Abstract
The article presents a methodology for solving forecasting problems based on machine learning methods. The technique consists of the following stages - data collection stage; research stage and data preparation; model training stage; the stage of determining the effectiveness of the model; stage of improving the efficiency of the model; visualization. The methodology is designed to systematically solve forecasting problems using various machine learning methods. Using the methodology as an example, the solution of the problem of forecasting the production of electricity for combined power plants using regression, tree-based models, boosting, bagging and neural networks is considered. A feature of the technique is the definition and study of the effectiveness of machine learning models and their improvement. The efficiency of predictive models of various types is analyzed. Various quality indicators were used to assess the quality of forecasts. Boosting is the best predictive model. The results obtained indicate the good quality of the predicted values using the proposed methodology.
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Kalinina, I., Gozhyj, A. (2023). Methodology for Solving Forecasting Problems Based on Machine Learning Methods. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making. ISDMCI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 149. Springer, Cham. https://doi.org/10.1007/978-3-031-16203-9_7
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