Online Time Series Prediction with Missing Data
Online Time Series Prediction with Missing Data
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发表时间：
2015-07
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通讯作者：
Oren Anava;Elad Hazan;A. Zeevi
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作者：
Oren Anava;Elad Hazan;A. Zeevi
我们考虑在缺失数据的存在下的时间序列预测问题。我们把这个问题转化为一个在线学习问题，学习者的目标是最小化预测误差。然后，我们设计了一个有效的算法的问题，这是基于自回归模型，并没有假设任何结构上的缺失数据，也没有机制，产生的时间序列。我们表明，我们的算法的性能渐近接近的性能最好的AR预测事后诸葛亮，并证实了理论结果与实证研究的合成和现实世界的数据。
We consider the problem of time series prediction in the presence of missing data. We cast the problem as an online learning problem in which the goal of the learner is to minimize prediction error. We then devise an efficient algorithm for the problem, which is based on autoregressive model, and does not assume any structure on the missing data nor on the mechanism that generates the time series. We show that our algorithm's performance asymptotically approaches the performance of the best AR predictor in hindsight, and corroborate the theoretic results with an empirical study on synthetic and real-world data.