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Improving Multi-step Prediction of Learned Time Series Models

Arun Venkatraman, Martial Hebert and J. Andrew (Drew) Bagnell
Conference Paper, Carnegie Mellon University, Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15), January, 2015

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Abstract

Most typical statistical and machine learning ap- proaches to time series modeling optimize a single- step prediction error. In multiple-step simulation, the learned model is iteratively applied, feeding through the previous output as its new input. Any such pre- dictor however, inevitably introduces errors, and these compounding errors change the input distribution for future prediction steps, breaking the train-test i.i.d as- sumption common in supervised learning. We present an approach that reuses training data to make a no-regret learner robust to errors made during multi-step predic- tion. Our insight is to formulate the problem as imita- tion learning; the training data serves as a “demonstra- tor” by providing corrections for the errors made dur- ing multi-step prediction. By this reduction of multi- step time series prediction to imitation learning, we es- tablish theoretically a strong performance guarantee on the relation between training error and the multi-step prediction error. We present experimental results of our method, DAD, and show significant improvement over the traditional approach in two notably different domains, dynamic system modeling and video texture prediction.

BibTeX Reference
@conference{Venkatraman-2015-5902,
title = {Improving Multi-step Prediction of Learned Time Series Models},
author = {Arun Venkatraman and Martial Hebert and J. Andrew (Drew) Bagnell},
booktitle = {Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15)},
school = {Robotics Institute , Carnegie Mellon University},
month = {January},
year = {2015},
address = {Pittsburgh, PA},
}
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