Conformal Prediction Regions for Time Series Using Linear Complementarity Programming
Matthew Cleaveland,
Insup Lee,
George J. Pappas
et al.
Abstract:Conformal prediction is a statistical tool for producing prediction regions of machine learning models that are valid with high probability. However, applying conformal prediction to time series data leads to conservative prediction regions. In fact, to obtain prediction regions over T time steps with confidence 1--delta, previous works require that each individual prediction region is valid with confidence 1--delta/T. We propose an optimization-based method for reducing this conservatism to enable long hor… Show more
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