) traditional features from PROMs and Electronic Health Records (EHR), (2) Ecological Momentary Assessment (EMA) collected from smartphones, and (3) sensing data from wristbands. Moreover, we introduce new features derived from the correlation of EMA and wearable features measured within the same time frame, effectively enhancing predictive performance by capturing the interdependencies between the two data modalities. Our model interpretation uncovers the complementary nature of the different data modalities and their distinctive contributions toward multiple surgical outcomes. Furthermore, through individualized decision analysis, our model identifies personal high risk factors to aid clinical decision making and approach personalized treatments. In a clinical study involving 122 patients undergoing lumbar spine surgery, our M3TL model outperforms a diverse set of baseline methods in predictive performance, demonstrating the value of integrating multi-modal data and learning from multiple surgical outcomes. This work contributes to advancing personalized peri-operative care with accurate pre-operative predictions of multi-dimensional outcomes.
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G, Mrs Gowri. "Prediction of Air Pollution in Smart Cities Using Machine Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 273–77. http://dx.doi.org/10.22214/ijraset.2021.39241.
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Abstract: Air-pollution is one of the main threats for developed societies. According to the World Health Organization (WHO), pollution is the main cause of deaths among children aged under five years. Smart cities are called to play a decisive role to increase such pollution in real-time. The increase in air pollution due to fossil fuel consumption as well as its ill effects on the climate has made air pollution forecasting an important research area in today’s times. Deployment of the Internet of things (IoT) based sensors has considerably changed the dynamics of predicting air quality. prediction of spatio-temporal data has been one of the major challenges in creating a good predictive model. There are many different approaches which have been used to create an accurate predictive model. Primitive predictive machine learning algorithms like simple linear regression have failed to produce accurate results primarily due to lack of computing power but also due to lack of optimization techniques. A recent development in deep learning as well as improvements in computing resources has increased the accuracy of predicting time series data. However, with large spatio-temporal data sets spanning over years. Employing regression models on the entire data can cause per date predictions to be corrupted. In this work, we look at dealing with pre-processing the times series. However, pre-processing involves a similarity measure, we explore the use of Dynamic Time Warping (DTW). K-means is then used to classify the spatio-temporal pollution data over a period of 16 years from 2000 to 2016. Here Mean Absolute error (MAE) and Root Mean Square Error (RMSE) have been used as evaluation criteria for the comparison of regression models. Keywords: Spatio-temporal data, Primitive predictive machine learning algorithms, regression models
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Islam, Md Sariful, and Thomas W. Crawford. "Assessment of Spatio-Temporal Empirical Forecasting Performance of Future Shoreline Positions." Remote Sensing 14, no. 24 (December 16, 2022): 6364. http://dx.doi.org/10.3390/rs14246364.
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Coasts and coastlines in many parts of the world are highly dynamic in nature, where large changes in the shoreline position can occur due to natural and anthropogenic influences. The prediction of future shoreline positions is of great importance in the better planning and management of coastal areas. With an aim to assess the different methods of prediction, this study investigates the performance of future shoreline position predictions by quantifying how prediction performance varies depending on the time depths of input historical shoreline data and the time horizons of predicted shorelines. Multi-temporal Landsat imagery, from 1988 to 2021, was used to quantify the rates of shoreline movement for different time period. Predictions using the simple extrapolation of the end point rate (EPR), linear regression rate (LRR), weighted linear regression rate (WLR), and the Kalman filter method were used to predict future shoreline positions. Root mean square error (RMSE) was used to assess prediction accuracies. For time depth, our results revealed that the higher the number of shorelines used in calculating and predicting shoreline change rates the better predictive performance was yielded. For the time horizon, prediction accuracies were substantially higher for the immediate future years (138 m/year) compared to the more distant future (152 m/year). Our results also demonstrated that the forecast performance varied temporally and spatially by time period and region. Though the study area is located in coastal Bangladesh, this study has the potential for forecasting applications to other deltas and vulnerable shorelines globally.
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Carton, Quinten, Bart Merema, and Hilde Breesch. "Recommendations for model identification for MPC of an all-Air HVAC system." E3S Web of Conferences 246 (2021): 11006. http://dx.doi.org/10.1051/e3sconf/202124611006.
