The Phenomenon of Temperature Increase in Poland: A Machine Learning Approach to Understanding Patterns and Projections
Source abstract
Abstract
This study presents an analysis of patterns in mean monthly air temperature increases in Poland using the deep learning model Neural Basis Expansion Analysis for Time Series (N-BEATS) algorithm. The dataset comprises mean monthly temperatures recorded between 1951 and 2024 at eight meteorological stations across Poland. The research was conducted in two phases. In the first phase, the 74-year period was divided into two distinct intervals: one characterized by relative temperature stability, and the other by a marked upward trend. In the second phase, the N-BEATS neural network was employed to extract temporal patterns directly from the data and to forecast future temperature values. The results confirm the capacity of machine learning methods to identify persistent climate trends and demonstrate their utility for long-term monitoring and prediction.
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Research context
OpenAlex topics: Energy Load and Power Forecasting · Forecasting Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Geoinformatics and Applied Computer Science, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Kraków, Poland
Faculty of Geography and Geology, Jagiellonian University, ul. Gronostajowa 7, 30-387 Kraków, Poland
References (26)
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