OPTIMIZING TIME SERIES FORECASTING: LEVERAGING MACHINE LEARNING MODELS FOR ENHANCED PREDICTIVE ACCURACY
OPTYMALIZACJA PROGNOZOWANIA SZEREGÓW CZASOWYCH: WYKORZYSTANIE MODELI UCZENIA MASZYNOWEGO W CELU ZWIĘKSZENIA DOKŁADNOŚCI PREDYKCYJNEJ
Wojcik W. Shayakhmetova A. Akhmetova A. Abdildayeva A. Nurtugan G.
2024Politechnika Lubelska
Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Srodowiska
2024#14Issue 4115 - 120 pp.
Engaging in investment activities plays a crucial and strategic role in fostering the growth of businesses and ensuring their resilience in the market. This involvement entails expenditures on acquiring assets, embracing technological advancements, expanding production capacities, conducting research and development, among various other domains. Collectively, these aspects form the foundation for the sustained success of an organization over the long term. This thesis will delve into an exploration of leveraging machine learning techniques to forecast key parameters in business, including investments and their impact on the financial health of the company. In this research, explored a vari ety of time series models and identified that both the Random Forest Regressor and Decision Tree Regressor models deliver superior accuracy, showcasing identical RMSE values of 88.36 on the validation dataset. Furthermore, the Cat Boost and Light GBM models exhibited praiseworthy performance, registering RMSE values of 92.47 and 104.69, respectively. These findings highlight the robust performance of Random Forest Regressor and Decision Tree Regressor, emphasizing their capability to provide accurate predictions. It is noted that Random Forest Regressor and Decision Tree Regressor are distinguished by high accuracy in time series forecasting, and the choice between them should take into account the trade-offs between computational efficiency and interpretability of the model. These results allow us to propose practical strategies for managing investment resources to ensure the sustainable development and prosperity of the enterprise in the long term.
ARIMA , autoregression , Cat Boost Regressor , Decision Tree Regressor , Random Forest Regressor , time series
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Lublin University of Technology, Lublin, Poland
Al-Farabi Kazakh National University, Almaty, Kazakhstan
Lublin University of Technology
Al-Farabi Kazakh National University
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026