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Introduction

Part of the Springer Series in Statistics book series (SSS)

Abstract

In attempts to understand the world around us, observations are frequently made sequentially over time. Values in the future depend, usually in a stochastic manner, on the observations available at present. Such dependence makes it worthwhile to predict the future from its past. Indeed, we will depict the underlying dynamics from which the observed data are generated and will therefore forecast and possibly control future events. This chapter introduces some examples of time series data and probability models for time series processes. It also gives a brief overview of the fundamental ideas that will be introduced in this book.

Keywords

Time Series Sunspot Number Time Series Model Nonlinear Time Series White Noise Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Sciences+Business Media, Inc. 2005

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