Forecasting Temperature Time Series Data Using Combined Statistical and Deep Learning Methods: A Case Study of Nairobi County Daily Temperature

John Kamwele Mutinda, Amos Kipkorir Langat and Samuel Musili Mwalili

International Journal of Mathematics and Mathematical Sciences, 2025, vol. 2025, 1-20

Abstract: Accurate temperature forecasting is of paramount importance across various sectors, influencing decision-making processes and impacting numerous aspects of daily life. This study analyzes temperature time series data from the Nairobi County in Kenya, aiming to develop accurate hybrid time series forecasting models. Initial statistical tests revealed significant nonstationarity and nonlinearity in the data, prompting the adoption of specialized modeling techniques. Using variational mode decomposition (VMD), the raw time series was decomposed into interpretable components, enhancing feature representation and understanding of temperature dynamics. Hybrid forecasting models were then constructed by integrating VMD with both statistical (autoregressive integrated moving average [ARIMA]) and deep learning (gated recurrent unit [GRU], long short-term memory [LSTM], and Transformer) architectures. Evaluation metrics, including root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R-squared, highlighted the superiority of hybrid models over individual approaches, particularly those combining VMD with ARIMA, GRU, LSTM, and Transformer. The experimental results for temperature prediction show that the hybrid models combining VMD with statistical and deep learning networks achieved improved forecasting accuracy compared with baseline models. Specifically, the VMD–ARIMA–GRU model emerged as the top performer, demonstrating the lowest error metrics and highest explanatory power. With a low RMSE of 0.710090, MAE of 0.561726, and MAPE of 2.808193%, the model demonstrates remarkable accuracy in predicting temperature values. In addition, the high R-squared value of 0.779234 indicates that approximately 77.92% of the variance in the observed data is explained by the model, showcasing its robustness and effectiveness in capturing the underlying patterns in temperature time series data. Overall, this study underscores the importance of VMD in preprocessing data to enhance feature representation and forecasting accuracy. By combining statistical and deep learning methods, hybrid models incorporating VMD offer a comprehensive solution for accurate temperature prediction, with implications for climate modeling and environmental monitoring.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jijmms:4795841

DOI: 10.1155/ijmm/4795841

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