Detecting Seasonality Through Autocorrelation

Seasonality is a common phenomenon where recurring patterns occur in time series data over a certain period. It often repeats at fixed intervals, with the trend changing with the changing season. Detecting seasonality becomes essential as it provides a deeper understanding of market dynamics and equips traders and investors with the knowledge to make informed decisions, manage risks, and optimize their trading strategies. Autoencoders are majorly used for this purpose because they can learn meaningful data representations in a lower-dimensional space. Autocorrelation is a fundamental concept in time series analysis that identifies the relationship between a data point and its past values in an identical time series. It allows us to identify whether a pattern repeats regularly in seasonality and its timing and magnitude. Seasonal decomposition is a method used to separate a time series into its components, such as trend, seasonality, and residual (or noise). The Autocorrelation Function (ACF) measures the relationship between a data point and its past values within a time series, while Partial Autocorrelation Function (PACF) measures the correlation between a data point and its past values, considering the impact of intermediate values. Dealing with seasonality in time series data is crucial for accurate modeling and forecasting, and three common strategies are Seasonal Differencing, Seasonal Decomposition, and Seasonal ARIMA Models.