PDF Download Time Series Prediction: Forecasting The Future And Understanding The Past (Santa Fe Institute Series), by Andreas S. Weigend
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Time Series Prediction: Forecasting The Future And Understanding The Past (Santa Fe Institute Series), by Andreas S. Weigend
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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.
- Sales Rank: #1962500 in Books
- Published on: 1993-11-20
- Original language: English
- Number of items: 1
- Dimensions: 9.00" h x 1.50" w x 6.00" l, 2.08 pounds
- Binding: Paperback
- 672 pages
From the Back Cover
This volume addresses these shortcomings by presenting the results of a careful comparison of different methods for time series prediction and characterization.
About the Author
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, Massachusetts.
Most helpful customer reviews
11 of 12 people found the following review helpful.
Excellent book in case studies
By D. Hundley
I am suprised that no one has reviewed this book- I have found it very useful to have on my bookshelf. The book is a summary of a time series forecasting competition that was held a few 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 is 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.
FYI- I think the data is available on the world wide web, so it is possible to perform the analysis that is described in the book.
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