Compression-based methods for nonparametric density estimation, on-line prediction, regression and classification for time series
<|reference_start|>Compression-based methods for nonparametric density estimation, on-line prediction, regression and classification for time series: 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.<|reference_end|>
@article{ryabko2007compression-based,
    title={Compression-based methods for nonparametric density estimation, on-line
  prediction, regression and classification for time series},
    author={Boris Ryabko},
    journal={arXiv preprint arXiv:cs/0701036},
    year={2007},
    archivePrefix={arXiv},
    eprint={cs/0701036},
    primaryClass={cs.IT math.IT}
}