This book aims to provide readers with the current information, developments, and trends in a time series analysis, particularly in time series data patterns, technical methodologies, and real-world applications.
... 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 ...
This book introduces deep learning for time series analysis, particularly for cyclic time series. It elaborates on the methods employed for time series analysis at the deep level of their architectures.
... ., 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 is gaining traction and considerable attention due to the state-of-the-art results obtained in computer vision, object detection, natural language processing, sequential analysis, and multiple other domains.
This book takes the reader beyond the ‘black-box’ approach to neural networks and provides the knowledge that is required for their proper design and use in financial markets forecasting —with an emphasis on futures trading.
Primarily intended for students of statistics, economics and data science with an undergraduate knowledge of probability and statistics, the book will equally appeal to industry professionals in the fields of artificial intelligence and ...
These insights and patterns can then be utilized to explore past events and forecast future values in the series. This book explores the basics of time series analysis with R and lays the foundations you need to build forecasting models.
The book introduces popular forecasting methods and approaches used in a variety of business applications. The book offers clear explanations, practical examples, and end-of-chapter exercises and cases.