Forecasting is required in many situations.

Some of the key mathematical results are stated without proof in order to make the underlying theory acccessible to a wider audience. The book assumes a knowledge only of basic calculus, matrix algebra, and elementary statistics.

The book is also an excellent reference for practitioners and researchers who need to model and analyze time series data to generate forecasts.

This volume introduces the reader to the tools, methods, and techniques of forecasting, specifically as they apply to financial and investing decisions.

This is an ideal introduction for university students studying forecasting, practitioners new to the field and for general readers interested in how economists forecast.

Providing a clear explanation of the fundamental theory of time series analysis and forecasting, this book couples theory with applications of two popular statistical packages--SAS and SPSS.

This edition contains a large number of additions and corrections scattered throughout the text, including the incorporation of a new chapter on state-space models.

The book is also an excellent reference for practitioners and researchers who need to model and analyze time series data to generate forecasts.

But a vast amount of successful prediction is nonetheless possible, especially in the context of applied sciences such as medicine, meteorology, and engineering. This book examines our prospects for finding out about the future in advance.

Time series forecasting is different from other machine learning problems.