Relevant bibliographies by topics / Predicting model

Academic literature on the topic 'Predicting model'

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Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "Predicting model"

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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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Carlsson, Leo S., Mikael Vejdemo-Johansson, Gunnar Carlsson, and Pär G. Jönsson. "Fibers of Failure: Classifying Errors in Predictive Processes." Algorithms 13, no. 6 (June 23, 2020): 150. http://dx.doi.org/10.3390/a13060150.

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Predictive models are used in many different fields of science and engineering and are always prone to make faulty predictions. These faulty predictions can be more or less malignant depending on the model application. We describe fibers of failure (FiFa), a method to classify failure modes of predictive processes. Our method uses Mapper, an algorithm from topological data analysis (TDA), to build a graphical model of input data stratified by prediction errors. We demonstrate two ways to use the failure mode groupings: either to produce a correction layer that adjusts predictions by similarity to the failure modes; or to inspect members of the failure modes to illustrate and investigate what characterizes each failure mode. We demonstrate FiFa on two scenarios: a convolutional neural network (CNN) predicting MNIST images with added noise, and an artificial neural network (ANN) predicting the electrical energy consumption of an electric arc furnace (EAF). The correction layer on the CNN model improved its prediction accuracy significantly while the inspection of failure modes for the EAF model provided guiding insights into the domain-specific reasons behind several high-error regions.
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Prédhumeau, Manon, Lyuba Mancheva, Julie Dugdale, and Anne Spalanzani. "Agent-Based Modeling for Predicting Pedestrian Trajectories Around an Autonomous Vehicle." Journal of Artificial Intelligence Research 73 (April 19, 2022): 1385–433. http://dx.doi.org/10.1613/jair.1.13425.

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This paper addresses modeling and simulating pedestrian trajectories when interacting with an autonomous vehicle in a shared space. Most pedestrian–vehicle interaction models are not suitable for predicting individual trajectories. Data-driven models yield accurate predictions but lack generalizability to new scenarios, usually do not run in real time and produce results that are poorly explainable. Current expert models do not deal with the diversity of possible pedestrian interactions with the vehicle in a shared space and lack microscopic validation. We propose an expert pedestrian model that combines the social force model and a new decision model for anticipating pedestrian–vehicle interactions. The proposed model integrates different observed pedestrian behaviors, as well as the behaviors of the social groups of pedestrians, in diverse interaction scenarios with a car. We calibrate the model by fitting the parameters values on a training set. We validate the model and evaluate its predictive potential through qualitative and quantitative comparisons with ground truth trajectories. The proposed model reproduces observed behaviors that have not been replicated by the social force model and outperforms the social force model at predicting pedestrian behavior around the vehicle on the used dataset. The model generates explainable and real-time trajectory predictions. Additional evaluation on a new dataset shows that the model generalizes well to new scenarios and can be applied to an autonomous vehicle embedded prediction.
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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 (August 27, 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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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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Wang, Chun Sheng. "Information-Entropy-Based Integrated Model for Predicting Burn-Through Point in Lead-Zinc Sintering Process." Advanced Materials Research 396-398 (November 2011): 40–43. http://dx.doi.org/10.4028/www.scientific.net/amr.396-398.40.

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This paper presents an information-entropy-based integrated model for predicting the burn-through point (BTP) in lead-zinc sintering process. First, a fuzzy T-S prediction model for BTP was established to deal with the uncertainty of the vertical burning speed. Considering the BTP is also affected by process parameters, a neural network (NN) prediction model for BTP was then built. Finally, an integrated model for predicting the BTP was constructed by combining the above two models using the recursive entropy algorithm. The practical running results demonstrate the validity of the proposed integrated predictive model.
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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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Motesharei, Arman, Cecile Batailler, Daniele De Massari, Graham Vincent, Antonia F. Chen, and Sébastien Lustig. "Predicting robotic-assisted total knee arthroplasty operating time." Bone & Joint Open 3, no. 5 (May 1, 2022): 383–89. http://dx.doi.org/10.1302/2633-1462.35.bjo-2022-0014.r1.

