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Ansah, Kwabena, Ismail Wafaa Denwar, and Justice Kwame Appati. "Intelligent Models for Stock Price Prediction." Journal of Information Technology Research 15, no. 1 (2022): 1–17. http://dx.doi.org/10.4018/jitr.298616.
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Prediction of the stock price is a crucial task as predicting it may lead to profits. Stock price prediction is a challenge owing to non-stationary and chaotic data. Thus, the projection becomes challenging among the investors and shareholders to invest the money to make profits. This paper is a review of stock price prediction, focusing on metrics, models, and datasets. It presents a detailed review of 30 research papers suggesting the methodologies, such as Support Vector Machine Random Forest, Linear Regression, Recursive Neural Network, and Long Short-Term Movement based on the stock price prediction. Aside from predictions, the limitations, and future works are discussed in the papers reviewed. The commonly used technique for achieving effective stock price prediction is the RF, LSTM, and SVM techniques. Despite the research efforts, the current stock price prediction technique has many limits. From this survey, it is observed that the stock market prediction is a complicated task, and other factors should be considered to accurately and efficiently predict the future.
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Fang, Yiheng. "Prediction of the Ammonia Nitrogen Content with Improved Grey Model by Markov Chain." Highlights in Science, Engineering and Technology 88 (March 29, 2024): 156–61. http://dx.doi.org/10.54097/zee1cd17.
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Water pollution prediction plays a crucial role in environmental protection and sustainable development. This study proposes an innovative approach to enhance the accuracy of water pollution prediction by combining the grey prediction model (GM) with Markov chain analysis. This research focuses on predicting the concentration of ammonia nitrogen (NH3-N) in Dongting Lake, a significant water body. Grey prediction models (GM) are utilized to forecast NH3-N content, addressing the challenge posed by incomplete or insufficient data. However, due to the dynamic nature of water quality indicators, GM models may have limitations in terms of accuracy. To overcome this issue, this study introduces the concept of Markov chains, incorporating historical state transitions into prediction models to achieve more precise forecasts. The research demonstrates a novel method for water pollution prediction that integrates GM models with Markov chain analysis, resulting in improved accuracy when predicting NH3-N concentrations. A comparison with traditional GM predictions highlights the effectiveness of this approach. The model's performance was evaluated using actual data from the China Automated Water Quality Monitoring Report. Combining grey prediction models with Markov chains outperforms traditional methods when it comes to predicting water pollution levels. The result contributes to advancing the field of water pollution forecasting by enhancing forecasting accuracy and providing informed decision support for environmental protection and management purposes.
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Siemens, Angela, Spencer J. Anderson, S. Rod Rassekh, Colin J. D. Ross, and Bruce C. Carleton. "A Systematic Review of Polygenic Models for Predicting Drug Outcomes." Journal of Personalized Medicine 12, no. 9 (2022): 1394. http://dx.doi.org/10.3390/jpm12091394.
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Polygenic models have emerged as promising prediction tools for the prediction of complex traits. Currently, the majority of polygenic models are developed in the context of predicting disease risk, but polygenic models may also prove useful in predicting drug outcomes. This study sought to understand how polygenic models incorporating pharmacogenetic variants are being used in the prediction of drug outcomes. A systematic review was conducted with the aim of gaining insights into the methods used to construct polygenic models, as well as their performance in drug outcome prediction. The search uncovered 89 papers that incorporated pharmacogenetic variants in the development of polygenic models. It was found that the most common polygenic models were constructed for drug dosing predictions in anticoagulant therapies (n = 27). While nearly all studies found a significant association with their polygenic model and the investigated drug outcome (93.3%), less than half (47.2%) compared the performance of the polygenic model against clinical predictors, and even fewer (40.4%) sought to validate model predictions in an independent cohort. Additionally, the heterogeneity of reported performance measures makes the comparison of models across studies challenging. These findings highlight key considerations for future work in developing polygenic models in pharmacogenomic research.
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Ben Shoham, Ofir, and Nadav Rappoport. "CPLLM: Clinical prediction with large language models." PLOS Digital Health 3, no. 12 (2024): e0000680. https://doi.org/10.1371/journal.pdig.0000680.
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We present Clinical Prediction with Large Language Models (CPLLM), a method that involves fine-tuning a pre-trained Large Language Model (LLM) for predicting clinical disease and readmission. We utilized quantization and fine-tuned the LLM using prompts. For diagnostic predictions, we predicted whether patients would be diagnosed with a target disease during their next visit or in the subsequent diagnosis, leveraging their historical medical records. We compared our results to various baselines, including Retain and Med-BERT, the latter of which is the current state-of-the-art model for disease prediction using temporal structured EHR data. In addition, we also evaluated CPLLM’s utility in predicting hospital readmission and compared our method’s performance with benchmark baselines. Our experiments ultimately revealed that our proposed method, CPLLM, surpasses all the tested models in terms of PR-AUC and ROC-AUC metrics, providing state-of-the-art performance as a tool for predicting disease diagnosis and patient hospital readmission without requiring pre-training on medical data. Such a method can be easily implemented and integrated into the clinical workflow to help care providers plan next steps for their patients.
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Ikenna, Ukabuiro, and Stella Agomah. "Prediction Models for Forex Data Exchange System." Prediction Models for Forex Data Exchange System 8, no. 12 (2024): 4. https://doi.org/10.5281/zenodo.10453255.
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Foreign exchange prediction is of important interest to investors and individual traders in financial industries in other to maximize profits and reduces losses. However owing to some factors and the non- linearity of the FX markets especially in a developing economy like Nigeria, generating suitable, accurate and appropriate FX predictions becomes difficult for the traders of the market. This study utilized models that include various machine learning algorithm over a trend analysis and pattern of its prediction. The model results on the currency pair of United States(USD) over Nigeria Naira (NGN) using Root Mean Squared Error (RMSE), Mean Absolute Error(MAE), Mean Square Error (MSE), and R-square (R2) showed GRU performed better in predicting the trend and we therefore considered it best fit for the forecast. The result showed high prediction over ANN and LSTM, with RMSE, MAE, MSE, and R2 values of 0.112, 0.075, 0.013, and 0.969. Keywords:- Forex, ANN, LSTM, GRU MAE, MSE.
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Busari, Ibrahim, Debabrata Sahoo, R. Daren Harmel, and Brian E. Haggard. "A Review of Machine Learning Models for Harmful Algal Bloom Monitoring in Freshwater Systems." Journal of Natural Resources and Agricultural Ecosystems 1, no. 2 (2023): 63–76. http://dx.doi.org/10.13031/jnrae.15647.
