Improving Multi-Step Prediction of Learned Time Series Models

  title={Improving Multi-Step Prediction of Learned Time Series Models},
  author={Arun Venkatraman and Martial Hebert and J. Andrew Bagnell},
Most typical statistical and machine learning approaches to time series modeling optimize a singlestep prediction error. In multiple-step simulation, the learned model is iteratively applied, feeding through the previous output as its new input. Any such predictor however, inevitably introduces errors, and these compounding errors change the input distribution for future prediction steps, breaking the train-test i.i.d assumption common in supervised learning. We present an approach that reuses… CONTINUE READING
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