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With this increase in model complexity, however, comes a lack in understanding of the underlying model decision process, which is problematic for safety-critical application scenarios. At the same time, simple, interpretable forecasting methods such as ARIMA and ETS still perform very well, sometimes on-par with Deep Learning approaches. We argue that using interpretable forecasters leads to good predictions in most cases. However, the forecasting performance can be improved by selecting a Deep Learning method only for few, important predictions, increasing the overall interpretability of the forecasting process. In this context, we propose a novel online model selection framework which learns to identify these predictions. An extensive empirical study on various real-world datasets containing over 3500 individual time-series shows that our selection methodology performs comparable to state-of-the-art online model selection methods in most cases while being significantly more interpretable. We find that almost always choosing a simple autoregressive or exponential smoothing model for forecasting, results in competitive performance, suggesting that the need for opaque black-box models in time-series forecasting might be smaller than recent works would suggest.<\/jats:p>","DOI":"10.1007\/s10994-026-07020-2","type":"journal-article","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T12:37:55Z","timestamp":1772800675000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AALF: Almost Always Linear Forecasting"],"prefix":"10.1007","volume":"115","author":[{"given":"Matthias","family":"Jakobs","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Liebig","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,6]]},"reference":[{"key":"7020_CR1","doi-asserted-by":"publisher","first-page":"22300","DOI":"10.52202\/068431-1620","volume":"35","author":"IM Alabdulmohsin","year":"2022","unstructured":"Alabdulmohsin, I. 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