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Estimating Predictability: Redundancy and Surrogate Data Method

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  • Milan Palus
  • L. Pecen
  • D. Pivka

Abstract

A method for estimating theoretical predictability of time series is presented, based on information-theoretic functionals---redundancies and surrogate data technique. The redundancy, designed for a chosen model and a prediction horizon, evaluates amount of information between a model input (e.g., lagged versions of the series) and a model output (i.e., a series lagged by the prediction horizon from the model input) in number of bits. This value, however, is influenced by a method and precision of redundancy estimation and therefore it is (a) normalized by maximum possible redundancy (given by the precision used), and (b) compared to the redundancies obtained from two types of the surrogate data in order to obtain reliable classification of a series as either unpredictable or predictable. The type of predictability (linear or nonlinear) and its level can be further evaluated. The method is demonstrated using a numerically generated time series as well as high-frequency foreigh exchange data and the theoretical predictability is compared to performance of a nonlinear predictor.

Suggested Citation

  • Milan Palus & L. Pecen & D. Pivka, 1995. "Estimating Predictability: Redundancy and Surrogate Data Method," Working Papers 95-07-060, Santa Fe Institute.
    • Handle: RePEc:wop:safiwp:95-07-060
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    References listed on IDEAS

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    1. Dacorogna, Michael M. & Muller, Ulrich A. & Nagler, Robert J. & Olsen, Richard B. & Pictet, Olivier V., 1993. "A geographical model for the daily and weekly seasonal volatility in the foreign exchange market," Journal of International Money and Finance, Elsevier, vol. 12(4), pages 413-438, August.
    2. Milan Paluv s, 1994. "Testing for Nonlinearity in Weather Records," Working Papers 94-07-043, Santa Fe Institute.
    3. Dean Prichard & James Theiler, 1994. "Generating Surrogate Data for Time Series with Several Simultaneously Measured Variables," Working Papers 94-04-023, Santa Fe Institute.
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    Cited by:

    1. Yan Jiang & Xin Bao & Shaonan Hao & Hongtao Zhao & Xuyong Li & Xianing Wu, 2020. "Monthly Streamflow Forecasting Using ELM-IPSO Based on Phase Space Reconstruction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(11), pages 3515-3531, September.
    2. Chong Hong & Beom Kim, 2011. "Mutual information and redundancy for categorical data," Statistical Papers, Springer, vol. 52(1), pages 17-31, February.

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