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Store Demand Forecast
course content

Course Content

Time Series Analysis

Store Demand ForecastStore Demand Forecast

As in the previous problem, slightly different models are currently used to forecast demand, which is more complicated than the usual ARIMA. Which, for example? SARIMAX.

This model is very similar to the ARIMA model, except that there is an additional set of autoregressive and moving average components.

The SARIMA model allows data to be distinguished by seasonal frequency as well as other non-seasonal differences. Knowing which options are the best can be made easier with automatic option search frameworks like pmdarina.

You can use SARIMA with statsmodels:

The moving average can also be used to predict demand. However, the results we can get using this method can surpass even XGBoost (reduces the error by 32%). But what should we expect from such a simple method?

In any case, your main task in time series prediction is the optimal choice of the model size (its computational performance) and the results it can bring.


What is the most important data you would collect to create a demand forecasting dataset for an online store?

Select the correct answer

Everything was clear?

Section 6. Chapter 2