  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.