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Rule-based control (RBC) strategies are often unable to execute the optimal control action, which leads to unnecessary energy consumption and suboptimal comfort. Model predictive control (MPC) is a dynamic control strategy for heating, ventilation and air-conditioning (HVAC) systems that is mostly more capable of performing optimal control actions. The identification process of predictive models is an essential aspect of MPC. However, this model identification process remains time consuming due to the large variation in buildings and systems. The aim of this paper is to determine guidelines to identify predictive grey-box models more time efficient, thus enhancing the applicability of MPC. This paper focusses on a case study building equipped with an all-air HVAC system, which combines ventilation, heating and cooling. Making both temperature and CO2-concentration key parameters to predict. The grey-box model represents an open zone in a landscaped office, making the influence of neighbouring zones an additional challenge. Different models for predicting the zone temperature and CO2-concentration are identified, evaluated and validated using CTSM-R. The following aspects are studied: the dataset size, the influence of neighbouring zones, the difference between winter and summer conditions, number of states and the prediction horizon. A three state RC-model with the implementation of the zone temperature of one neighbouring zone is preferred for predicting the indoor temperature with an acceptable prediction horizon of one day. However, this temperature model is not suitable during sunny periods. A simple model representing a mass balance obtains accurate predictions of the zone CO2-concentration for a timestep of 15 minutes. For both model types the utilization of 5-day datasets is favoured over 12-day datasets due to a shorter monitoring period, lower prediction error and an easier parameter convergence. The usage of 12-day datasets is only preferred when an accurate estimation of the thermal inertia is pursued.
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Geweke, John, and Gianni Amisano. "Prediction with Misspecified Models." American Economic Review 102, no. 3 (May 1, 2012): 482–86. http://dx.doi.org/10.1257/aer.102.3.482.
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The assumption that one of a set of prediction models is a literal description of reality formally underlies many formal econometric methods, including Bayesian model averaging and most approaches to model selection. Prediction pooling does not invoke this assumption and leads to predictions that improve on those based on Bayesian model averaging, as assessed by the log predictive score. The paper shows that the improvement is substantial using a pool consisting of a dynamic stochastic general equilibrium model, a vector autoregression, and a dynamic factor model, in conjunction with standard US postwar quarterly macroeconomic time series.
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Kulkarni, N. M., A. Chandra, and S. S. Jagdale. "A Dynamic Model for End Milling Using Single Point Cutting Theory." Journal of Manufacturing Science and Engineering 118, no. 2 (May 1, 1996): 272–74. http://dx.doi.org/10.1115/1.2831021.
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The dynamics of a milling process can significantly influence the surface quality and integrity of the finished part. Accordingly, various researchers have investigated the dynamics of milling processes using a hierarchy of models. Tlusty and Smith (1991) provides a review of these models. In recent years, several other researchers (e.g., Armarego and Deshpande, 1989; Montgomery and Altintas, 1991; Nallakatla and Smith, 1992) have also continued to enhance various aspects of such dynamic models. While these dynamic models provide significant insights into the cutting characteristics of a milling process, their utilization in process design has proven to be elusive. The accuracy of these models, however, depends significantly on the prediction of cutting force characteristics. Under the current state-of-the-art, detailed experimentations using actual set-up are necessary to make such predictions accurately. Experimentally obtained constants can vary widely from one milling situation to another, which in turn, significantly restricts their usefulness as predictive tools for process design.
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Kim, Donghyun, Heechan Han, Wonjoon Wang, Yujin Kang, Hoyong Lee, and Hung Soo Kim. "Application of Deep Learning Models and Network Method for Comprehensive Air-Quality Index Prediction." Applied Sciences 12, no. 13 (July 1, 2022): 6699. http://dx.doi.org/10.3390/app12136699.
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Accurate pollutant prediction is essential in fields such as meteorology, meteorological disasters, and climate change studies. In this study, long short-term memory (LSTM) and deep neural network (DNN) models were applied to six pollutants and comprehensive air-quality index (CAI) predictions from 2015 to 2020 in Korea. In addition, we used the network method to find the best data sources that provide factors affecting comprehensive air-quality index behaviors. This study had two steps: (1) predicting the six pollutants, including fine dust (PM10), fine particulate matter (PM2.5), ozone (O3), sulfurous acid gas (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO) using the LSTM model; (2) forecasting the CAI using the six predicted pollutants in the first step as predictors of DNNs. The predictive ability of each model for the six pollutants and CAI prediction was evaluated by comparing it with the observed air-quality data. This study showed that combining a DNN model with the network method provided a high predictive power, and this combination could be a remarkable strength in CAI prediction. As the need for disaster management increases, it is anticipated that the LSTM and DNN models with the network method have ample potential to track the dynamics of air pollution behaviors.
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Abishek, B. Ebenezer, Vijayalakshmi A, Blessy Sharon Gem, and P. Sathish Kumar. "ULTRA WIDE-BAND SYSTEMS WITH ENSEMBLES OF CLASSIFIERS BASED LATENT GRAPH PREDICTOR FM FOR OPTIMAL RESOURCE PREDICTION." ICTACT Journal on Communication Technology 14, no. 4 (December 1, 2023): 3043–49. http://dx.doi.org/10.21917/ijct.2023.0453.
