Information Theoretic Mask Analysis of Rainfall Time Series Data

Authors

Martin Zwick, Portland State UniversityFollow
Hui Shu, Portland State University
Roy Koch, Portland State University

Published In

Advances in Systems Science and Applications

Document Type

Post-Print

Publication Date

1-1-1995

Subjects

Information theory, Entropy, Rainfall probabilities

Abstract

This study explores an information-theoretic/log-linear approach to multivariate time series analysis. The method is applied to daily rainfall data(4 sites, 9 years), originally quantitative but here treated as dichotomous. The analysis ascertains which lagged variables are most predictive of future rainfall and how season can be optimally defined as an auxiliary predicting parameter. Call the rainfall variables at the four sites A...D, and collectively, Z, the lagged site variables at t-1, E,,,H, at t-2, I...L, etc. and the seasonal parameter, S. The best model, reducing the Shannon uncertainty, u(Z), by 22%. is HGFSJK Z, where the independent variables, H through K, are given in the order of their predictive power and S is dichotomous with unequal winter and summer lengths. Keywords: Reconstructability Analysis, rainfall modeling, time series analysis, entropy modeling

Rights

This is the author's manuscript version. The final version was published in Advances in Systems Science and Applications.

Persistent Identifier

https://archives.pdx.edu/ds/psu/42748

Citation Details

Zwick, M., Shu, H., and Koch, R. (1995). [Post-print]. "Information-Theoretic Mask Analysis of Rainfall Time-Series Data." Published in Advances in Systems Science and Applications, 1, pp.154-159.