利用专家建议预测局部静态数据

IF 0.5 4区 计算机科学 Q4 COMPUTER SCIENCE, THEORY & METHODS
V. V. V’yugin, V. G. Trunov, R. D. Zukhba
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引用次数: 0

摘要

我们要解决的是终身机器学习问题。在博弈论方法中,在计算下一次预测时,我们对产生数据流的源的随机性质不作任何假设:源可以是模拟的,也可以是算法的,还可以是概率的;它的参数可以随机变化;在构建预测模型时,只对数据产生的性质作结构性假设。我们提出了一种局部静止时间序列的在线预测算法。我们还对所提算法的效率进行了估计。我们通过数值实验结果说明了所获得的算法遗憾估计值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Prediction of Locally Stationary Data Using Expert Advice

Prediction of Locally Stationary Data Using Expert Advice

We address the lifelong machine learning problem. Within the game-theoretic approach, in the calculation of the next prediction we use no assumptions on the stochastic nature of a source that generates the data flow: the source can be either analog, or algorithmic, or probabilistic; its parameters can change at random times; when constructing a prediction model, only structural assumptions are used about the nature of data generation. We present an online forecasting algorithm for a locally stationary time series. We also obtain an estimate for the efficiency of the proposed algorithm. The obtained estimates for the regret of the algorithm are illustrated by results of numerical experiments.

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来源期刊
Problems of Information Transmission
Problems of Information Transmission 工程技术-计算机:理论方法
CiteScore
2.00
自引率
25.00%
发文量
10
审稿时长
>12 weeks
期刊介绍: Problems of Information Transmission is of interest to researcher in all fields concerned with the research and development of communication systems. This quarterly journal features coverage of statistical information theory; coding theory and techniques; noisy channels; error detection and correction; signal detection, extraction, and analysis; analysis of communication networks; optimal processing and routing; the theory of random processes; and bionics.
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