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Aims No predictive model has been published to forecast operating time for total knee arthroplasty (TKA). The aims of this study were to design and validate a predictive model to estimate operating time for robotic-assisted TKA based on demographic data, and evaluate the added predictive power of CT scan-based predictors and their impact on the accuracy of the predictive model. Methods A retrospective study was conducted on 1,061 TKAs performed from January 2016 to December 2019 with an image-based robotic-assisted system. Demographic data included age, sex, height, and weight. The femoral and tibial mechanical axis and the osteophyte volume were calculated from CT scans. These inputs were used to develop a predictive model aimed to predict operating time based on demographic data only, and demographic and 3D patient anatomy data. Results The key factors for predicting operating time were the surgeon and patient weight, followed by 12 anatomical parameters derived from CT scans. The predictive model based only on demographic data showed that 90% of predictions were within 15 minutes of actual operating time, with 73% within ten minutes. The predictive model including demographic data and CT scans showed that 94% of predictions were within 15 minutes of actual operating time and 88% within ten minutes. Conclusion The primary factors for predicting robotic-assisted TKA operating time were surgeon, patient weight, and osteophyte volume. This study demonstrates that incorporating 3D patient-specific data can improve operating time predictions models, which may lead to improved operating room planning and efficiency. Cite this article: Bone Jt Open 2022;3(5):383–389.
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Tang, Li, Ping He Pan, and Yong Yi Yao. "EPAK: A Computational Intelligence Model for 2-level Prediction of Stock Indices." International Journal of Computers Communications & Control 13, no. 2 (April 13, 2018): 268–79. http://dx.doi.org/10.15837/ijccc.2018.2.3187.

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This paper proposes a new computational intelligence model for predicting univariate time series, called EPAK, and a complex prediction model for stock market index synthesizing all the sector index predictions using EPAK as a kernel. The EPAK model uses a complex nonlinear feature extraction procedure integrating a forward rolling Empirical Mode Decomposition (EMD) for financial time series signal analysis and Principal Component Analysis (PCA) for dimension reduction to generate information-rich features as input to a new two-layer K-Nearest Neighbor (KNN) with Affinity Propagation (AP) clustering for prediction via regression. The EPAK model is then used as a kernel for predicting each of all the sector indices of the stock market. The sector indices predictions are then synthesized via weighted average to generate the prediction of the stock market index, yielding a complex prediction model for the stock market index. The EPAK model and the complex prediction model for stock index are tested on real historical financial time series in Chinese stock index including CSI 300 and ten sector indices, with results confirming the effectiveness of the proposed models.
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Rather, Akhter Mohiuddin. "A Hybrid Intelligent Method of Predicting Stock Returns." Advances in Artificial Neural Systems 2014 (September 7, 2014): 1–7. http://dx.doi.org/10.1155/2014/246487.

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This paper proposes a novel method for predicting stock returns by means of a hybrid intelligent model. Initially predictions are obtained by a linear model, and thereby prediction errors are collected and fed into a recurrent neural network which is actually an autoregressive moving reference neural network. Recurrent neural network results in minimized prediction errors because of nonlinear processing and also because of its configuration. These prediction errors are used to obtain final predictions by summation method as well as by multiplication method. The proposed model is thus hybrid of both a linear and a nonlinear model. The model has been tested on stock data obtained from National Stock Exchange of India. The results indicate that the proposed model can be a promising approach in predicting future stock movements.
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Dissertations / Theses on the topic "Predicting model"

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Andeta, Jemal Ahmed. "Road-traffic accident prediction model : Predicting the Number of Casualties." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-20146.