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The proliferation of Ultra Wide-Band (UWB) systems has introduced new challenges in predicting optimal resource allocation, necessitating advanced methodologies to enhance efficiency. Current resource prediction models for UWB systems often struggle to accurately forecast optimal resource allocation due to the dynamic and complex nature of the communication environment. This study aims to overcome these limitations by introducing a novel framework that integrates machine learning ensembles and latent graph predictor FM to achieve more accurate and reliable resource predictions. While various resource prediction models exist, a noticeable gap remains in achieving optimal predictions for UWB systems in dynamic scenarios. Existing models lack the adaptability and precision required for efficient resource allocation. This research bridges this gap by introducing a comprehensive approach that leverages ensembles of classifiers and latent graph predictor FM to enhance prediction accuracy. This study addresses the existing gaps in resource prediction by proposing an innovative approach that combines ensembles of classifiers with a Latent Graph Predictor FM. Our methodology involves the development of an integrated model that combines the strengths of machine learning ensembles and latent graph predictor FM. The ensemble of classifiers captures diverse patterns and features, while the latent graph predictor FM refines predictions based on latent relationships within the communication network. This dual-layered approach ensures robust and accurate resource prediction in UWB systems. The experimental results demonstrate a significant improvement in resource prediction accuracy compared to existing models. The proposed framework effectively adapts to dynamic UWB environments, providing optimal resource allocation in real-time scenarios. The study showcases the potential of ensembles of classifiers and latent graph predictor FM in addressing the challenges of resource prediction in UWB systems.
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Villegas Mier, Oscar, Anna Dittmann, Wiebke Herzberg, Holger Ruf, Elke Lorenz, Michael Schmidt, and Rainer Gasper. "Predictive Control of a Real Residential Heating System with Short-Term Solar Power Forecast." Energies 16, no. 19 (October 7, 2023): 6980. http://dx.doi.org/10.3390/en16196980.
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Predictive control has great potential in the home energy management domain. However, such controls need reliable predictions of the system dynamics as well as energy consumption and generation, and the actual implementation in the real system is associated with many challenges. This paper presents the implementation of predictive controls for a heat pump with thermal storage in a real single-family house with a photovoltaic rooftop system. The predictive controls make use of a novel cloud camera-based short-term solar energy prediction and an intraday prediction system that includes additional data sources. In addition, machine learning methods were used to model the dynamics of the heating system and predict loads using extensive measured data. The results of the real and simulated operation will be presented.
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Mo, Hanlin. "Comparative Analysis of Linear Regression, Polynomial Regression, and ARIMA Model for Short-term Stock Price Forecasting." Advances in Economics, Management and Political Sciences 49, no. 1 (December 1, 2023): 166–75. http://dx.doi.org/10.54254/2754-1169/49/20230509.
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This research investigates the effectiveness of three prominent stock price prediction methodologies: Linear Regression, Polynomial Regression, and AutoRegressive Integrated Moving Average (ARIMA) model. The study leverages one and a half years of historical data from Apple, Tesla, Amazon, and Nike stocks to predict average prices over the ensuing 14 days. The predictive efficacy of each model is tested against actual data, revealing their respective strengths and limitations. Linear Regression offers an overview of stock trends with limited intricacy, while Polynomial Regression delivers a more comprehensive understanding of price variations and cyclical trends. However, Polynomial Regression's reliability for predictions remains uncertain. In contrast, the ARIMA model, designed explicitly for short-term forecasting, demonstrates superior accuracy, correctly predicting seven out of eight scenarios. It should be noted that this is despite its assumptions of linearity and stationarity. The findings underscore the complexity of accurate stock market prediction and highlight the ARIMA model's reliability for short-term forecasts. Therefore, understanding the strengths and weaknesses of each model is crucial for improving stock price prediction techniques and for better grasping the complex dynamics of volatile stock markets.
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Kačur, Ján, Patrik Flegner, Milan Durdán, and Marek Laciak. "Prediction of Temperature and Carbon Concentration in Oxygen Steelmaking by Machine Learning: A Comparative Study." Applied Sciences 12, no. 15 (August 1, 2022): 7757. http://dx.doi.org/10.3390/app12157757.
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The basic oxygen steelmaking process (BOS) faces the issue of the absence of information about the melt temperature and the carbon concentration in the melt. Although deterministic models for predicting steelmaking process variables are being developed in metallurgical research, machine-learning models can model the nonlinearities of process variables and provide a good estimate of the target process variables. In this paper, five machine learning methods were applied to predict the temperature and carbon concentration in the melt at the endpoint of BOS. Multivariate adaptive regression splines (MARS), support-vector regression (SVR), neural network (NN), k-nearest neighbors (k-NN), and random forest (RF) methods were compared. Machine modeling was based on static and dynamic observations from many melts. In predicting from dynamic melting data, a method of pairing static and dynamic data to create a training set was proposed. In addition, this approach has been found to predict the dynamic behavior of temperature and carbon during melting. The results showed that the piecewise-cubic MARS model achieved the best prediction performance for temperature in testing on static and dynamic data. On the other hand, carbon predictions by machine models trained on joined static and dynamic data were more powerful. In the case of predictions from dynamic data, the best results were obtained by the k-NN-based model, i.e., carbon, and the piecewise-linear MARS model in the case of temperature. In contrast, the neural network recorded the lowest prediction performance in more tests.
