Compression-based methods for nonparametric density estimation, on-line prediction, regression and classification for time series
| dc.creator | Ryabko, Boris | |
| dc.date | 2007-01-07 | |
| dc.date | 2007-11-01 | |
| dc.date.accessioned | 2026-07-07T08:39:47Z | |
| dc.date.available | 2026-07-07T08:39:47Z | |
| dc.description | 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. | |
| dc.identifier | https://arxiv.org/abs/cs/0701036 | |
| dc.identifier | http://arxiv.org/abs/cs/0701036 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/141194 | |
| dc.subject | Information Theory | |
| dc.title | Compression-based methods for nonparametric density estimation, on-line prediction, regression and classification for time series | |
| dc.type | text |