Temperature Forecasting with LSTM: A Case Study on Kyiv Weather Data

Résumé fourni par la source

Accurate temperature forecasting is essential for urban planning, energy management, and environmental monitoring in smart cities. This study evaluates the use of long short-term memory (LSTM) networks for weekly average temperature (TAVG) prediction in Kyiv, using ARIMA as a baseline model. Historical temperature time series from 1960 onward were employed, and multiple look-back windows were tested to capture seasonal and long-term dependencies. Forecast performance was assessed using RMSE and MAE metrics, showing that LSTM provides more accurate predictions, while ARIMA effectively captures trends and seasonal components. Forecast errors were further analyzed via normal distribution fitting to compare model characteristics. The study emphasizes the importance of rigorous model comparison, including alternatives such as Prophet, and highlights opportunities for long-term analysis to investigate climate trends, global warming effects, or anomalies linked to astronomical, climatic, or anthropogenic factors. These findings demonstrate the potential of deep learning approaches to support data-driven decision- making and sustainable urban management.

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