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.

The readers will learn the fundamentals of PyTorch in the early stages of the book. Next, the time series forecasting is covered in greater depth after the programme has been developed.

Time series forecasting is different from other machine learning problems.

Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challengesin time series, using both traditional statistical and ...

Despite its increasing relevance, getting the most out of deep learning requires significant technical expertise. This book guides you through applying deep learning to time series data with the help of easy-to-follow code recipes.

Style and approach This book takes the readers from the basic to advance level of Time series analysis in a very practical and real world use cases.

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.

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 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.

... 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 ...