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Highlights Machine Learning (ML) models are identified, reviewed, and analyzed for HAB predictions. Data preprocessing is vital for efficient ML model development. ML models for toxin production and monitoring are limited. Abstract. Harmful algal blooms (HABs) are detrimental to livestock, humans, pets, the environment, and the global economy, which calls for a robust approach to their management. While process-based models can inform practitioners about HAB enabling conditions, they have inherent limitations in accurately predicting harmful algal blooms. To address these limitations, Machine Learning (ML) models can potentially leverage large volumes of IoT data to aid in near real-time predictions. ML models have evolved as efficient tools for understanding patterns and relationships between water quality parameters and HAB expansion. This review describes ML models currently used for predicting and forecasting HABs in freshwater ecosystems and presents model structures and their application for predicting algal parameters and related toxins. The review revealed that regression trees, random forest, Artificial Neural Network (ANN), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) are the most frequently used models for HABs monitoring. This review shows ML models' prowess in identifying significant variables influencing algal growth, HAB drivers, and multistep HAB prediction. Hybrid models also improve the prediction of algal-related parameters through improved optimization techniques and variable selection algorithms. While ML models often focus on algal biomass prediction, few studies apply ML models for toxin monitoring and prediction. This limitation can be associated with a lack of high-frequency toxin datasets for model development, and exploring this domain is encouraged. This review serves as a guide for policymakers and researchers to implement ML models for HAB prediction and reveals the potential of ML models for decision support and early prediction for HAB management. Keywords: Cyanobacteria, Freshwater, Harmful algal blooms, Machine learning, Water quality.
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Afshartous, David, and Jan de Leeuw. "Prediction in Multilevel Models." Journal of Educational and Behavioral Statistics 30, no. 2 (2005): 109–39. http://dx.doi.org/10.3102/10769986030002109.
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Multilevel modeling is an increasingly popular technique for analyzing hierarchical data. This article addresses the problem of predicting a future observable y*j in thej th group of a hierarchical data set. Three prediction rules are considered and several analytical results on the relative performance of these prediction rules are demonstrated. In addition, the prediction rules are assessed by means of a Monte Carlo study that extensively covers both the sample size and parameter space. Specifically, the sample size space concerns the various combinations of Level 1 (individual) and Level 2 (group) sample sizes, while the parameter space concerns different intraclass correlation values. The three prediction rules employ OLS, prior, and multilevel estimators for the Level 1 coefficientsβj The multilevel prediction rule performs the best across all design conditions, and the prior prediction rule degrades as the number of groups, J, increases. Finally, this article investigates the robustness of the multilevel prediction rule to misspecifications of the Level 2 model.
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Kappen, Teus H., and Linda M. Peelen. "Prediction models." Current Opinion in Anaesthesiology 29, no. 6 (2016): 717–26. http://dx.doi.org/10.1097/aco.0000000000000386.
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Lekea, Angella, and Wynand J. vdM Steyn. "Performance of Pavement Temperature Prediction Models." Applied Sciences 13, no. 7 (2023): 4164. http://dx.doi.org/10.3390/app13074164.
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Appropriate asphalt binder selection is dependent on the correct determination of maximum and minimum pavement temperatures. Temperature prediction models have been developed to determine pavement design temperatures. Accordingly, accurate temperature prediction is necessary to ensure the correct design of climate-resilient pavements and for suitable pavement overlay design. Research has shown that the complexity of the model, input variables, geographical location among others affect the accuracy of temperature prediction models. Calibration has also proved to improve the accuracy of the predicted temperature. In this paper, the performance of three pavement temperature prediction models with a sample of materials, including asphalt, was examined. Furthermore, the effect of calibration on model accuracy was evaluated. Temperature data sourced from Pretoria were used to calibrate and test the models. The performance of both the calibrated and uncalibrated models in a different geographical location was also assessed. Asphalt temperature data from two locations in Ghana were used. The determination coefficient (R2), Variance Accounted For (VAF), Maximum Relative Error (MRE) and Root Mean Square Error (RMSE) statistical methods were used in the analysis. It was observed that the models performed better at predicting maximum temperature, while minimum temperature predictions were highly variable. The performance of the models varied for the maximum temperature prediction depending on the material. Calibration improved the accuracy of the models, but test data relevant to each location ought to be used for calibration to be effective. There is also a need for the models to be tested with data sourced from other continents.
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Geweke, John, and Gianni Amisano. "Prediction with Misspecified Models." American Economic Review 102, no. 3 (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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Sun, Zhaoyue, Jiazheng Li, Gabriele Pergola, and Yulan He. "ExDDI: Explaining Drug-Drug Interaction Predictions with Natural Language." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 24 (2025): 25228–36. https://doi.org/10.1609/aaai.v39i24.34709.
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Predicting unknown drug-drug interactions (DDIs) is crucial for improving medication safety. Previous efforts in DDI prediction have typically focused on binary classification or predicting DDI categories, with the absence of explanatory insights that could enhance trust in these predictions. In this work, we propose to generate natural language explanations for DDI predictions, enabling the model to reveal the underlying pharmacodynamics and pharmacokinetics mechanisms simultaneously as making the prediction. To do this, we have collected DDI explanations from DDInter and DrugBank and developed various models for extensive experiments and analysis. Our models can provide accurate explanations for unknown DDIs between known drugs. This paper contributes new tools to the field of DDI prediction and lays a solid foundation for further research on generating explanations for DDI predictions.
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Chauhan, Kushal, Rishabh Tiwari, Jan Freyberg, Pradeep Shenoy, and Krishnamurthy Dvijotham. "Interactive Concept Bottleneck Models." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 5 (2023): 5948–55. http://dx.doi.org/10.1609/aaai.v37i5.25736.
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Concept bottleneck models (CBMs) are interpretable neural networks that first predict labels for human-interpretable concepts relevant to the prediction task, and then predict the final label based on the concept label predictions. We extend CBMs to interactive prediction settings where the model can query a human collaborator for the label to some concepts. We develop an interaction policy that, at prediction time, chooses which concepts to request a label for so as to maximally improve the final prediction. We demonstrate that a simple policy combining concept prediction uncertainty and influence of the concept on the final prediction achieves strong performance and outperforms static approaches as well as active feature acquisition methods proposed in the literature. We show that the interactive CBM can achieve accuracy gains of 5-10% with only 5 interactions over competitive baselines on the Caltech-UCSD Birds, CheXpert and OAI datasets.
