<ul><li>Accurate evaluation of forecasting models is essential for ensuring reliable predictions.</li><li>Current practices for evaluating and comparing forecasting models focus on summarizing performance into a single score, which may not provide enough information about model behavior under varying conditions.</li><li>To address this limitation, ModelRadar is proposed as a framework for evaluating univariate time series forecasting models across multiple aspects, such as stationarity, presence of anomalies, or forecasting horizons.</li><li>Comparing 24 forecasting methods, including classical approaches and different machine learning algorithms, NHITS, a state-of-the-art neural network architecture, performs best overall, but its superiority varies depending on the forecasting conditions.</li></ul>

ModelRadar: Aspect-based Forecast Evaluation

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