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|Abstract:||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.|
|Citation:||Anava, Oren, Elad Hazan, and Assaf Zeevi. "Online Time Series Prediction with Missing Data." In Proceedings of the 32nd International Conference on Machine Learning (2015): pp. 2191-2199.|
|Pages:||2191 - 2199|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||Proceedings of the 32nd International Conference on Machine Learning|
|Version:||Final published version. Article is made available in OAR by the publisher's permission or policy.|
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