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Hu, Guang, and Yue Tang. "GERPM: A Geographically Weighted Stacking Ensemble Learning-Based Urban Residential Rents Prediction Model." Mathematics 11, no. 14 (2023): 3160. http://dx.doi.org/10.3390/math11143160.
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Accurate prediction of urban residential rents is of great importance for landlords, tenants, and investors. However, existing rents prediction models face challenges in meeting practical demands due to their limited perspectives and inadequate prediction performance. The existing individual prediction models often lack satisfactory accuracy, while ensemble learning models that combine multiple individual models to improve prediction results often overlook the impact of spatial heterogeneity on residential rents. To address these issues, this paper proposes a novel prediction model called GERPM, which stands for Geographically Weighted Stacking Ensemble Learning-Based Urban Residential Rents Prediction Model. GERPM comprehensively analyzes the influencing factors of residential rents from multiple perspectives and leverages a geographically weighted stacking ensemble learning approach. The model combines multiple machine learning and deep learning models, optimizes parameters to achieve optimal predictions, and incorporates the geographically weighted regression (GWR) model to consider spatial heterogeneity. By combining the strengths of deep learning and machine learning models and taking into account geographical factors, GERPM aims to improve prediction accuracy and provide robust predictions for urban residential rents. The model is evaluated using housing data from Nanjing, a major city in China, and compared with representative individual prediction models, the equal weight combination model, and the ensemble learning model. The experimental results demonstrate that GERPM outperforms other models in terms of prediction performance. Furthermore, the model’s effectiveness and robustness are validated by applying it to other major cities in China, such as Shanghai and Hangzhou. Overall, GERPM shows promising potential in accurately predicting urban residential rents and contributing to the advancement of the rental market.
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Masdiantini, Putu Riesty, and Ni Made Sindy Warasniasih. "Laporan Keuangan dan Prediksi Kebangkrutan Perusahaan." Jurnal Ilmiah Akuntansi 5, no. 1 (2020): 196. http://dx.doi.org/10.23887/jia.v5i1.25119.
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This study aims to determine differences in bankruptcy predictions at company’s sub-sector of cosmetics and household listed on the Indonesia Stock Exchange (IDX) using the Altman model, Springate model, Zmijewski model, Taffler model, and Fulmer model, and to determine the bankruptcy prediction model that is the most accurate of the five bankruptcy prediction models. This study uses secondary data in the form of company financial statements for the period 2014-2018. Data analysis techniques in this study used the Kruskal-Wallis test. The results showed there were differences in bankruptcy predictions using the Altman model, Springate model, Zmijewski model, Taffler model, and Fulmer model. The Zmijewski, Taffler, and Fulmer models have the same accuracy level of 100% so that the three prediction models are the most accurate prediction models for predicting the potential bankruptcy at companies sub-sector of cosmetics and household listed on the IDX.
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Raghuram, Shree R., and Bhawna Rathi Dr. "Prediction Models and Analysis for Diabetes Prediction." Journal Of Scientific Research And Technology (JSRT) 1, no. 2 (2023): 101–15. https://doi.org/10.5281/zenodo.7980387.
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Domain knowledge is an important part of data science. Data science requires domain expertise, which is the familiarity with and comprehension of the context in which data science is used. Acquiring domain-specific knowledge entails familiarity with important ideas, terms, and procedures. The ability to read and understand the data being worked with is essential for data scientists in order to create relevant and accurate models and analyses. Data science is being utilized across many different sectors, including finance, military, telecommunications, healthcare, and many more, all of which place a premium on domain expertise. Data scientists in the banking business, for instance, need to be familiar with financial goods, transactions, and laws. Similar knowledge of military operations, equipment, and strategy is necessary for data scientists working in the defense business. Data scientists may have trouble developing reliable models and analyses if they lack this background knowledge. However, it is not always simple for data scientists to learn the ins and outs of a new sector. It's a process that calls both dedication and curiosity. One strategy is to choose a topic that piques one's curiosity and then slowly work one's way through the fundamentals. This might be accomplished by activities such as reading relevant books, going to conferences, or enrolling in courses. A data scientist with an interest in sports analytics, for instance, would start with a foundational understanding of statistics and data analysis, before moving on to more sport-specific methods and tools. The creation of connected initiatives is still another option. Collaboration with subject-matter specialists or the creation of industry-specific models and analysis are two possible approaches. A data scientist's job in the healthcare sector could include, for instance, the creation of models to foretell patient outcomes or the detection of disease risk factors.
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Rejovitzky, Elisha, and Eli Altus. "On single damage variable models for fatigue." International Journal of Damage Mechanics 22, no. 2 (2012): 268–84. http://dx.doi.org/10.1177/1056789512443902.
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This study focuses on an analytical investigation of the common characteristics of fatigue models based on a single damage variable. The general single damage variable constitutive equation is used to extract several fundamental properties. It is shown that at constant amplitude loads, damage evolution results are sufficient for predicting fatigue life under any load history. Two-level fatigue envelopes constitute an indirect measure of the damage evolution and form an alternative basis for life prediction. In addition, high-to-low and low-to-high envelopes are anti-symmetrical with respect to each other. A new integral formula for life prediction under random loads is verified with the models of Manson and Hashin, and also developed analytically for other models including Chaboche, resulting in analytical predictions. The Palmgren – Miner rule is found to yield an upper bound for fatigue life predictions under random loads, regardless of the load distribution and the specific single damage variable model.
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Merrill, Zachary, Subashan Perera, and Rakié Cham. "Torso Segment Parameter Prediction in Working Adults." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (2018): 1257–61. http://dx.doi.org/10.1177/1541931218621289.
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Body segment parameters (BSPs) such as segment mass, center of mass, and radius of gyration are used as inputs in static and dynamic ergonomic and biomechanical models used to predict joint and muscle forces, and related risks of musculoskeletal injury. Because these models are sensitive to BSP values, accurate and representative parameters are necessary for injury risk prediction. While previous studies have determined segment parameters in the general population, as well as the impact of age and obesity levels on these parameters, estimated errors in the prediction of BSPs can be as large as 40% (Durkin, 2003). Thus, more precise values are required for attempting to predict injury risk in individuals. This study aims to provide statistical models for predicting torso segment parameters in working adults using whole body dual energy x-ray absorptiometry (DXA) scan data along with a set of anthropometric measurements. The statistical models were developed on a training subset of the study population, and validated on a testing subset. When comparing the model predictions to the actual BSPs of the testing subset, the predictions were, on average, within 5% of the calculated parameters, while previously developed predictions (de Leva, 1996) had average errors of up to 30%, indicating that the new statistical models greatly increase the accuracy in predicting BSPs.
