Robust Parameter-Free Season Length Detection in Time Series
This addresses a practical bottleneck for time series analysis where existing methods often require manual parameter tuning, though it appears incremental relative to prior work.
The paper tackles the problem of automatically detecting season length in time series without requiring user-defined parameters, presenting an algorithm that interpolates, filters, detrends data and analyzes autocorrelation function zeros. The result shows it outperformed a comparable algorithm by passing 122 out of 165 tests versus 83 tests.
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.