Selecting the independent variables is first. These should have both numerical features (such that price, quantity) as well as categorical ones (seasonal indicator). This can refer to either a month or a quarter, depending on the type of seasonality being measured. Let’s assume, in this instance, that we want to look at the quarterly seasonality of the past three years.
Total sales for every quarter of each year will be the dependent variable. Then, using the two variables as inputs to a regression analysis model is required in order to capture all of the seasonal variations. To identify outliers and anomalies, an exponential line could be fitted to the data to find them.
We can then compare our projected quarterly results to actual quarter results in order assess the accuracy of predictions. Additional factors like market conditions and external influences can be used to further refine the forecast if necessary.
The building of a regression model with categorical factors such as seasonal indicators gives companies valuable insight into the expected performance in future sales, based on previous trends. This allows them to make adjustments accordingly.
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