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Lu, Zenan. "Comparison of stock price prediction models for linear models, random forest and LSTM." Applied and Computational Engineering 54, no. 1 (2024): 226–33. http://dx.doi.org/10.54254/2755-2721/54/20241598.
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With the rapid development of financial markets, accurate stock price prediction is significant to investors and financial institutions. Many researchers proposed stock price prediction models, including linear models, random forests, and LSTMs. However, few studies have comprehensively compared the three models. This study aims to fill this gap by analysing the forecasting effectiveness of different models through empirical studies. This research is to explore the application of linear models, random forests, and LSTM models in predicting stock prices and analyse and compare the principles, advantages and disadvantages, and the scope of application of these three models. According to the analysis, they all have their scope of application and limitations in different situations. In practical application, the appropriate model can be chosen for prediction and analysis according to the specific data sets and research purpose. Meanwhile, it is also possible to try to integrate and improve different models to get better prediction results. In addition, the influence of data quality and completeness, feature selection and extraction from the prediction results should be noted to improve the prediction accuracy and stability of the model. In conclusion, this thesis provides some references and lessons for related studies and practical applications by analysing and comparing the applications of LSTM, linear models, and random forests in predicting stock prices.
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Salehi, Mahdi, Mahmoud Lari Dashtbayaz, and Masomeh Heydari. "Audit fees prediction using fuzzy models." Problems and Perspectives in Management 14, no. 2 (2016): 104–17. http://dx.doi.org/10.21511/ppm.14(2).2016.11.
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The current study aims to predict the optimal amount of independent audit fees based on the factors influencing audit fees. To identify the factors influencing audit fees, the stakeholders of 30 auditing firms, members of the Iranian Association of Certified Public Accountants in Tehran selected randomly, were interviewed. Finally, the linear programming model for audit fees and its determinants is defined and sum of squared error is used to solve the function with minimum. Also, given that the data are quantitative and comparative and normally distributed, Pearson’s correlation coefficient is used to test the research hypotheses. The results show that a positive significant correlation exists between the variables of expected time to perform audit procedures, the number of accounting documents, audit operation risk, complexity of operations, existence of specific rules and regulations governing the activities of the entity
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Jeong, Jiseok, and Changwan Kim. "Comparison of Machine Learning Approaches for Medium-to-Long-Term Financial Distress Predictions in the Construction Industry." Buildings 12, no. 10 (2022): 1759. http://dx.doi.org/10.3390/buildings12101759.
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A method for predicting the financial status of construction companies after a medium-to-long-term period can help stakeholders in large construction projects make decisions to select an appropriate company for the project. This study compares the performances of various prediction models. It proposes an appropriate model for predicting the financial distress of construction companies considering three, five, and seven years ahead of the prediction point. To establish the prediction model, a financial ratio was selected, which was adopted in existing studies on medium-to-long-term predictions in other industries, as an additional input variable. To compare the performances of the prediction models, single-machine learning and ensemble models’ performances were compared. The comprehensive performance comparison of these models was based on the average value of the prediction performance and the results of the Friedman test. The comparison result determined that the random subspace (RS) model exhibited the best performance in predicting the financial status of construction companies after a medium-to-long-term period. The proposed model can be effectively employed to help large-scale project stakeholders avoid damage caused by the financial distress of construction companies during the project implementation process.
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Archer, Graeme, Michael Balls, Leon H. Bruner, et al. "The Validation of Toxicological Prediction Models." Alternatives to Laboratory Animals 25, no. 5 (1997): 505–16. http://dx.doi.org/10.1177/026119299702500507.
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An alternative method is shown to consist of two parts: the test system itself; and a prediction model for converting in vitro endpoints into predictions of in vivo toxicity. For the alternative method to be relevant and reliable, it is important that its prediction model component is of high predictive power and is sufficiently robust against sources of data variability. In other words, the prediction model must be subjected to criticism, leading successful models to the state of confirmation. It is shown that there are certain circumstances in which a new prediction model may be introduced without the necessity to generate new test system data.
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Hier Majumder, C. A., E. Bélanger, S. DeRosier, D. A. Yuen, and A. P. Vincent. "Data assimilation for plume models." Nonlinear Processes in Geophysics 12, no. 2 (2005): 257–67. http://dx.doi.org/10.5194/npg-12-257-2005.
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Abstract. We use a four-dimensional variational data assimilation (4D-VAR) algorithm to observe the growth of 2-D plumes from a point heat source. In order to test the predictability of the 4D-VAR technique for 2-D plumes, we perturb the initial conditions and compare the resulting predictions to the predictions given by a direct numerical simulation (DNS) without any 4D-VAR correction. We have studied plumes in fluids with Rayleigh numbers between 106 and 107 and Prandtl numbers between 0.7 and 70, and we find the quality of the prediction to have a definite dependence on both the Rayleigh and Prandtl numbers. As the Rayleigh number is increased, so is the quality of the prediction, due to an increase of the inertial effects in the adjoint equations for momentum and energy. The horizon predictability time, or how far into the future the 4D-VAR method can predict, decreases as Rayleigh number increases. The quality of the prediction is decreased as Prandtl number increases, however. Quality also decreases with increased prediction time.
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Je-Gal, Hong, Young-Seo Park, Seong-Ho Park, et al. "Time-Series Explanatory Fault Prediction Framework for Marine Main Engine Using Explainable Artificial Intelligence." Journal of Marine Science and Engineering 12, no. 8 (2024): 1296. http://dx.doi.org/10.3390/jmse12081296.
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As engine monitoring data has become more complex with an increasing number of sensors, fault prediction based on artificial intelligence (AI) has emerged. Existing fault prediction models using AI significantly improve the accuracy of predictions by effectively handling such complex data, but at the same time, the problem arises that the AI-based models cannot explain the rationale of their predictions to users. To address this issue, we propose a time-series explanatory fault prediction framework to provide an explainability even when using AI-based fault prediction models. It consists of a data feature reduction process, a fault prediction model training process using long short-term memory, and an interpretation process of the fault prediction model via an explainable AI method. In particular, the proposed framework can explain a fault prediction based on time-series data. Therefore, it indicates which part of the data was significant for the fault prediction not only in terms of sensor type but also in terms of time. Through extensive experiments, we evaluate the proposed framework using various fault data by comparing the prediction performance of fault prediction and by assessing how well the main pre-symptoms of the fault are extracted when predicting a fault.
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Karpac, Dusan, and Viera Bartosova. "The verification of prediction and classification ability of selected Slovak prediction models and their emplacement in forecasts of financial health of a company in aspect of globalization." SHS Web of Conferences 74 (2020): 06010. http://dx.doi.org/10.1051/shsconf/20207406010.