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Efficient and effective road traffic prediction and management techniques are crucial in intelligent transportation systems. It can positively influence road advancement, safety enhancement, regulation formulation, and route planning to save living things in advance from road traffic accidents. This thesis considers road safety by predicting the number of casualties if an accident occurs using multiple traffic accident attributes. It helps individuals (drivers) or traffic offices to adjust and control their contributions for the occurrence of an accident before emerging it. Three candidate algorithms from different regression fit patterns are proposed and evaluated to conduct the thesis: the bagging, linear, and non-linear fitting patterns. The gradient boosting machines (GBoost) from the bagging, Linearsupport vector regression (LinearSVR) from the linear, and extreme learning machines (ELM) also from the non-linear side are the selected algorithms. RMSE and MAE performance evaluation metrics are applied to evaluate the models. The GBoost achieved a better performance than the other two with a low error rate and minimum prediction interval value for 95% prediction interval. A SHAP (SHapley Additive exPlanations) interpretation technique is applied to interpret each model at the global interpretation level using SHAP’s beeswarm plots. Finally, suggestions for future improvements are presented via the dataset and hyperparameter tuning.
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li, yiwen. "Predicting Hearing Loss Using Auditory Steady-State Responses." Digital WPI, 2009. https://digitalcommons.wpi.edu/etd-theses/84.

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Auditory Steady-State Response (ASSR) is a promising tool for detecting hearing loss. In this project, we analyzed hearing threshold data obtained from two ASSR methods and a gold standard, pure tone audiometry, applied to both normal and hearing-impaired subjects. We constructed a repeated measures linear model to identify factors that show significant differences in the mean response. The analysis shows that there are significant differences due to hearing status (normal or impaired) and ASSR method, and that there is a significant interaction between hearing status and test signal frequency. The second task of this project was to predict the PTA threshold (gold standard) from the ASSR-A and ASSR-B thresholds separately at each frequency, in order to measure how accurate the ASSR measurements are and to obtain a ¡°correction function¡± to correct the bias in the ASSR measurements. We used two approaches. In the first, we modeled the relation of the PTA responses to the ASSR values for the two hearing status groups as a mixture model and tried two prediction methods. The mixture modeling was successful, but the predictions gave disappointing results. A second approach, using logistic regression to predict group membership based on ASSR value and then using those predictions to obtain a predictor of the PTA value, gave successful results.
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Kingwell, Stephen. "Predicting Complications After Spinal Surgery: Surgeons’ Aided and Unaided Predictions." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41559.

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Despite the emergence of artificial intelligence (AI) and machine learning (ML) in medicine and the resultant interest in predictive analytics in surgery, there remains a paucity of research on the actual impact of prediction models and their effect on surgeons’ risk assessment of post-surgical complications. This research evaluated how spinal surgeons predict post-surgical complications with and without additional information generated by a ML predictive model. The study was conducted in two stages. In the preliminary stage an ML prediction model for post-surgical complications in spine surgery was developed. In the second stage, a survey instrument was developed, using patient vignettes, to determine how providing ML model support affected surgeons’ predictions of post-surgical complications. Results show that support provided by a ML prediction model improved surgeons’ accuracy to correctly predict the presence or absence of a complication in patients undergoing spinal surgery from 49.1% to 54.8% (p=0.024). It is clear that predicting post-surgical complications in patients undergoing spinal surgery is difficult, for models and experienced surgeons, but it is not surprising that additional information provided by the ML model prediction was beneficial overall. This is the first study in the spine surgery literature that has evaluated the impact of a ML prediction model on surgeon prediction accuracy of post-surgical complications.
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Degerman, Engfeldt Johnny. "Predicting Electrochromic Smart Window Performance." Licentiate thesis, KTH, Tillämpad elektrokemi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-95167.

