multiple seasonality Time series analysis in Python

Question

I have a daily time series dataset that I am using Python SARIMAX method to predict for future. But I do not know how to write codes in python that accounts for multiple seasonalities. As far as I know, SARIMAX takes care of only one seasonality but I want to check for weekly, monthly, and quarterly seasonalities. I know to capture day of the week seasonality, I should create 6 dummy variables, To capture day of the month seasonality, create 30 dummy variables, and To capture month of the year, create 11 dummy variables. But I don't know how to incorporate it with the main SARIMAX function in Python? I mean SARIMAX is just a function that does the autoregressive, moving average and the differencing parts but how should I include multiple seasonality codes in my time series analysis with SARIMAX? So far, I know how to create dummy variables for each category but don't know how to replicate it to the entire dataset? After that I don't know how to write Python codes that do SARIMAX and captures multiple seasonalities at the same time.

I am in need of help for Python code that can do it.

Please advise accordingly

Regards

Answer 1

Yes, SARIMA model is designed for dealing with a single seasonality.

• To make it work for multiple seasonality, it is possible to apply a method called Fourier terms.

• Secondly, there is a better method for time series data with multiple seasonality effects which is called TBABS. Here is an example that includes codes and explanation for both approaches: https://medium.com/intive-developers/forecasting-time-series-with-multiple-seasonalities-using-tbats-in-python-398a00ac0e8a

• Thirdly, you can check https://facebook.github.io/prophet/ which also brings up an easier way to this issue.

• For a deeper research, you can always google terms "time series" with "multiple seasonality" or "multiple seasonal effect"