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Abstract:
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Seasonality is among the most visible properties in time series data, yet a multitude of statistical tests devised over decades of research have only achieved limited success in its detection. In this paper we examine eight existing tests of seasonality and show that there is significant variation in how they classify a series. We then show how this variation, combined with characteristics of the time series (e.g. auto-correlation, frequency, skewness, kurtosis, etc.), can be exploited by a Random Forest (RF) framework to map the hypothesis test space and make more accurate predictions regarding the seasonal disposition of a series. Our proposed method reduces Type II errors by approximately sixty percentage points over the next best alternative.
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