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Andreas S. Weigend, Neil A. Gershenfeld, eds.
Time Series Prediction: forecasting the future and understanding the past.
Addison-Wesley. 1993

Most observational disciplines, such as physics, biology, and finance, try to infer properties of an unfamiliar system from the analysis of a measured time record of its behavior. There are many mature techniques associated with traditional time series analysis. However, during the last decade, several new and and innovative approaches have emerged (such as neural networks and time-delay embedding), promising insights not available with these standard methods. Unfortunately, the realization of this promise has been difficult. Adequate bench-marks have been lacking, and much of the literature has been fragmentary and anecdotal.

This volume addresses these shortcomings by presenting the results of a careful comparison of different methods for time series prediction and characterization. This breadth and depth was achieved through the Santa Fe Time Series Prediction and Analysis Competition, which brought together an international group of time series experts from a wide variety of fields to analyze data from the following common data sets:
• A physics laboratory experiment (NH3 laser)
• Physiological data from a patient with sleep apnea
• Tick-by-tick currency exchange rate data
• A computer-generated series designed specifically for the competition
• Astrophysical data from a variable white dwarf star
• J. S. Bach’s last (unfinished) fugue from Die Kunst der Fuge.

In bringing together the results of this unique competition, this volume serves as a much-needed survey of the latest techniques in time series analysis.

Contents

• Tim D. Sauer. Time series prediction using delay coordinate embedding. 1993