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摘要翻译：
时间序列预测广泛应用于医学、环境和经济等领域的日常统计应用。本文提出了基于专家组合的非参数预测策略，并证明了这些策略在最小条件下的普遍一致性。我们对真实数据集进行了深入的分析，并表明这些非参数策略更灵活，更快速，在归一化累积预测误差方面通常优于ARMA方法。
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英文标题：
《Nonparametric sequential prediction of time series》
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作者：
G\'erard Biau, Kevin Bleakley, L\'aszl\'o Gy\"orfi and Gy\"orgy
Ottucs\'ak
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最新提交年份：
2008
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分类信息：
一级分类：Statistics 统计学
二级分类：Methodology 方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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一级分类：Mathematics 数学
二级分类：Probability 概率
分类描述：Theory and applications of probability and stochastic processes: e.g. central limit theorems, large deviations, stochastic differential equations, models from statistical mechanics, queuing theory
概率论与随机过程的理论与应用：例如中心极限定理，大偏差，随机微分方程，统计力学模型，排队论
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英文摘要：
Time series prediction covers a vast field of every-day statistical applications in medical, environmental and economic domains. In this paper we develop nonparametric prediction strategies based on the combination of a set of 'experts' and show the universal consistency of these strategies under a minimum of conditions. We perform an in-depth analysis of real-world data sets and show that these nonparametric strategies are more flexible, faster and generally outperform ARMA methods in terms of normalized cumulative prediction error.
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PDF链接：
https://arxiv.org/pdf/801.0327
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