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The building sector is one of the largest consumers of energy, where the cooling of buildings accounts for a large portion of the total energy consumption. Electrochromic (EC) smart windows have a great potential for increasing indoor comfort and saving large amounts of energy for buildings. An EC device can be viewed as a thin-film electrical battery whose charging state is manifested in optical absorption, i.e. the optical absorption increases with increased state-of-charge (SOC) and decreases with decreased state-of-charge. It is the EC technology's unique ability to control the absorption (transmittance) of solar energy and visible light in windows with small energy effort that can reduce buildings' cooling needs. Today, the EC technology is used to produce small windows and car rearview mirrors, and to reach the construction market it is crucial to be able to produce large area EC devices with satisfactory performance. A challenge with up-scaling is to design the EC device system with a rapid and uniform coloration (charging) and bleaching (discharging). In addition, up-scaling the EC technology is a large economic risk due to its expensive production equipment, thus making the choice of EC material and system extremely critical. Although this is a well-known issue, little work has been done to address and solve these problems. This thesis introduces a cost-efficient methodology, validated with experimental data, capable of predicting and optimizing EC device systems' performance in large area applications, such as EC smart windows. This methodology consists of an experimental set-up, experimental procedures and a twodimensional current distribution model. The experimental set-up, based on camera vision, is used in performing experimental procedures to develop and validate the model and methodology. The two-dimensional current distribution model takes secondary current distribution with charge transfer resistance, ohmic and time-dependent effects into account. Model simulations are done by numerically solving the model's differential equations using a finite element method. The methodology is validated with large area experiments. To show the advantage of using a well-functioning current distribution model as a design tool, some EC window size coloration and bleaching predictions are also included. These predictions show that the transparent conductor resistance greatly affects the performance of EC smart windows.
Byggnadssektorn är en av de största energiförbrukarna, där kylningen av byggnader står för en stor del av den totala energikonsumtionen. Elektrokroma (EC) smarta fönster har en stor potential för att öka inomhuskomforten och spara stora mängder energi för byggnader. Ett elektrokromt fönster kan ses som ett tunnfilmsbatteri vars laddningsnivå yttrar sig i dess optiska absorption, d.v.s. den optiska absorptionen ökar med ökad laddningsnivå och vice versa. Det är EC-teknologins unika egenskaper att kunna kontrollera absorptionen (transmittansen) av solenergi och synligt ljus i fönster med liten energiinsats som kan minska byggnaders kylningsbehov. EC-teknologin används idag till att producera små fönster och bilbackspeglar, men för att nå byggnadsmarknaden är det nödvändigt att kunna producera stora EC-anordningar med fullgod prestanda. En välkänd utmaning med uppskalning är att utforma EC-systemet med snabb och jämn infärgning (laddning) och urblekning (urladdning), vilket även innebär att uppskalning är en stor ekonomisk risk på grund av den dyra produktionsutrustningen. Trots att detta är välkända problem har lite arbete gjorts för att lösa dessa. Denna avhandling introducerar ett kostnadseffektivt tillvägagångssätt, validerat med experimentella data, kapabelt till att förutsäga och optimera ECsystems prestanda för anordningar med stor area, såsom elektrokroma smarta fönster. Detta tillvägagångssätt består av en experimentell uppställning, experiment och en tvådimensionell strömfördelningsmodell. Den experimentella uppställningen, baserad på kamerateknik, används i de experimentella tillvägagångssätten så att modellen kan utvecklas och valideras. Den tvådimensionella strömfördelningsmodellen inkluderar sekundär strömfördelning med laddningsöverföringsmotstånd, ohmska och tidsberoende effekter. Modellsimuleringarna görs genom att numeriskt lösa en modells differentialekvationer med hjälp av en finita-element-metod. Tillvägagångssättet är validerat med experiment gjorda på stora EC anordningar. För att visa fördelarna med att använda en väl fungerande strömfördelningsmodell som ett designverktyg, har några prediktioner av infärgning och urblekning av EC-fönster inkluderats. Dessa prediktioner visar att den transparenta strömtilledarresistansen har stor påverkan på EC-fönsters prestanda.
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Barnhart, Gregory J. "Predicting hail size using model vertical velocities." Thesis, Monterey, Calif. : Naval Postgraduate School, 2008. http://bosun.nps.edu/uhtbin/hyperion-image.exe/08Mar%5FBarnhart.pdf.

