## Abstract

The problem concerning the aggregating of the forecasts of specialized expert strategies is examined using the mathematical theory of machine learning. Expert strategies are understood as the algorithms capable of successively predicting the components of a time series in the online mode. The specialized strategies can refrain from predictions at certain time instants—they make forecasts in compliance with the application area of the specific model of an object region forming their basis. An optimal algorithm whereby the forecasts of such expert strategies are aggregated into the single forecast is proposed. The algorithmic optimality consists in that, on average, its total losses are asymptotically less than those of any active prediction strategies on a set of time instants. The uppermost estimated error of the given mixing of predictions, i.e., the regret of aggregating strategies, is determined. The errors are estimated in the worst situation where no assumptions are made about the mechanism underlying the initial data source. The proposed algorithm is tested using the real information on the commodity circulation of a trading network. The numerical results and estimates of the regret are presented.

Original language | English |
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Pages (from-to) | 1400-1410 |

Number of pages | 11 |

Journal | Journal of Communications Technology and Electronics |

Volume | 61 |

Issue number | 12 |

DOIs | |

Publication status | Published - 1 Dec 2016 |

Externally published | Yes |

## Keywords

- adaptive regret
- aggregating algorithm
- online learning
- specialized experts