Combining Machine Learning with Seasonal-Trend Decomposition using LOESS in Power BI

Yanka Aleksandrova () and Mihail Radev ()
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Yanka Aleksandrova: University of Economics - Varna, Varna, Bulgaria
Mihail Radev: University of Economics - Varna, Varna, Bulgaria

Izvestia Journal of the Union of Scientists - Varna. Economic Sciences Series, 2024, issue 1, 81-89

Abstract: Time series analysis has been extensively used for forecasting in various industries. A method frequently used for decomposition of time series is Seasonal-Trend decomposition using LOESS (STL). In combination with the machine learning approaches, STL is a helpful method to analyze the seasonal-trend structure of complicated time series. This hybrid approach helps interpret seasonality, trends, and other residual patterns better than when using only predictive machine learning models. The explanation and interpretation of the models can be effectively implemented in the context of Business Intelligence and analytical platforms. In the current paper, a practical approach involving the integration of STL with Random Forest regressor in Power BI has been proposed. It is evidenced from the results that integration of STL decomposition with machine learning provides a robust analytical tool and this allows user to perform a sophisticated time series forecasts right within the engaged interactive dashboards.

Keywords: seasonal-trend decomposition; STL; machine learning; random forest; forecasting (search for similar items in EconPapers)
JEL-codes: C53 (search for similar items in EconPapers)
Date: 2024
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