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Predicting financial health of a company is in this global world necessary for each business entity, especially for the international ones, as it´s very important to know financial stability. Forecasting business failure is a worldwide known term, in a global notion, and there is a lot of prediction models constructed to compute financial health of a company and, by that, state whether a company inclines to financial boom or bankruptcy. Globalized prediction models compute financial health of companies, but the vast majority of models predicting business failure are constructed solely for the conditions of a particular country or even just for a specific sector of a national economy. Field of financial predictions regarding to international view consists of elementary used models, for example, such as Altman´s Z-score or Beerman´s index, which are globally know and used as basic of many other modificated models. Following article deals with selected Slovak prediction models designed to Slovak conditions, states how these models stand in this global world, what is their international connection to the worldwide economies, and also states verification of their prediction ability in a specific sector. The verification of predictive ability of the models is defined by ROC analysis and through results the paper demonstrates the most suitable prediction models to use in the selected sector.
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Lema, Guillermo. "Risk Prediction Models." Mayo Clinic Proceedings 96, no. 4 (2021): 1095. http://dx.doi.org/10.1016/j.mayocp.2021.02.001.
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Hadayeghi, Alireza, Amer S. Shalaby, and Bhagwant N. Persaud. "Safety Prediction Models." Transportation Research Record: Journal of the Transportation Research Board 2019, no. 1 (2007): 225–36. http://dx.doi.org/10.3141/2019-27.
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DETSKY, ALLAN S. "Clinical prediction models." Acta Anaesthesiologica Scandinavica 39 (June 1995): 134–35. http://dx.doi.org/10.1111/j.1399-6576.1995.tb04295.x.
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Steyerberg, Ewout W., and Hester F. Lingsma. "Validating prediction models." BMJ 336, no. 7648 (2008): 789.2–789. http://dx.doi.org/10.1136/bmj.39542.610000.3a.
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Liao, Lawrence, and Daniel B. Mark. "Clinical prediction models." Journal of the American College of Cardiology 42, no. 5 (2003): 851–53. http://dx.doi.org/10.1016/s0735-1097(03)00836-2.
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Tripepi, G., G. Heinze, K. J. Jager, V. S. Stel, F. W. Dekker, and C. Zoccali. "Risk prediction models." Nephrology Dialysis Transplantation 28, no. 8 (2013): 1975–80. http://dx.doi.org/10.1093/ndt/gft095.
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Ranstam, J., J. A. Cook, and G. S. Collins. "Clinical prediction models." British Journal of Surgery 103, no. 13 (2016): 1886. http://dx.doi.org/10.1002/bjs.10242.
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Mijderwijk, Hendrik-Jan, Thomas Beez, Daniel Hänggi, and Daan Nieboer. "Clinical prediction models." Child's Nervous System 36, no. 5 (2020): 895–97. http://dx.doi.org/10.1007/s00381-020-04577-8.
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Kim, Hyeongjun, Hoon Cho, and Doojin Ryu. "Corporate Default Predictions Using Machine Learning: Literature Review." Sustainability 12, no. 16 (2020): 6325. http://dx.doi.org/10.3390/su12166325.
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Corporate default predictions play an essential role in each sector of the economy, as highlighted by the global financial crisis and the increase in credit risk. This study reviews the corporate default prediction literature from the perspectives of financial engineering and machine learning. We define three generations of statistical models: discriminant analyses, binary response models, and hazard models. In addition, we introduce three representative machine learning methodologies: support vector machines, decision trees, and artificial neural network algorithms. For both the statistical models and machine learning methodologies, we identify the key studies used in corporate default prediction. By comparing these methods with findings from the interdisciplinary literature, our review suggests some new tasks in the field of machine learning for predicting corporate defaults. First, a corporate default prediction model should be a multi-period model in which future outcomes are affected by past decisions. Second, the stock price and the corporate value determined by the stock market are important factors to use in default predictions. Finally, a corporate default prediction model should be able to suggest the cause of default.
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Mujahidin, Irfan, and Fikri Arif Rakhman. "Application of Optimization Algorithm to Machine Learning Model for Solar Panel Output Power Prediction: A Review." Jurnal Informatika: Jurnal Pengembangan IT 9, no. 2 (2024): 180–87. http://dx.doi.org/10.30591/jpit.v9i2.7051.
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Solar panels have become a popular source of renewable energy due to their sustainability and environmental friendliness. Accurate predictions of solar panel output are crucial for various applications, such as energy system optimization, power grid management, and economic planning. Many important factors pose challenges in predicting the output of solar panels, such as weather conditions that can change at any time, geographical factors, data quality, and the duration of data collection. Machine learning (ML) models show promising performance in this prediction; there are many types of machine learning models, some are single models and others are hybrid models. Optimization algorithms are used to optimize parameters and improve the prediction accuracy of machine learning models. This research reviews fifteen journals that have been filtered to obtain those discussing optimization algorithms in the predictive models of solar panel output power. This journal will examine the optimization algorithms used in machine learning models for predicting solar panel output power, discussing various types of optimization algorithms, their application in machine learning models, the prediction results from these models, the input data used, and the data collection locations that significantly influence the prediction outcomes. From the results of this research, it does not conclude which machine learning model is the best, due to the many factors that influence it. However, this research is expected to provide references on the application of machine learning models in predicting the output power of solar panels, thereby encouraging the use of renewable energy sources.
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Lin, Fucheng, Longfei Xie, Yuanshuo Hao, Zheng Miao, and Lihu Dong. "Comparison of Modeling Approaches for the Height–Diameter Relationship: An Example with Planted Mongolian Pine (Pinus sylvestris var. mongolica) Trees in Northeast China." Forests 13, no. 8 (2022): 1168. http://dx.doi.org/10.3390/f13081168.
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In the process of modeling height–diameter models for Mongolian pine (Pinus sylvestris var. mongolica), the fitting abilities of six models were compared: (1) a basic model with only diameter at breast height (D) as a predictor (BM); (2) a plot-level basic mixed-effects model (BMM); (3) quantile regression with nine quantiles based on BM (BQR); (4) a generalized model with stand or competition covariates (GM); (5) a plot-level generalized mixed-effects model (GMM); and (6) quantile regression with nine quantiles based on GM (GQR). The prediction bias of the developed models was assessed in cases of total tree height (H) predictions with calibration or without calibration. The results showed that extending the Chapman–Richards function with the dominant height and relative size of individual trees improved the prediction accuracy. Prediction accuracy was improved significantly when H predictions were calibrated for all models, among which GMM performed best because random effect calibration provided the lowest prediction bias. When at least 8% of the trees were selected from a new plot, relatively accurate and low-cost prediction results were obtained by all models. When predicting the H values of Mongolian pine for a new stand, GMM and BMM were preferable if there were available height measurements for calibration; otherwise, GQR was the best choice.