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Thesis (M.S. in Meteorology)--Naval Postgraduate School, March 2008.
Thesis Advisor(s): Nuss, Wendell. "March 2008." Description based on title screen as viewed on April 25, 2008. Includes bibliographical references (p. 47-49). Also available in print.
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Sofi, Backman. "A model for predicting robot dresspack damage." Thesis, Umeå universitet, Institutionen för fysik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-149369.

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McClain, Michael Patrick. "A micromechanical model for predicting tensile strength." Thesis, This resource online, 1996. http://scholar.lib.vt.edu/theses/available/etd-10052007-143117/.

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Gao, Zhiyuan, and Likai Qi. "Predicting Stock Price Index." Thesis, Halmstad University, Applied Mathematics and Physics (CAMP), 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-3784.

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This study is based on three models, Markov model, Hidden Markov model and the Radial basis function neural network. A number of work has been done before about application of these three models to the stock market. Though, individual researchers have developed their own techniques to design and test the Radial basis function neural network. This paper aims to show the different ways and precision of applying these three models to predict price processes of the stock market. By comparing the same group of data, authors get different results. Based on Markov model, authors find a tendency of stock market in future and, the Hidden Markov model behaves better in the financial market. When the fluctuation of the stock price index is not drastic, the Radial basis function neural network has a nice prediction.

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Seidu, Mohammed Nazib. "Predicting Bankruptcy Risk: A Gaussian Process Classifciation Model." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-119120.

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This thesis develops a Gaussian processes model for bankruptcy risk classification and prediction in a Bayesian framework. Gaussian processes and linear logistic models are discriminative methods used for classification and prediction purposes. The Gaussian processes model is a much more flexible model than the linear logistic model with smoothness encoded in the kernel with the potential to improve the modeling of the highly nonlinear relationships between accounting ratios and bankruptcy risk. We compare the linear logistic regression with the Gaussian process classification model in the context of bankruptcy prediction. The posterior distributions of the GPs are non-Gaussian, and we investigate the effectiveness of the Laplace approximation and the expectation propagation approximation across several different kernels for the Gaussian process. The approximate methods are compared to the gold standard of Markov Chain Monte Carlo (MCMC) sampling from the posterior. The dataset is an unbalanced panel consisting of 21846 yearly observations for about 2000 corporate firms in Sweden recorded between 1991−2008. We used 5000 observations to train the models and the rest for evaluating the predictions. We find that the choice of covariance kernel affects the GP model’s performance and we find support for the squared exponential covariance function (SEXP) as an optimal kernel. The empirical evidence suggests that a multivariate Gaussian processes classifier with squared exponential kernel can effectively improve bankruptcy risk prediction with high accuracy (90.19 percent) compared to the linear logistic model (83.25 percent).
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Chen, Dong. "Neural network model for predicting performance of projects." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape9/PQDD_0021/MQ48059.pdf.

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Books on the topic "Predicting model"

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Ferguson, Dennis E. Predicting regeneration establishment with the prognosis model. [Ogden, Utah?]: U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1993.

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Ferguson, Dennis E. Predicting regeneration establishment with the prognosis model. [Ogden, Utah?]: U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1993.

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Ferguson, Dennis E. Predicting regeneration establishment with the prognosis model. [Ogden, Utah?]: U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1993.

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United States. National Aeronautics and Space Administration., ed. Development of a model for predicting NASA/MSFC project success. [Huntsville, Ala.]: Dept. of Industrial and Systems Engineering, University of Alabama in Huntsville, 1990.

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Ward, S. C. Validation of a CFD model for predicting film cooling performance. Washington, D. C: American Institute of Aeronautics and Astronautics, 1993.

