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

arXiv: https://arxiv.org/abs/2204.00872 · HTML: https://mlsanity.com/paper/2204.00872/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2022.

## Built on by (most cited first, 2 of 2)

- [An adaptive standardisation methodology for Day-Ahead electricity price forecasting](https://mlsanity.com/paper/2311.02610/) (2023)
- [Data-driven Calibration Sample Selection and Forecast Combination in Electricity Price Forecasting: An Application of the ARHNN Method](https://mlsanity.com/paper/2510.15011/) (2025)

## Abstract (arXiv)

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 τ 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.