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Lu, Ying, Xiaopeng Fan, Zhipan Zhao, and Xuepeng Jiang. "Dynamic Fire Risk Classification Prediction of Stadiums: Multi-Dimensional Machine Learning Analysis Based on Intelligent Perception." Applied Sciences 12, no. 13 (June 29, 2022): 6607. http://dx.doi.org/10.3390/app12136607.
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Stadium fires can easily cause massive casualties and property damage. The early risk prediction of stadiums will be able to reduce the incidence of fires by making corresponding fire safety management and decision making in an early and targeted manner. In the field of building fires, some studies apply data mining techniques and machine learning algorithms to the collected risk hazard data for fire risk prediction. However, most of these studies use all attributes in the dataset, which may degrade the performance of predictive models due to data redundancy. Furthermore, machine learning algorithms are numerous and applied to fewer stadium fires, and it is crucial to explore models suitable for predicting stadium fire risk. The purpose of this study was to identify salient features to build a model for predicting stadium fire risk predictions. In this study, we designed an index attribute threshold interval to classify and quantify different fire risk data. We then used Gradient Boosting-Recursive Feature Elimination (GB-RFE) and Pearson correlation analysis to perform efficient feature selection on risk feature attributes to find the most informative salient feature subsets. Two cross-validation strategies were employed to address the dataset imbalance problem. Using the smart stadium fire risk data set provided by the Wuhan Emergency Rescue Detachment, the optimal prediction model was obtained based on the identified significant features and six machine learning methods of 12 combination forms, and full features were input as an experimental comparison study. Five performance evaluation metrics were used to evaluate and compare the combined models. Results show that the best performing model had an F1 score of 81.9% and an accuracy of 93.2%. Meanwhile, by introducing a precision-recall curve to explain the actual classification performance of each model, AdaBoost achieves the highest Auprc score (0.78), followed by SVM (0.77), which reveals more stable performance under such imbalanced data.
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Ma, Junwei, Xiaoxu Niu, Huiming Tang, Yankun Wang, Tao Wen, and Junrong Zhang. "Displacement Prediction of a Complex Landslide in the Three Gorges Reservoir Area (China) Using a Hybrid Computational Intelligence Approach." Complexity 2020 (January 28, 2020): 1–15. http://dx.doi.org/10.1155/2020/2624547.
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Displacement prediction of reservoir landslide remains inherently uncertain since a complete understanding of the complex nonlinear, dynamic landslide system is still lacking. An appropriate quantification of predictive uncertainties is a key underpinning of displacement prediction and mitigation of reservoir landslide. A density prediction, offering a full estimation of the probability density for future outputs, is promising for quantification of the uncertainty of landslide displacement. In the present study, a hybrid computational intelligence approach is proposed to build a density prediction model of landslide displacement and quantify the associated predictive uncertainties. The hybrid computational intelligence approach consists of two steps: first, the input variables are selected through copula analysis; second, kernel-based support vector machine quantile regression (KSVMQR) is employed to perform density prediction. The copula-KSVMQR approach is demonstrated through a complex landslide in the Three Gorges Reservoir Area (TGRA), China. The experimental study suggests that the copula-KSVMQR approach is capable of construction density prediction by providing full probability density distributions of the prediction with perfect performance. In addition, different types of predictions, including interval prediction and point prediction, can be derived from the obtained density predictions with excellent performance. The results show that the mean prediction interval widths of the proposed approach at ZG287 and ZG289 are 27.30 and 33.04, respectively, which are approximately 60 percent lower than that obtained using the traditional bootstrap-extreme learning machine-artificial neural network (Bootstrap-ELM-ANN). Moreover, the obtained point predictions show great consistency with the observations, with correlation coefficients of 0.9998. Given the satisfactory performance, the presented copula-KSVMQR approach shows a great ability to predict landslide displacement.
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Zhang, Shaohu, Jianxiao Ma, Boshuo Geng, and Hanbin Wang. "Traffic flow prediction with a multi-dimensional feature input: A new method based on attention mechanisms." Electronic Research Archive 32, no. 2 (2024): 979–1002. http://dx.doi.org/10.3934/era.2024048.
