What techniques do you employ to account for and adjust seasonality in time series data analysis?

This question requires you to explain the methods you use to account for seasonality when analyzing time series data. Seasonality is a repeating pattern within a fixed time frame in a time series data and it can distort the long-term trend. Therefore, recognizing and adjusting for seasonal patterns is an integral part of time series analysis. In your response, you should describe the specific techniques or tools you use to identify, measure, and account for such patterns. Important points to consider include the statistical methods, software tools, or computational procedures you might use, and why you chose these methods.

I typically employ several techniques to account for seasonality in time series data. First, seasonal decomposition (STL) can be used to separate a time series into three components: trend, seasonality, and residuals. This method leverages loess smoothing to make it robust to outliers. Second, seasonal ARIMA (SARIMA) models are also very useful for handling the seasonal nature of a data series. The parameters in SARIMA models allow for trend and seasonal components to be modelled separately. For both methods, I prefer using the programming language R considering the availability of packages like ‘forecast’ that simplifies the implementation of these methods.

To account for seasonality in time series data, one of the most common methods I use is the method of differencing. We can remove the seasonal component by subtracting the data point that occurred exactly one season ago. This works by computing the difference between an observation and the corresponding observation from the previous season, which by definition is the seasonal period. However, in cases with complex seasonal patterns, I would employ Fourier series to model the seasonality, which allows for flexible models that can capture the seasonal pattern better. For computation and visualisation of the results, I use Python’s pandas and matplotlib libraries, which offer robust functions for time series analysis.