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Foretelling the Future by Adaptive Modeling

Dynamic Markov models can compress data to 2.2 bits per character or 0.18 bits per pixel

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A Computer Science Reader

Abstract

Foretelling the future is a pastime that all of us enjoy, but is not normally viewed as a scientific activity. We would all like to be able to predict which horse will win a particular race or which stock will double next week. If we knew how to do that, we would not be writing this article. Yet it is often quite possible to predict the future—in a limited way—and to put the prediction to good use. By describing an expected, “normal” course of events, a good prediction focuses attention on how an unfolding event differs from the norm. In many cases this is more interesting than the events themselves.

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Further Reading

  • Huffman, D.A. “A Method for the Construction of Minimum-Redundancy Codes.” Proceedings of the Institute of Radio Engineers 40 (1952): 1698.

    Google Scholar 

  • Shannon, C.E., and Weaver, W. The Mathematical Theory of Communication. Urbana, IL: The University of Illinois Press, 1949.

    MATH  Google Scholar 

  • Wiener, N. Extrapolation, Interpolation and Smoothing of Stationary Time Series. Cambridge, MA: M.I.T. Press, 1947.

    Google Scholar 

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© 1988 Springer Science+Business Media New York

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Witten, I.H., Cleary, J.G. (1988). Foretelling the Future by Adaptive Modeling. In: Weiss, E.A. (eds) A Computer Science Reader. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-8726-6_10

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  • DOI: https://doi.org/10.1007/978-1-4419-8726-6_10

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4612-6458-3

  • Online ISBN: 978-1-4419-8726-6

  • eBook Packages: Springer Book Archive

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