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Weber, Randal S. A model for predicting transfusion requirements in head and neck surgery. St. Louis, MO: American Laryngological, Rhinological and Otological Society, 1995.

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Maurice, Clark Robert, and Risk Reduction Engineering Laboratory (U.S.), eds. Predicting the inactivation of giardia lamblia: A mathematical and statistical model. Cincinnati, Ohio: U.S. Environmental Protection Agency, Risk Reduction Engineering Laboratory, 1990.

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Eskridge, Robert E. ROADWAY--a numerical model for predicting air pollutants near highways: User's guide. Research Triangle Park, NC: U.S. Environmental Protection Agency, Atmospheric Sciences Research Laboratory, 1987.

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Eskridge, Robert E. ROADWAY--a numerical model for predicting air pollutants near highways: User's guide. Research Triangle Park, NC: U.S. Environmental Protection Agency, Atmospheric Sciences Research Laboratory, 1987.

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Gentry, James A. Predicting industrial bond ratings with a probit model and funds flow components. [Urbana, Ill.]: College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1985.

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Book chapters on the topic "Predicting model"

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Halbrügge, Marc. "Model-Based UI Development (MBUID)." In Predicting User Performance and Errors, 19–22. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60369-8_3.

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Wasserman, Theodore, and Lori Wasserman. "Predicting Errors and Motivation." In Motivation, Effort, and the Neural Network Model, 77–84. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58724-6_6.

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McMillan, David G. "Forecast and Market Timing Power of the Model and the Role of Inflation." In Predicting Stock Returns, 103–29. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69008-7_6.

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Balaniuk, Remis, Hercules Antonio do Prado, Renato da Veiga Guadagnin, Edilson Ferneda, and Paulo Roberto Cobbe. "Predicting Evasion Candidates in Higher Education Institutions." In Model and Data Engineering, 143–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24443-8_16.

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Qian, Shenghua. "Vehicle Collision Prediction Model on the Internet of Vehicles." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications, 518–30. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_53.

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AbstractAn active collision prediction model on the Internet of Vehicles is proposed. Through big data calculation on the cloud computing platform, the model predicts whether the vehicles may collide and the time of the collision, so the server actively sends warning signals to the vehicles that may collide. Firstly, the vehicle collision prediction model preprocesses the data set, and then constructs a new feature set through feature engineering. For the imbalance of the data set, which affects predictive results, SMOTE algorithm is proposed to generate new samples. Then, the LightGBM algorithm optimized by Bayesian parameters is used to predict the vehicle collision state. Finally, for the problem of low accuracy in predicting the collision time, the time prediction is transformed into a classification problem, and the Bayesian optimization K-means algorithm is used to predict the vehicle collision time. The experimental results prove that the vehicle collision prediction model proposed in this paper has better results.
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Stephan, Blossom Christa Maree. "Models for Predicting Risk of Dementia: Predictive Accuracy and Model Complexity." In International Perspectives on Aging, 141–59. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06650-9_10.

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Geisser, Seymour. "Selecting a statistical model and predicting." In Predictive Inference: An Introduction, 88–117. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4899-4467-2_4.

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Oliveira, Nelson, Joana Costa, Catarina Silva, and Bernardete Ribeiro. "Retweet Predictive Model for Predicting the Popularity of Tweets." In Proceedings of the Tenth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2018), 185–93. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17065-3_19.

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Mounter, William, Huda Dawood, and Nashwan Dawood. "The Impact of Data Segmentation in Predicting Monthly Building Energy Use with Support Vector Regression." In Springer Proceedings in Energy, 69–76. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63916-7_9.

