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Bibliographies thématiques / Dynaic prediction

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Littérature scientifique sur le sujet « Dynaic prediction »

Auteur : Grafiati

Publié le 25 janvier 2025

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Articles de revues sur le sujet "Dynaic prediction"

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Daniele, Mario, and Elisa Raoli. "Early Warning Systems for financial crises prediction in private companies: Evidence from the Italian context." FINANCIAL REPORTING, no. 2 (December 2024): 133–61. https://doi.org/10.3280/fr2024-002006.

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Purpose: This study compares models for predicting business financial crises, fo-cusing on which are most effective. In light of the new European Directive on business failure, it highlights a trade-off between predictive accuracy and timeli-ness in static models and offers an alternative approach. Design/methodology/approach: This study examines the Italian early warning system (EWS), testing static alert indicators' predictive ability on a large sample of private companies. It then proposes a dynamic version of the EWS. Findings: The results show a trade-off between predictive ability and ti

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Lin, Huan, Weiye Yu, and Zhan Lian. "Influence of Ocean Current Features on the Performance of Machine Learning and Dynamic Tracking Methods in Predicting Marine Drifter Trajectories." Journal of Marine Science and Engineering 12, no. 11 (2024): 1933. http://dx.doi.org/10.3390/jmse12111933.

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Accurately and rapidly predicting marine drifter trajectories under conditions of information scarcity is critical for addressing maritime emergencies and conducting marine surveys with resource-limited unmanned vessels. Machine learning-based tracking methods, such as Long Short-Term Memory networks (LSTM), offer a promising approach for trajectory prediction in such scenarios. This study combines satellite observations and idealized simulations to compare the predictive performance of LSTM with a resource-dependent dynamic tracking method (DT). The results indicate that when driven solely by

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Stoodley, Catherine J., and Peter T. Tsai. "Adaptive Prediction for Social Contexts: The Cerebellar Contribution to Typical and Atypical Social Behaviors." Annual Review of Neuroscience 44, no. 1 (2021): 475–93. http://dx.doi.org/10.1146/annurev-neuro-100120-092143.

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Social interactions involve processes ranging from face recognition to understanding others’ intentions. To guide appropriate behavior in a given context, social interactions rely on accurately predicting the outcomes of one's actions and the thoughts of others. Because social interactions are inherently dynamic, these predictions must be continuously adapted. The neural correlates of social processing have largely focused on emotion, mentalizing, and reward networks, without integration of systems involved in prediction. The cerebellum forms predictive models to calibrate movements and adapt

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Oh, Cheol, Stephen G. Ritchie, and Jun-Seok Oh. "Exploring the Relationship between Data Aggregation and Predictability to Provide Better Predictive Traffic Information." Transportation Research Record: Journal of the Transportation Research Board 1935, no. 1 (2005): 28–36. http://dx.doi.org/10.1177/0361198105193500104.

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Providing reliable predictive traffic information is a crucial element for successful operation of intelligent transportation systems. However, there are difficulties in providing accurate predictions mainly because of limitations in processing data associated with existing traffic surveillance systems and the lack of suitable prediction techniques. This study examines different aggregation intervals to characterize various levels of traffic dynamic representations and to investigate their effects on prediction accuracy. The relationship between data aggregation and predictability is explored

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Siek, M., and D. P. Solomatine. "Nonlinear chaotic model for predicting storm surges." Nonlinear Processes in Geophysics 17, no. 5 (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,

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Prasanna, Christopher, Jonathan Realmuto, Anthony Anderson, Eric Rombokas, and Glenn Klute. "Using Deep Learning Models to Predict Prosthetic Ankle Torque." Sensors 23, no. 18 (2023): 7712. http://dx.doi.org/10.3390/s23187712.

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Inverse dynamics from motion capture is the most common technique for acquiring biomechanical kinetic data. However, this method is time-intensive, limited to a gait laboratory setting, and requires a large array of reflective markers to be attached to the body. A practical alternative must be developed to provide biomechanical information to high-bandwidth prosthesis control systems to enable predictive controllers. In this study, we applied deep learning to build dynamical system models capable of accurately estimating and predicting prosthetic ankle torque from inverse dynamics using only s

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Bisola Oluwafadekemi Adegoke, Tolulope Odugbose, and Christiana Adeyemi. "Data analytics for predicting disease outbreaks: A review of models and tools." International Journal of Life Science Research Updates 2, no. 2 (2024): 001–9. http://dx.doi.org/10.53430/ijlsru.2024.2.2.0023.

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The burgeoning field of data analytics has emerged as a pivotal force in the realm of public health, particularly in the context of predicting and mitigating disease outbreaks. This comprehensive review delves into the diverse landscape of models and tools employed in data analytics for disease outbreak prediction. With a focus on synthesizing existing knowledge, the paper aims to provide a nuanced understanding of the strengths, limitations, and future directions within this dynamic field. The review begins with an exploration of various models utilized for disease outbreak prediction, rangin

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Zhang, Xiaopeng. "Paris House Rental Price Index Prediction-A Classical Statistical Model Approach." Highlights in Science, Engineering and Technology 88 (March 29, 2024): 294–99. http://dx.doi.org/10.54097/q6kz2d72.

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The study focuses on predicting rental prices in Paris and aims to contribute to urban economics and data analytics. It analyzes a wide range of data sources, including historical rental prices, economic indicators, demographics, and regulations. The goal is to compare classical statistical models' prediction accuracy of these three models: ARIMA, dynamic regression, and random forest. The results reveal that the ARIMA model performs best, offering more accurate predictions. ARIMA relies on time series analysis, capturing complex patterns in rental prices, making it well-suited for dynamic rea

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Nik Nurul Hafzan, Mat Yaacob, Deris Safaai, Mat Asiah, Mohamad Mohd Saberi, and Safaai Siti Syuhaida. "Review on Predictive Modelling Techniques for Identifying Students at Risk in University Environment." MATEC Web of Conferences 255 (2019): 03002. http://dx.doi.org/10.1051/matecconf/201925503002.

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Predictive analytics including statistical techniques, predictive modelling, machine learning, and data mining that analyse current and historical facts to make predictions about future or otherwise unknown events. Higher education institutions nowadays are under increasing pressure to respond to national and global economic, political and social changes such as the growing need to increase the proportion of students in certain disciplines, embedding workplace graduate attributes and ensuring that the quality of learning programs are both nationally and globally relevant. However, in higher ed

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Kim, Jeonghun, and Ohbyung Kwon. "A Model for Rapid Selection and COVID-19 Prediction with Dynamic and Imbalanced Data." Sustainability 13, no. 6 (2021): 3099. http://dx.doi.org/10.3390/su13063099.

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The COVID-19 pandemic is threatening our quality of life and economic sustainability. The rapid spread of COVID-19 around the world requires each country or region to establish appropriate anti-proliferation policies in a timely manner. It is important, in making COVID-19-related health policy decisions, to predict the number of confirmed COVID-19 patients as accurately and quickly as possible. Predictions are already being made using several traditional models such as the susceptible, infected, and recovered (SIR) and susceptible, exposed, infected, and resistant (SEIR) frameworks, but these

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