... ., et al.: Data augmentation techniques in time series domain: A survey and taxonomy. arXiv preprint arXiv:220613508 (2022) updates 2 5 1 4 Prediction of Deposition Parameters in Optimizing Biomass Forecasting and Supply Chain 71 ...
... Deep learning for time series forecasting : Tutorial and literature survey . ACM Computing Surveys , 55 ( 6 ) , 1-36 . ISSN 0360-0300 . https://doi.org/10.1145/3533382 Bica , I. , Alaa , A. M. , Jordon , J. , & van der Schaar , M ...
Deep learning methods offer a lot of promise for time series forecasting, such as the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality.
... literature review shows that ML methods play crucial roles in the domain of forecasting time series data. The use of ... Tutorial Survey of Architectures, Algorithms, and Application for Deep Learning-ERRATUM”. APSIPA Transaction ...
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... literature review. Appl. Soft Comput. 36, 534–551 (2015) 5. Aguilar-Rivera, R., Valenzuela-Rend-on, M., Rodr-guez-Ortiz, J.: Genetic algorithms and Darwinian ... Neural Network vs Deep Learning for Forecasting 14 R. A. Kawy et al.
This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for ...
This book contains selected papers from the 7th International Conference on Information Science and Applications (ICISA 2016) and provides a snapshot of the latest issues encountered in technical convergence and convergences of security ...
This book explores recent Machine Learning advances on flood forecast and management in a timely manner and presents interdisciplinary approaches to modelling the complexity of flood hazards-related issues, with contributions to integrative ...