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AbstractAdvances in metering technologies and machine learning methods provide both opportunities and challenges for predicting building energy usage in the both the short and long term. However, there are minimal studies on comparing machine learning techniques in predicting building energy usage on their rolling horizon, compared with comparisons based upon a singular forecast range. With the majority of forecasts ranges being within the range of one week, due to the significant increases in error beyond short term building energy prediction. The aim of this paper is to investigate how the accuracy of building energy predictions can be improved for long term predictions, in part of a larger study into which machine learning techniques predict more accuracy within different forecast ranges. In this case study the ‘Clarendon building’ of Teesside University was selected for use in using it’s BMS data (Building Management System) to predict the building’s overall energy usage with Support Vector Regression. Examining how altering what data is used to train the models, impacts their overall accuracy. Such as by segmenting the model by building modes (Active and dormant), or by days of the week (Weekdays and weekends). Of which it was observed that modelling building weekday and weekend energy usage, lead to a reduction of 11% MAPE on average compared with unsegmented predictions.
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Poovammal, E., Mayank Kumar Nagda, and K. Annapoorani. "Predicting Property Prices: A Universal Model." In EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing, 259–68. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19562-5_26.

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Conference papers on the topic "Predicting model"

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Knight, Michael, Ghousia Saeed, Yu-Horng Chen, and Andre G. P. Brown. "Remote Location in an Urban Digital Model." In eCAADe 2007: Predicting the Future. eCAADe, 2007. http://dx.doi.org/10.52842/conf.ecaade.2007.581.

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Huang, Chuen-huei (Joseph), and Robert J. Krawczyk. "A Choice Model of Consumer Participatory Design for Modular Houses." In eCAADe 2007: Predicting the Future. eCAADe, 2007. http://dx.doi.org/10.52842/conf.ecaade.2007.679.

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Huang, Chuen-huei (Joseph), and Robert J. Krawczyk. "A Choice Model of Consumer Participatory Design for Modular Houses." In eCAADe 2007: Predicting the Future. eCAADe, 2007. http://dx.doi.org/10.52842/conf.ecaade.2007.679.

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Schlueter, Arno, and Tobias Bonwetsch. "The M.ANY Project - Exploring a Matrix Model for a Fully Digital Workflow in Architectural Design." In eCAADe 2007: Predicting the Future. eCAADe, 2007. http://dx.doi.org/10.52842/conf.ecaade.2007.895.

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Aman, Fazal, Azhar Rauf, Rahman Ali, Farkhund Iqbal, and Asad Masood Khattak. "A Predictive Model for Predicting Students Academic Performance." In 2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA). IEEE, 2019. http://dx.doi.org/10.1109/iisa.2019.8900760.

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Liangfang, Lin, Zeng Tao, Yu Yongquan, and Lin Shangfang. "Extension Cluster Prediction Model Used in Predicting Wastewater Emissions." In 2008 International Symposium on Computer Science and Computational Technology. IEEE, 2008. http://dx.doi.org/10.1109/iscsct.2008.179.

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Elmore, Emily, Khalid Al-Mutairi, Bilal Hussain, and A. Sherif El-Gizawy. "Development of Analytical Model for Predicting Dual-Phase Ejector Performance." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-65844.

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An analytical model is developed to extend the single-phase model to dual-phase applications. The introduced dual-phase model helps in predicting ejector performance, particularly pressure recovery and efficiency, to entrained fluids of a liquid/gas mixture. The empirical loss coefficients are replaced by analytical equations accounting for the geometry of and flow conditions within the individual ejector components. In order to verify the present analytical model predictions, liquid ejector performance is studied experimentally when the entrained fluid is both a single-phase liquid (water) and a dual-phase liquid/gas mixture (water/air). The results show consistently better agreement with the experimental data than those delivered by existing models, reducing the root mean square error of the pressure recovery prediction to less than 10% of its former value.
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KARANKA, J., and D. LUQUE. "PREDICTING COLLISION: A CONNECTIONIST MODEL." In Proceedings of the Eighth Neural Computation and Psychology Workshop. WORLD SCIENTIFIC, 2004. http://dx.doi.org/10.1142/9789812702784_0004.

