  The in-depth analysis of time series has gained a lot of research interest in
recent years, with the identification of periodic patterns being one important
aspect. Many of the methods for identifying periodic patterns require time
series' season length as input parameter. There exist only a few algorithms for
automatic season length approximation. Many of these rely on simplifications
such as data discretization and user defined parameters. This paper presents an
algorithm for season length detection that is designed to be sufficiently
reliable to be used in practical applications and does not require any input
other than the time series to be analyzed. The algorithm estimates a time
series' season length by interpolating, filtering and detrending the data. This
is followed by analyzing the distances between zeros in the directly
corresponding autocorrelation function. Our algorithm was tested against a
comparable algorithm and outperformed it by passing 122 out of 165 tests, while
the existing algorithm passed 83 tests. The robustness of our method can be
jointly attributed to both the algorithmic approach and also to design
decisions taken at the implementational level.