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Vidyadevi, G.Biradar. "A Study on Prediction Models." Journal of Advancement in Parallel Computing 5, no. 1 (2022): 1–14. https://doi.org/10.5281/zenodo.6471760.
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<em>Defect-prone code development may be improved by focusing on recent research on software defect prediction. This improves software quality and saves resources. Many approaches, datasets, and frameworks for predicting software defects have been published, but it is difficult to get an overall sense of where defect prediction research is now at. In this research article, we have tried to understand the relationships between various variables which are important for IT SME’s. The study is carried out with the help of a well-structured questionnaire using IBM SPSS tool for data analysis and interpretation.</em>
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Möst, Lisa, Matthias Schmid, Florian Faschingbauer, and Torsten Hothorn. "Predicting birth weight with conditionally linear transformation models." Statistical Methods in Medical Research 25, no. 6 (2016): 2781–810. http://dx.doi.org/10.1177/0962280214532745.
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Low and high birth weight (BW) are important risk factors for neonatal morbidity and mortality. Gynecologists must therefore accurately predict BW before delivery. Most prediction formulas for BW are based on prenatal ultrasound measurements carried out within one week prior to birth. Although successfully used in clinical practice, these formulas focus on point predictions of BW but do not systematically quantify uncertainty of the predictions, i.e. they result in estimates of the conditional mean of BW but do not deliver prediction intervals. To overcome this problem, we introduce conditionally linear transformation models (CLTMs) to predict BW. Instead of focusing only on the conditional mean, CLTMs model the whole conditional distribution function of BW given prenatal ultrasound parameters. Consequently, the CLTM approach delivers both point predictions of BW and fetus-specific prediction intervals. Prediction intervals constitute an easy-to-interpret measure of prediction accuracy and allow identification of fetuses subject to high prediction uncertainty. Using a data set of 8712 deliveries at the Perinatal Centre at the University Clinic Erlangen (Germany), we analyzed variants of CLTMs and compared them to standard linear regression estimation techniques used in the past and to quantile regression approaches. The best-performing CLTM variant was competitive with quantile regression and linear regression approaches in terms of conditional coverage and average length of the prediction intervals. We propose that CLTMs be used because they are able to account for possible heteroscedasticity, kurtosis, and skewness of the distribution of BWs.
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Carballo, Alba, María Durbán, and Dae-Jin Lee. "Out-of-Sample Prediction in Multidimensional P-Spline Models." Mathematics 9, no. 15 (2021): 1761. http://dx.doi.org/10.3390/math9151761.
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The prediction of out-of-sample values is an interesting problem in any regression model. In the context of penalized smoothing using a mixed-model reparameterization, a general framework has been proposed for predicting in additive models but without interaction terms. The aim of this paper is to generalize this work, extending the methodology proposed in the multidimensional case, to models that include interaction terms, i.e., when prediction is carried out in a multidimensional setting. Our method fits the data, predicts new observations at the same time, and uses constraints to ensure a consistent fit or impose further restrictions on predictions. We have also developed this method for the so-called smooth-ANOVA model, which allows us to include interaction terms that can be decomposed into the sum of several smooth functions. We also develop this methodology for the so-called smooth-ANOVA models, which allow us to include interaction terms that can be decomposed as a sum of several smooth functions. To illustrate the method, two real data sets were used, one for predicting the mortality of the U.S. population in a logarithmic scale, and the other for predicting the aboveground biomass of Populus trees as a smooth function of height and diameter. We examine the performance of interaction and the smooth-ANOVA model through simulation studies.
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Xiao, Yang. "Individual Stock Price Prediction Using Stacking Method." Advances in Economics, Management and Political Sciences 99, no. 1 (2024): 17–22. http://dx.doi.org/10.54254/2754-1169/99/2024ox0212.
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This paper explores the application of stacking in machine learning to predict the price of a single stock. The complexity of financial markets and the high noise in data make stock price prediction a challenging task. To improve prediction accuracy, this paper combines multiple machine learning models, including linear regression, decision trees, and random forests, using stacking to integrate the predictions of these base models. Experimental results indicate that the stacking model performs exceptionally well in predicting the stock price of Apple Inc. (AAPL), significantly outperforming individual models. This paper evaluates the model using mean squared error (MSE) and root mean squared error (RMSE) and demonstrated the models prediction accuracy and robustness. The findings demonstrate that the stacking model not only reduces prediction errors but also enhances robustness against the volatile nature of stock prices. This study underscores the high potential of stacking in financial time series prediction, providing valuable insights and references for investment decisions. By integrating multiple predictive models, stacking offers a powerful tool for navigating the complexities of financial markets and making informed investment choices.
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Fang, Lili. "Prediction of Rainfall-Induced Landslide-Mudslide Hazard Chain Using Coupled TRIGRS and RAMMS Models." International Journal of Information System Modeling and Design 16, no. 1 (2025): 1–23. https://doi.org/10.4018/ijismd.370964.
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This study develops an effective prediction method for rainfall-induced landslide-mudslide disaster chains by integrating the TRIGRS landslide prediction model with the RAMMS mudslide simulation model. Using a mountainous area prone to such disasters as a case study, it first applies the TRIGRS model to assess slope stability under rainfall conditions to identify potential landslide hazards. The identified landslide predictions serve as input for the RAMMS model to simulate the subsequent mudslide processes, predicting their paths, velocities, impact forces, and accumulation areas. Through iterative optimization of this coupled model, the system achieves comprehensive prediction of the disaster chain. Case analysis results demonstrate high accuracy in predicting both the location and scale of landslides and the characteristics of triggered mudslides, validating the model's effectiveness and providing a robust scientific basis for early warning and disaster mitigation planning.
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Dhakal, Rabin, Ashish Sedai, Suhas Pol, Siva Parameswaran, Ali Nejat, and Hanna Moussa. "A Novel Hybrid Method for Short-Term Wind Speed Prediction Based on Wind Probability Distribution Function and Machine Learning Models." Applied Sciences 12, no. 18 (2022): 9038. http://dx.doi.org/10.3390/app12189038.