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<abstract> <p>Accurately predicting traffic flow is an essential component of intelligent transportation systems. The advancements in traffic data collection technology have broadened the range of features that affect and represent traffic flow variations. However, solely inputting gathered features into the model without analysis might overlook valuable information, hindering the improvement of predictive performance. Furthermore, intricate dynamic relationships among various feature inputs could constrain the model's potential for further enhancement in predictive accuracy. Consequently, extracting pertinent features from datasets and modeling their mutual influence is critical in attaining heightened precision in traffic flow predictions. First, we perform effective feature extraction by considering the temporal dimension and inherent operating rules of traffic flow, culminating in Multivariate Time Series (MTS) data used as input for the model. Then, an attention mechanism is proposed based on the MTS input data. This mechanism assists the model in selecting pertinent time series for multivariate forecasting, mitigating inter-feature influence, and achieving accurate predictions through the concentration on crucial information. Finally, empirical findings from real highway datasets illustrate the enhancement of predictive accuracy attributed to the proposed features within the model. In contrast to conventional machine learning or attention-based deep learning models, the proposed attention mechanism in this study demonstrates superior accuracy and stability in MTS-based traffic flow prediction tasks.</p> </abstract>
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Zeng, Lingchao, Cheng Zhang, Pengfei Qin, Yejun Zhou, and Yaxing Cai. "One Method for Predicting Satellite Communication Terminal Service Demands Based on Artificial Intelligence Algorithms." Applied Sciences 14, no. 14 (July 10, 2024): 6019. http://dx.doi.org/10.3390/app14146019.
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This paper presents a traffic demand prediction method based on deep learning algorithms, aiming to address the dynamic traffic demands in satellite communication and enhance resource management efficiency. Integrating Seq2Seq and LSTM networks, the method improves prediction accuracy and applicability, especially for mobile terminals such as aviation and maritime ones. Unlike traditional approaches, it does not require extensive statistical data and can be generalized to real-world systems, providing stable long-term traffic demand predictions. This study utilizes real-world flight data mapped to corresponding satellite beams, allowing the precise prediction of beam-specific traffic demands. The results show that aggregating historical demand data for beams with similar trends achieves an average predictive Mean Squared Error (MSE) of 0.0007 and a maximum MSE fluctuation of 0.014, significantly outperforming predictions based on average values in terms of stability and accuracy. This novel solution for resource management in satellite communication ensures efficient and accurate long-term traffic demand predictions.
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Zhang, Fuhao, Wenbo Shi, Jian Zhang, Min Zeng, Min Li, and Lukasz Kurgan. "PROBselect: accurate prediction of protein-binding residues from proteins sequences via dynamic predictor selection." Bioinformatics 36, Supplement_2 (December 2020): i735—i744. http://dx.doi.org/10.1093/bioinformatics/btaa806.
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Abstract Motivation Knowledge of protein-binding residues (PBRs) improves our understanding of protein−protein interactions, contributes to the prediction of protein functions and facilitates protein−protein docking calculations. While many sequence-based predictors of PBRs were published, they offer modest levels of predictive performance and most of them cross-predict residues that interact with other partners. One unexplored option to improve the predictive quality is to design consensus predictors that combine results produced by multiple methods. Results We empirically investigate predictive performance of a representative set of nine predictors of PBRs. We report substantial differences in predictive quality when these methods are used to predict individual proteins, which contrast with the dataset-level benchmarks that are currently used to assess and compare these methods. Our analysis provides new insights for the cross-prediction concern, dissects complementarity between predictors and demonstrates that predictive performance of the top methods depends on unique characteristics of the input protein sequence. Using these insights, we developed PROBselect, first-of-its-kind consensus predictor of PBRs. Our design is based on the dynamic predictor selection at the protein level, where the selection relies on regression-based models that accurately estimate predictive performance of selected predictors directly from the sequence. Empirical assessment using a low-similarity test dataset shows that PROBselect provides significantly improved predictive quality when compared with the current predictors and conventional consensuses that combine residue-level predictions. Moreover, PROBselect informs the users about the expected predictive quality for the prediction generated from a given input protein. Availability and implementation PROBselect is available at http://bioinformatics.csu.edu.cn/PROBselect/home/index. Supplementary information Supplementary data are available at Bioinformatics online.
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Li, Jiale, Li Fan, Xuran Wang, Tiejiang Sun, and Mengjie Zhou. "Product Demand Prediction with Spatial Graph Neural Networks." Applied Sciences 14, no. 16 (August 9, 2024): 6989. http://dx.doi.org/10.3390/app14166989.