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Nishimura, Tomoki, Akiyoshi Hara, Hiroki Miyamoto, Masahiro Furukawa, and Taro Maeda. "Mutual Prediction Model for Predicting Information for Human Motion Generation." In 2020 IEEE/SICE International Symposium on System Integration (SII). IEEE, 2020. http://dx.doi.org/10.1109/sii46433.2020.9026182.

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Hinkel, Georg, and Misha Strittmatter. "Predicting the Perceived Modularity of MOF-based Metamodels." In 6th International Conference on Model-Driven Engineering and Software Development. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0006539300480058.

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Reports on the topic "Predicting model"

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Barnes, Graham. Predicting Flare Properties Using the Minimum Current Corona Model. Fort Belvoir, VA: Defense Technical Information Center, May 2009. http://dx.doi.org/10.21236/ada503355.

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Scharine, Angelique A., Paula P. Henry, Mohan D. Rao, and Jason T. Dreyer. A Model for Predicting Intelligibility of Binaurally Perceived Speech. Fort Belvoir, VA: Defense Technical Information Center, April 2007. http://dx.doi.org/10.21236/ada466840.

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Levine, Daniel B., John J. Cloos, James Perry, Thomas C. Varley, and Stanley A. Horowitz. A Model for Predicting the Inventory of Navy Spares. Fort Belvoir, VA: Defense Technical Information Center, June 1991. http://dx.doi.org/10.21236/ada243087.

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Dunn, Stuart, Douglas Coats, Gary Nickerson, Samuel Sopok, and Peter O'Hara. Unified Computer Model for Predicting Thermochemical Erosion in Gun Barrels. Fort Belvoir, VA: Defense Technical Information Center, July 1995. http://dx.doi.org/10.21236/ada420028.

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Allen, D. H., and W. E. Haisler. A Model for Predicting Thermomechanical Response of Large Space Structures. Fort Belvoir, VA: Defense Technical Information Center, June 1985. http://dx.doi.org/10.21236/ada162139.

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Allen, D. H., and W. E. Haisler. A Model for Predicting Thermomechanical Response of Large Space Structures. Fort Belvoir, VA: Defense Technical Information Center, July 1986. http://dx.doi.org/10.21236/ada172966.

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Meidani, Hadi, and Amir Kazemi. Data-Driven Computational Fluid Dynamics Model for Predicting Drag Forces on Truck Platoons. Illinois Center for Transportation, November 2021. http://dx.doi.org/10.36501/0197-9191/21-036.

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Fuel-consumption reduction in the truck industry is significantly beneficial to both energy economy and the environment. Although estimation of drag forces is required to quantify fuel consumption of trucks, computational fluid dynamics (CFD) to meet this need is expensive. Data-driven surrogate models are developed to mitigate this concern and are promising for capturing the dynamics of large systems such as truck platoons. In this work, we aim to develop a surrogate-based fluid dynamics model that can be used to optimize the configuration of trucks in a robust way, considering various uncertainties such as random truck geometries, variable truck speed, random wind direction, and wind magnitude. Once trained, such a surrogate-based model can be readily employed for platoon-routing problems or the study of pavement performance.
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Yoo, Junsoo. Development of a Mechanistic Model for Predicting Sliding Vapor Bubble Growth. Office of Scientific and Technical Information (OSTI), August 2017. http://dx.doi.org/10.2172/1468535.

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Kumar, A., P. G. Young, and M. B. Chadwick. Assessment of some optical model potentials in predicting neutron cross sections. Office of Scientific and Technical Information (OSTI), March 1998. http://dx.doi.org/10.2172/572672.

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Vitek, J. M., Y. S. Iskander, E. M. Oblow, S. S. Babu, and S. A. David. Neural network model for predicting ferrite number in stainless steel welds. Office of Scientific and Technical Information (OSTI), November 1998. http://dx.doi.org/10.2172/290929.

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