Uncertainty Quantification through Dropout in Time Series Prediction by Echo State Networks

Miguel Atencia, Ruxandra Stoean and Gonzalo Joya
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Miguel Atencia: Department of Applied Mathematics, Universidad de Málaga, 29071-Málaga, Spain
Ruxandra Stoean: Romanian Institute of Science and Technology, 400022-Cluj-Napoca, Romania
Gonzalo Joya: Department of Electronics Technology, Universidad de Málaga, 29071-Málaga, Spain

Mathematics, 2020, vol. 8, issue 8, 1-13

Abstract: The application of echo state networks to time series prediction has provided notable results, favored by their reduced computational cost, since the connection weights require no learning. However, there is a need for general methods that guide the choice of parameters (particularly the reservoir size and ridge regression coefficient), improve the prediction accuracy, and provide an assessment of the uncertainty of the estimates. In this paper we propose such a mechanism for uncertainty quantification based on Monte Carlo dropout, where the output of a subset of reservoir units is zeroed before the computation of the output. Dropout is only performed at the test stage, since the immediate goal is only the computation of a measure of the goodness of the prediction. Results show that the proposal is a promising method for uncertainty quantification, providing a value that is either strongly correlated with the prediction error or reflects the prediction of qualitative features of the time series. This mechanism could eventually be included into the learning algorithm in order to obtain performance enhancements and alleviate the burden of parameter choice.

Keywords: echo state networks; reservoir computing; uncertainty quantification; dropout; ensemble learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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