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In the rapidly evolving online marketplace, accurately predicting the demand for pre-owned items presents a significant challenge for sellers, impacting pricing strategies, product presentation, and marketing investments. Traditional demand prediction methods, while foundational, often fall short in addressing the dynamic and heterogeneous nature of e-commerce data, which encompasses textual descriptions, visual elements, geographic contexts, and temporal dynamics. This paper introduces a novel approach utilizing the Graph Neural Network (GNN) to enhance demand prediction accuracy by leveraging the spatial relationships inherent in online sales data, named SGNN. Drawing from the rich dataset provided in the fourth Kaggle competition, we construct a spatially aware graph representation of the marketplace, integrating advanced attention mechanisms to refine predictive accuracy. Our methodology defines the product demand prediction problem as a regression task on an attributed graph, capturing both local and global spatial dependencies that are fundamental to accurate predicting. Through attention-aware message propagation and node-level demand prediction, our model effectively addresses the multifaceted challenges of e-commerce demand prediction, demonstrating superior performance over traditional statistical methods, machine learning techniques, and even deep learning models. The experimental findings validate the effectiveness of our GNN-based approach, offering actionable insights for sellers navigating the complexities of the online marketplace. This research not only contributes to the academic discourse on e-commerce demand prediction but also provides a scalable and adaptable framework for future applications, paving the way for more informed and effective online sales strategies.
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Cao, Ren-Meng, Xiao Fan Liu, and Xiao-Ke Xu. "Why cannot long-term cascade be predicted? Exploring temporal dynamics in information diffusion processes." Royal Society Open Science 8, no. 9 (September 2021): 202245. http://dx.doi.org/10.1098/rsos.202245.
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Predicting information cascade plays a crucial role in various applications such as advertising campaigns, emergency management and infodemic controlling. However, predicting the scale of an information cascade in the long-term could be difficult. In this study, we take Weibo, a Twitter-like online social platform, as an example, exhaustively extract predictive features from the data, and use a conventional machine learning algorithm to predict the information cascade scales. Specifically, we compare the predictive power (and the loss of it) of different categories of features in short-term and long-term prediction tasks. Among the features that describe the user following network, retweeting network, tweet content and early diffusion dynamics, we find that early diffusion dynamics are the most predictive ones in short-term prediction tasks but lose most of their predictive power in long-term tasks. In-depth analyses reveal two possible causes of such failure: the bursty nature of information diffusion and feature temporal drift over time. Our findings further enhance the comprehension of the information diffusion process and may assist in the control of such a process.
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Sun, Sihan, Minming Gu, and Tuoqi Liu. "Adaptive Sliding Window–Dynamic Time Warping-Based Fluctuation Series Prediction for the Capacity of Lithium-Ion Batteries." Electronics 13, no. 13 (June 26, 2024): 2501. http://dx.doi.org/10.3390/electronics13132501.
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Accurately predicting the capacity of lithium-ion batteries is crucial for improving battery reliability and preventing potential incidents. Current prediction models for predicting lithium-ion battery capacity fluctuations encounter challenges like inadequate fitting and suboptimal computational efficiency. This study presents a new approach for fluctuation prediction termed ASW-DTW, which integrates Adaptive Sliding Window (ASW) and Dynamic Time Warping (DTW). Initially, this approach leverages Empirical Mode Decomposition (EMD) to preprocess the raw battery capacity data and extract local fluctuation components. Subsequent to this, DTW is employed to forecast the fluctuation sequence through pattern-matching methods. Additionally, to boost model precision and versatility, a feature recognition-based ASW technique is used to determine the optimal window size for the current segment and assist in DTW-based predictions. The study concludes with capacity fluctuation prediction experiments carried out across various lithium-ion battery models. The results demonstrate the efficacy and extensive applicability of the proposed method.
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AlQahtani, Nasser A., Timothy J. Rogers, and Neil D. Sims. "Towards nonlinear model predictive control of flexible structures using Gaussian Processes." Journal of Physics: Conference Series 2909, no. 1 (December 1, 2024): 012004. https://doi.org/10.1088/1742-6596/2909/1/012004.
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Abstract In recent years, there is a growing interest of using and implementing data driven control in structural dynamics. This study considers applying Nonlinear Model Predictive Control (NMPC) to flexible structures by utilising recent developments in models which have been learnt from example data, i.e. machine learning approaches. The Gaussian process (GP) is a Bayesian machine learning algorithm identified for use as a black-box model in NMPC; it provides both the prediction of the system output and the associated confidence. In a control context, a GP can be utilised as a discrepancy model for linear or nonlinear flexible dynamic structures within MPC or even as the nonlinear model of the system itself. The Nonlinear Output Error model (GP-NOE) is a popular GP structure for dynamic systems that is utilised in predictive control strategies and requires predictions to be propagated to the control horizon. This novel framework is evaluated on a cantilever beam with light damping, and the results demonstrate robust control performance in both tracking and regulator tasks. The positive results inspire additional investigation into the proposed technique, particularly in the setting of a fully nonlinear system with unknown dynamics, such as an actuator within the flexible structure.