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The need to deliver accurate predictions of renewable energy generation has long been recognized by stakeholders in the field and has propelled recent improvements in more precise wind speed prediction (WSP) methods. Models such as Weibull-probability-density-based WSP (WEB), Rayleigh-probability-density-based WSP (RYM), autoregressive integrated moving average (ARIMA), Kalman filter and support vector machines (SVR), artificial neural network (ANN), and hybrid models have been used for accurate prediction of wind speed with various forecast horizons. This study intends to incorporate all these methods to achieve a higher WSP accuracy as, thus far, hybrid wind speed predictions are mainly made by using multivariate time series data. To do so, an error correction algorithm for the probability-density-based wind speed prediction model is introduced. Moreover, a comparative analysis of the performance of each method for accurately predicting wind speed for each time step of short-term forecast horizons is performed. All the models studied are used to form the prediction model by optimizing the weight function for each time step of a forecast horizon for each model that contributed to forming the proposed hybrid prediction model. The National Oceanic and Atmospheric Administration (NOAA) and System Advisory Module (SAM) databases were used to demonstrate the accuracy of the proposed models and conduct a comparative analysis. The results of the study show the significant improvement on the performance of wind speed prediction models through the development of a proposed hybrid prediction model.
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Wu, Peng, and Yongze Song. "Land Use Quantile Regression Modeling of Fine Particulate Matter in Australia." Remote Sensing 14, no. 6 (2022): 1370. http://dx.doi.org/10.3390/rs14061370.
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Small data samples are still a critical challenge for spatial predictions. Land use regression (LUR) is a widely used model for spatial predictions with observations at a limited number of locations. Studies have demonstrated that LUR models can overcome the limitation exhibited by other spatial prediction models which usually require greater spatial densities of observations. However, the prediction accuracy and robustness of LUR models still need to be improved due to the linear regression within the LUR model. To improve LUR models, this study develops a land use quantile regression (LUQR) model for more accurate spatial predictions for small data samples. The LUQR is an integration of the LUR and quantile regression, which both have advantages in predictions with a small data set of samples. In this study, the LUQR model is applied in predicting spatial distributions of annual mean PM2.5concentrations across the Greater Sydney Region, New South Wales, Australia, with observations at 19 valid monitoring stations in 2020. Cross validation shows that the goodness-of-fit can be improved by 25.6–32.1% by LUQR models when compared with LUR, and prediction root mean squared error (RMSE) and mean absolute error (MAE) can be reduced by 10.6–13.4% and 19.4–24.7% by LUQR models, respectively. This study also indicates that LUQR is a more robust model for the spatial prediction with small data samples than LUR. Thus, LUQR has great potentials to be widely applied in spatial issues with a limited number of observations.
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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 (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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Sandhu, Karansher Singh, Meriem Aoun, Craig F. Morris, and Arron H. Carter. "Genomic Selection for End-Use Quality and Processing Traits in Soft White Winter Wheat Breeding Program with Machine and Deep Learning Models." Biology 10, no. 7 (2021): 689. http://dx.doi.org/10.3390/biology10070689.
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Breeding for grain yield, biotic and abiotic stress resistance, and end-use quality are important goals of wheat breeding programs. Screening for end-use quality traits is usually secondary to grain yield due to high labor needs, cost of testing, and large seed requirements for phenotyping. Genomic selection provides an alternative to predict performance using genome-wide markers under forward and across location predictions, where a previous year’s dataset can be used to build the models. Due to large datasets in breeding programs, we explored the potential of the machine and deep learning models to predict fourteen end-use quality traits in a winter wheat breeding program. The population used consisted of 666 wheat genotypes screened for five years (2015–19) at two locations (Pullman and Lind, WA, USA). Nine different models, including two machine learning (random forest and support vector machine) and two deep learning models (convolutional neural network and multilayer perceptron) were explored for cross-validation, forward, and across locations predictions. The prediction accuracies for different traits varied from 0.45–0.81, 0.29–0.55, and 0.27–0.50 under cross-validation, forward, and across location predictions. In general, forward prediction accuracies kept increasing over time due to increments in training data size and was more evident for machine and deep learning models. Deep learning models were superior over the traditional ridge regression best linear unbiased prediction (RRBLUP) and Bayesian models under all prediction scenarios. The high accuracy observed for end-use quality traits in this study support predicting them in early generations, leading to the advancement of superior genotypes to more extensive grain yield trails. Furthermore, the superior performance of machine and deep learning models strengthens the idea to include them in large scale breeding programs for predicting complex traits.
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Khaokaew, Yonchanok, Hao Xue, and Flora D. Salim. "MAPLE." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 8, no. 1 (2024): 1–25. http://dx.doi.org/10.1145/3643514.
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In recent years, predicting mobile app usage has become increasingly important for areas like app recommendation, user behaviour analysis, and mobile resource management. Existing models, however, struggle with the heterogeneous nature of contextual data and the user cold start problem. This study introduces a novel prediction model, Mobile App Prediction Leveraging Large Language Model Embeddings (MAPLE), which employs Large Language Models (LLMs) and installed app similarity to overcome these challenges. MAPLE utilises the power of LLMs to process contextual data and discern intricate relationships within it effectively. Additionally, we explore the use of installed app similarity to address the cold start problem, facilitating the modelling of user preferences and habits, even for new users with limited historical data. In essence, our research presents MAPLE as a novel, potent, and practical approach to app usage prediction, making significant strides in resolving issues faced by existing models. MAPLE stands out as a comprehensive and effective solution, setting a new benchmark for more precise and personalised app usage predictions. In tests on two real-world datasets, MAPLE surpasses contemporary models in both standard and cold start scenarios. These outcomes validate MAPLE's capacity for precise app usage predictions and its resilience against the cold start problem. This enhanced performance stems from the model's proficiency in capturing complex temporal patterns and leveraging contextual information. As a result, MAPLE can potentially improve personalised mobile app usage predictions and user experiences markedly.
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Sudriyanto, Sudriyanto, Fatimatus Syahro, and Novi Fitriani. "PERBANDINGAN PERFORMA MODEL MACHINE LEARNING SUPPORT VECTOR MACHINE, NEURAL NETWORK, DAN K-NEAREST NEIGHBORS DALAM PREDIKSI HARGA SAHAM." Jurnal Advanced Research Informatika 2, no. 1 (2023): 13–21. http://dx.doi.org/10.24929/jars.v2i1.2983.
