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.
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© 2005 Springer Sciences+Business Media, Inc.
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(2005). Introduction. In: Nonlinear Time Series. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-69395-8_1
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DOI: https://doi.org/10.1007/978-0-387-69395-8_1
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-26142-3
Online ISBN: 978-0-387-69395-8
eBook Packages: Springer Book Archive