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The in-depth analysis of time series has gained a lot of re-search interest in recent years, with the identification of pe-riodic patterns being one important aspect. Many of themethods for identifying periodic patterns require time series’season length as input parameter. There exist only a few al-gorithms for automatic season length approximation. Manyof these rely on simplifications such as data discretization.This paper presents an algorithm for season length detec-tion that is designed to be sufficiently reliable to be used inpractical applications. The algorithm estimates a time series’season length by interpolating, filtering and detrending thedata. This is followed by analyzing the distances betweenzeros in the directly corresponding autocorrelation function.Our algorithm was tested against a comparable algorithmand outperformed it by passing 122 out of 165 tests, whilethe existing algorithm passed 83 tests. The robustness of ourmethod can be jointly attributed to both the algorithmic ap-proach and also to design decisions taken at the implemen-tational level.