Keyword: «pandas»

The purpose of this article is to find the best model for forecasting time series, taking into account the minimization of errors and high accuracy of the forecast. The method of comparative analysis of the most popular "traditional" econometric models ARIMA and SARIMA is used. Algorithms and models are implemented in the Python programming environment with the connected libraries Sklearn, Pandas, Numpy, and Statsmodels. As input data sets, we imported data on product sales for 5 years in 10 stores. The results of the study confirm the superiority of the SARIMA model, in which the RMSE error is 20% less than when using ARIMA. It is concluded that to improve the quality of the time series forecast, it is preferable to use an algorithm based on the SARIMA model.