This is the first book that integrates useful parametric and nonparametric techniques with time series modeling and prediction, the two important goals of time series analysis.

This book aims to deepen your understanding of time series by providing a comprehensive overview of popular Python time-series packages and help you build better predictive systems.

This thesis offers a thorough review of time series forecasting models, starting with simple forecasting methods such as linear models and ending up combining this method with dynamic models to represent interdependencies of the time series ...

The book also serves as an indispensablereference for practitioners in business, economics, engineering, statistics, mathematics, and the social, environmental, and life sciences.

From the author of the bestselling "Analysis of Time Series," Time-Series Forecasting offers a comprehensive, up-to-date review of forecasting methods.

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 hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.

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.

Not only is the coverage of traditional forecasting methods greatly expanded in this new edition, but a number of new techniques and methods are covered as well.

"Time series analysis is applicable in a variety of disciplines such as business administration, economics, public finances, engineering, statistics, econometrics, mathematics and actuarial sciences.