Quantifying the Uncertainty of Reservoir Computing: Confidence Intervals for Time-Series Forecasting

Laia Domingo, Mar Grande, Florentino Borondo and Javier Borondo ()
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Laia Domingo: Grupo de Sistemas Complejos, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Mar Grande: Grupo de Sistemas Complejos, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Florentino Borondo: Departamento de Química, Universidad Autónoma de Madrid, Cantoblanco, 28049 Madrid, Spain
Javier Borondo: AGrowingData, 04001 Almería, Spain

Mathematics, 2024, vol. 12, issue 19, 1-11

Abstract: Recently, reservoir computing (RC) has emerged as one of the most effective algorithms to model and forecast volatile and chaotic time series. In this paper, we aim to contribute to the understanding of the uncertainty associated with the predictions made by RC models and to propose a methodology to generate RC prediction intervals. As an illustration, we analyze the error distribution for the RC model when predicting the price time series of several agri-commodities. Results show that the error distributions are best modeled using a Normal Inverse Gaussian (NIG). In fact, NIG outperforms the Gaussian distribution, as the latter tends to overestimate the width of the confidence intervals. Hence, we propose a methodology where, in the first step, the RC generates a forecast for the time series and, in the second step, the confidence intervals are generated by combining the prediction and the fitted NIG distribution of the RC forecasting errors. Thus, by providing confidence intervals rather than single-point estimates, our approach offers a more comprehensive understanding of forecast uncertainty, enabling better risk assessment and more informed decision-making in business planning based on forecasted prices.

Keywords: reservoir computing; uncertainty; confidence intervals; time series; market; prices (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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