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Wu, Tengtao. "High throughput screening of thermal interface materials by machine learning." Applied and Computational Engineering 61, no. 1 (May 8, 2024): 77–86. http://dx.doi.org/10.54254/2755-2721/61/20240930.
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Till now, it remains a challenge for effective prediction and screening of novel materials with high thermal conductivity, as well as further optimization of the interface thermal resistance. Normally, people have to spend long time on tedious calculations when predicting and screening these materials. In this paper, I combined machine learning with molecular dynamics simulations to investigate the thermal conductive properties of materials with the aim of significantly reducing computational consumption. I first applied molecular dynamics simulations to obtain the relevant properties of materials, then generated models for predicting physical properties by machine learning, and finally made predictions of thermophysical properties of materials. The use of machine learning significantly reduces the prediction time compared to direct molecular dynamics simulations. Especially when the XGBoost and the neural network models are employed, the prediction efficiency is significantly improved. This work guides a new way for the future screening of high-performance thermal interface materials.
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Zhang, Junling, Min Mei, Jun Wang, Guangpeng Shang, Xuefeng Hu, Jing Yan, and Qian Fang. "The Construction and Application of a Deep Learning-Based Primary Support Deformation Prediction Model for Large Cross-Section Tunnels." Applied Sciences 14, no. 2 (January 21, 2024): 912. http://dx.doi.org/10.3390/app14020912.
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The deformation of tunnel support structures during tunnel construction is influenced by geological factors, geometrical factors, support factors, and construction factors. Accurate prediction of tunnel support structure deformation is crucial for engineering safety and optimizing support parameters. Traditional methods for tunnel deformation prediction have often relied on numerical simulations and model experiments, which may not always meet the time-sensitive requirements. In this study, we propose a fusion deep neural network (FDNN) model that combines multiple algorithms with a complementary tunnel information encoding method. The FDNN model utilizes Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to extract features related to tunnel structural deformation. FDNN model is used to predict deformations in the Capital Ring Expressway, and the predictions align well with monitoring results. To demonstrate the superiority of the proposed model, we use four different performance evaluation metrics to analyze the predictive performance of FDNN, DNN, XGBoost, Decision Tree Regression (DTR), and Random Forest Regression (RFR) methods. The results indicate that FDNN exhibits high precision and robustness. To assess the impact of different data types on the predictive results, we use tunnel geometry data as the base and combine geological, support, and construction data. The analysis reveals that models trained on datasets comprising all four data types perform the best. Geological parameters have the most significant impact on the predictive performance of all models. The findings of this research guide predicting tunnel construction parameters, particularly in the dynamic design of support parameters.
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Pipin, Sio Jurnalis, Ronsen Purba, and Heru Kurniawan. "Prediksi Saham Menggunakan Recurrent Neural Network (RNN-LSTM) dengan Optimasi Adaptive Moment Estimation." Journal of Computer System and Informatics (JoSYC) 4, no. 4 (August 25, 2023): 806–15. http://dx.doi.org/10.47065/josyc.v4i4.4014.
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Predicting stock price movements is a complex challenge in the financial market due to unpredictable price fluctuations and high sensitivity levels. Noise in historical stock price data and temporal dependencies between previous and current prices make recognizing price movement patterns difficult. In a dynamic market environment, the model's ability to generate accurate predictions holds significant implications for more informed investment decision-making. The Recurrent Neural Network - Long Short-Term Memory (RNN-LSTM) model holds great potential for stock price prediction. It captures temporal dependencies, identifies non-linear relationships, and deciphers complex trends in stock price data. This study employs deep learning techniques with the RNN-LSTM model optimized using Adaptive Moment Estimation (Adam) to enhance stock price prediction accuracy by leveraging historical stock price data and technical factors. Data preprocessing, including handling missing values and data normalization, aids the model in navigating the dataset's intricacies. Test results utilizing the Mean Squared Error (MSE) metric reveal the model's ability to produce predictions that closely resemble actual stock prices, with a low loss value of 0109012. The model also exhibits good predictive accuracy, as evidenced by a favorable Mean Percentage Error (MPE) score of 1.74% between predicted and actual values. These findings hold valuable implications for assisting investors and financial practitioners in managing complexity and uncertainty within the stock market
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Long, Hao, Feng Hu, and Lingjun Kong. "Enhanced Link Prediction and Traffic Load Balancing in Unmanned Aerial Vehicle-Based Cloud-Edge-Local Networks." Drones 8, no. 10 (September 27, 2024): 528. http://dx.doi.org/10.3390/drones8100528.