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This study aims to analyze the performance of three prediction models, namely K-Nearest Neighbors (K-NN), Neural Network (NN), and Support Vector Machine (SVM), in predicting the stock price of PT Astra International Tbk (ASII.JK). The research encompasses the initial stages through evaluation using optimal parameters for these three algorithms. The research findings reveal that the K-NN prediction model has the lowest Root Mean Square Error (RMSE) value, with a value of 0.037, indicating the most accurate prediction compared to the other models. Despite the NN model having an RMSE of 0.048, which is higher than K-NN, it still provides reasonably accurate predictions. Meanwhile, the SVM model has an RMSE of 0.075, indicating a higher level of error in its predictions. Based on these results, the recommendation is to utilize the K-NN model as the preferred choice for predicting the ASII.JK stock price.
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Tao, Jiahui, Yicheng Gu, Xin Yin, Junlai Chen, Tianqi Ao, and Jianyun Zhang. "Coupling SWAT and Transformer Models for Enhanced Monthly Streamflow Prediction." Sustainability 16, no. 19 (2024): 8699. http://dx.doi.org/10.3390/su16198699.
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The establishment of an accurate and reliable predictive model is essential for water resources planning and management. Standalone models, such as physics-based hydrological models or data-driven hydrological models, have their specific applications, strengths, and limitations. In this study, a hybrid model (namely SWAT-Transformer) was developed by coupling the physics-based Soil and Water Assessment Tool (SWAT) with the data-driven Transformer to enhance monthly streamflow prediction accuracy. SWAT is first constructed and calibrated, and then its outputs are used as part of the inputs to Transformer. By correcting the prediction errors of SWAT using Transformer, the two models are effectively coupled. Monthly runoff data at Yan’an and Ganguyi stations on Yan River, a first-order tributary of the Yellow River Basin, were used to evaluate the proposed model’s performance. The results indicated that SWAT performed well in predicting high flows but poorly in low flows. In contrast, Transformer was able to capture low-flow period information more accurately and outperformed SWAT overall. SWAT-Transformer could correct the errors of SWAT predictions and overcome the limitations of a single model. By integrating SWAT’s detailed physical process portrayal with Transformer’s powerful time-series analysis, the coupled model significantly improved streamflow prediction accuracy. The proposed models offer more accurate and reliable predictions for optimal water resource management, which is crucial for sustainable economic and societal development.
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Siagian, Ruben Cornelius, Lulut Alfaris, Ghulab Nabi Ahmad, et al. "Relationship between Solar Flux and Sunspot Activity Using Several Regression Models." JURNAL ILMU FISIKA | UNIVERSITAS ANDALAS 15, no. 2 (2023): 146–65. http://dx.doi.org/10.25077/jif.15.2.146-165.2023.
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This study examines the correlation and prediction between sunspots and solar flux, two closely related factors associated with solar activity, covering the period from 2005 to 2022. The study utilizes a combination of linear regression analysis and the ARIMA prediction method to analyze the relationship between these factors and forecast their values. The analysis results reveal a significant positive correlation between sunspots and solar flux. Additionally, the ARIMA prediction method suggests that the SARIMA model can effectively forecast the values of both sunspots and solar flux for a 12-period timeframe. However, it is essential to note that this study solely focuses on correlation analysis and does not establish a causal relationship. Nonetheless, the findings contribute valuable insights into future variations in solar flux and sunspot numbers, thereby aiding scientists in comprehending and predicting solar activity's potential impact on Earth. The study recommends further research to explore additional factors that may influence the relationship between sunspots and solar flux, extend the research period to enhance the accuracy of solar activity predictions and investigate alternative prediction methods to improve the precision of forecasts.
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Gauthier, Gilles, Guillaume Péron, Jean-Dominique Lebreton, Patrick Grenier, and Louise van Oudenhove. "Partitioning prediction uncertainty in climate-dependent population models." Proceedings of the Royal Society B: Biological Sciences 283, no. 1845 (2016): 20162353. http://dx.doi.org/10.1098/rspb.2016.2353.
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The science of complex systems is increasingly asked to forecast the consequences of climate change. As a result, scientists are now engaged in making predictions about an uncertain future, which entails the efficient communication of this uncertainty. Here we show the benefits of hierarchically decomposing the uncertainty in predicted changes in animal population size into its components due to structural uncertainty in climate scenarios (greenhouse gas emissions and global circulation models), structural uncertainty in the demographic model, climatic stochasticity, environmental stochasticity unexplained by climate–demographic trait relationships, and sampling variance in demographic parameter estimates. We quantify components of uncertainty surrounding the future abundance of a migratory bird, the greater snow goose ( Chen caeruslescens atlantica ), using a process-based demographic model covering their full annual cycle. Our model predicts a slow population increase but with a large prediction uncertainty. As expected from theoretical variance decomposition rules, the contribution of sampling variance to prediction uncertainty rapidly overcomes that of process variance and dominates. Among the sources of process variance, uncertainty in the climate scenarios contributed less than 3% of the total prediction variance over a 40-year period, much less than environmental stochasticity. Our study exemplifies opportunities to improve the forecasting of complex systems using long-term studies and the challenges inherent to predicting the future of stochastic systems.
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Lei, Xiangdong, Changhui Peng, Haiyan Wang, and Xiaolu Zhou. "Individual height–diameter models for young black spruce (Picea mariana) and jack pine (Pinus banksiana) plantations in New Brunswick, Canada." Forestry Chronicle 85, no. 1 (2009): 43–56. http://dx.doi.org/10.5558/tfc85043-1.
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Historically, height–diameter models have mainly been developed for mature trees; consequently, few height–diameter models have been calibrated for young forest stands. In order to develop equations predicting the height of trees with small diameters, 46 individual height–diameter models were fitted and tested in young black spruce (Picea mariana) and jack pine (Pinus banksiana) plantations between the ages of 4 to 8 years, measured from 182 plots in New Brunswick, Canada. The models were divided into 2 groups: a diameter group and a second group applying both diameter and additional stand- or tree-level variables (composite models). There was little difference in predicting tree height among the former models (Group I) while the latter models (Group II) generally provided better prediction. Based on goodness of fit (R2and MSE), prediction ability (the bias and its associated prediction and tolerance intervals in absolute and relative terms), and ease of application, 2 Group II models were recommended for predicting individual tree heights within young black spruce and jack pine forest stands. Mean stand height was required for application of these models. The resultant tolerance intervals indicated that most errors (95%) associated with height predictions would be within the following limits (a 95% confidence level): [-0.54 m, 0.54 m] or [-14.7%, 15.9%] for black spruce and [-0.77 m, 0.77 m] or [-17.1%, 18.6%] for jack pine. The recommended models are statistically reliable for growth and yield applications, regeneration assessment and management planning. Key words: composite model, linear model, model calibration, model validation, prediction interval, tolerance interval