Time Series Prediction: Forecasting The Future And Understanding The Past

Paperback | November 20, 1993

byAndreas S. WeigendEditorNeil A. Gershenfeld, EDITOR *

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The book is a summary of a time series forecasting competition that was held a number of years ago. The competition used four different kinds of time series (for example, one data set was chaotic from measurements of a laser, and another was a multidimensional physiological times series of heart beats and respiration, etc.).
The strength of the book lies in that it represents several ways to approach real time series prediction strategies in a concrete way - Invaluable, especially to researchers who may be just beginning.

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From Our Editors

This volume addresses these shortcomings by presenting the results of a careful comparison of different methods for time series prediction and characterization.

From the Publisher

The book is a summary of a time series forecasting competition that was held a number of years ago. The competition used four different kinds of time series (for example, one data set was chaotic from measurements of a laser, and another was a multidimensional physiological times series of heart beats and respiration, etc.). The streng...

From the Jacket

This volume addresses these shortcomings by presenting the results of a careful comparison of different methods for time series prediction and characterization.

Neil Gershenfeld is the Director of MIT's Center for Bits and Atoms, and the former director of its famed Media Lab. The author of numerous technical publications, patents, and books, including When Things Start to Think, he has been featured in media such as the New York Times, The Economist, CNN, and PBS. He lives in Somerville, Mass...

other books by Andreas S. Weigend

Format:PaperbackDimensions:672 pages, 9 × 6 × 1.46 inPublished:November 20, 1993Publisher:Avalon Publishing

The following ISBNs are associated with this title:

ISBN - 10:0201626020

ISBN - 13:9780201626025

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From Our Editors

This volume addresses these shortcomings by presenting the results of a careful comparison of different methods for time series prediction and characterization.