A filter based fisher g-test approach for periodicity detection in time series analysis

Periodicity is an interesting property of many time series data sets. A period can be defined as a self repeating pattern. This pattern provides useful information about the inherent structure in cyclic data set. In this paper, a filter based Fisher g-test approach is introduced. The filtering approach is based on the singular spectrum analysis. The power and running time of the proposed filter based approach are compared with non robust approaches. To evaluate the performance of the proposed approach we have performed a comprehensive simulation study. The results confirm the superiority of the proposed approach, considering various criteria which is insensitive to heavy contamination of outliers and short time series.