Compression-based methods for nonparametric density estimation, on-line prediction, regression and classification for time series

Details Compression-based methods for nonparametric density estimation, on-line prediction, regression and classification for time series Compression-based methods for nonparametric density estimation, on-line prediction, regression and classification for time series

2007-01-07

arxiv.org/abs/cs/0701036v2

Computer Science

Information Theory

Scientific paper

We address the problem of nonparametric estimation of characteristics for stationary and ergodic time series. We consider finite-alphabet time series and real-valued ones and the following four problems: i) estimation of the (limiting) probability (or estimation of the density for real-valued time series), ii) on-line prediction, iii) regression and iv) classification (or so-called problems with side information). We show that so-called archivers (or data compressors) can be used as a tool for solving these problems. In particular, firstly, it is proven that any so-called universal code (or universal data compressor) can be used as a basis for constructing asymptotically optimal methods for the above problems. (By definition, a universal code can "compress" any sequence generated by a stationary and ergodic source asymptotically till the Shannon entropy of the source.) And, secondly, we show experimentally that estimates, which are based on practically used methods of data compression, have a reasonable precision.

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