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Calibration window selection based on change-point detection for forecasting electricity prices

Julia Nasiadka, Weronika Nitka and Rafał Weron

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Abstract: We employ a recently proposed change-point detection algorithm, the Narrowest-Over-Threshold (NOT) method, to select subperiods of past observations that are similar to the currently recorded values. Then, contrarily to the traditional time series approach in which the most recent $\tau$ observations are taken as the calibration sample, we estimate autoregressive models only for data in these subperiods. We illustrate our approach using a challenging dataset - day-ahead electricity prices in the German EPEX SPOT market - and observe a significant improvement in forecasting accuracy compared to commonly used approaches, including the Autoregressive Hybrid Nearest Neighbors (ARHNN) method.

Date: 2022-04
New Economics Papers: this item is included in nep-ecm, nep-ene, nep-ets, nep-for and nep-ore
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Handle: RePEc:arx:papers:2204.00872