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With the advancement of cloud-edge-local computing, Unmanned Aerial Vehicles (UAVs), as flexible mobile nodes, offer novel solutions for dynamic network deployment. However, existing research on UAV networks faces substantial challenges in accurately predicting link dynamics and efficiently managing traffic loads, particularly in highly distributed and rapidly changing environments. These limitations result in inefficient resource allocation and suboptimal network performance. To address these challenges, this paper proposes a UAV-based cloud-edge-local network resource elastic scheduling architecture, which integrates the Graph-Autoencoder–GAN-LSTM (GA–GLU) algorithm for accurate link prediction and the FlowBender-Enhanced Reinforcement Learning for Load Balancing (FERL-LB) algorithm for dynamic traffic load balancing. GA–GLU accurately predicts dynamic changes in UAV network topologies, enabling adaptive and efficient scheduling of network resources. FERL-LB leverages these predictions to optimize traffic load balancing within the architecture, enhancing both performance and resource utilization. To validate the effectiveness of GA–GLU, comparisons are made with classical methods such as CN and Katz, as well as modern approaches like Node2vec and GAE–LSTM, which are commonly used for link prediction. Experimental results demonstrate that GA–GLU consistently outperforms these competitors in metrics such as AUC, MAP, and error rate. The integration of GA–GLU and FERL-LB within the proposed architecture significantly improves network performance in highly dynamic environments.
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Liu, Xiao Kang, Ji Sen Yang, Zhong Hua Gao, and Dong Lin Peng. "Position Predictive Measurement Method for Time Grating CNC Rotary Table." Advanced Materials Research 139-141 (October 2010): 1587–90. http://dx.doi.org/10.4028/www.scientific.net/amr.139-141.1587.
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Time grating sensor transforms space domain information to time domain and measures spatial displacement with time. To develop high precision time grating CNC rotary table and reduce the dynamic position feedback error of the table, circular position predictive measurement method is proposed for transforming time domain information back to the space domain based on time-space transformation technology. Predicted values are obtained by modeling the measured values with time series theory, and the last prediction error is corrected in real time using the current measured values. Modeling method and parameter estimation algorithm are presented. To confirm the validity of the position prediction method, an experimental system is designed. The dynamic prediction error of the rotary table circular position is 2, and precise predicting is achieved.
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Gevorgian, Aleksandr, Giovanni Pernigotto, and Andrea Gasparella. "Addressing Data Scarcity in Solar Energy Prediction with Machine Learning and Augmentation Techniques." Energies 17, no. 14 (July 9, 2024): 3365. http://dx.doi.org/10.3390/en17143365.
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The accurate prediction of global horizontal irradiance (GHI) is crucial for optimizing solar power generation systems, particularly in mountainous areas with complex topography and unique microclimates. These regions face significant challenges due to limited reliable data and the dynamic nature of local weather conditions, which complicate accurate GHI measurement. The scarcity of precise data impedes the development of reliable solar energy prediction models, impacting both economic and environmental outcomes. To address these data scarcity challenges in solar energy prediction, this paper focuses on various locations in Europe and Asia Minor, predominantly in mountainous regions. Advanced machine learning techniques, including random forest (RF) and extreme gradient boosting (XGBoost) regressors, are employed to effectively predict GHI. Additionally, optimizing training data distribution based on cloud opacity values and integrating synthetic data significantly enhance predictive accuracy, with R2 scores ranging from 0.91 to 0.97 across multiple locations. Furthermore, substantial reductions in root mean square error (RMSE), mean absolute error (MAE), and mean bias error (MBE) underscore the improved reliability of the predictions. Future research should refine synthetic data generation, optimize additional meteorological and environmental parameter integration, extend methodology to new regions, and test for predicting global tilted irradiance (GTI). The studies should expand training data considerations beyond cloud opacity, incorporating sky cover and sunshine duration to enhance prediction accuracy and reliability.
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Nguyen, Hoang, Christopher Bentley, Le Minh Kieu, Yushuai Fu, and Chen Cai. "Deep Learning System for Travel Speed Predictions on Multiple Arterial Road Segments." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 4 (April 2019): 145–57. http://dx.doi.org/10.1177/0361198119838508.
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Accurate travel speed prediction is a critical tool for incidence response management. The complex dynamics of transport systems render model-based prediction extremely challenging. However, the large amounts of available vehicle speed data contain the complex interdependencies of the target travel speed; the data itself can be used to generate accurate predictions using deep learning methods. In this work, a deep learning methodology involving feature generation, model development, and model deployment is presented. The authors demonstrate the high performance of deep learning methods (relative to more traditional benchmarks) in predicting travel speeds from 5–30 min in advance, for a challenging arterial road network. In this study, different deep learning architectures that exploit both spatial and temporal information for several time frames are compared and analyzed. Finally, the authors demonstrate the integration of their deep learning method into a visualization system that can be directly applied for vehicle speed prediction in real time. The model-selection analysis and data-to-visualization framework in this manuscript provide a step towards decision support for incident management; for practical implementation, the predictive power of deep learning models under incident conditions should continue to be investigated and improved.