Temperature Prediction Using Transformer–LSTM Deep Learning Models and Sarimax from a Signal Processing Perspective

Recent developments in machine learning (ML), deep learning (DL), and statistical signal processing have led to substantial improvements in the accuracy of time series forecasting, particularly for environmental parameters such as temperature. The accuracy of air temperature prediction is not only vital for meteorological forecasting but also critically impacts agriculture, energy management, and environmental monitoring. In this study, a comprehensive modeling approach is proposed by incorporating both data-driven learning methods and classical signal processing techniques. Specifically, statistical models such as Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors (SARIMAX) are evaluated alongside modern neural network architectures, including Long Short-Term Memory (LSTM) networks and Transformer-based attention mechanisms. The implemented models utilize key atmospheric variables—humidity, pressure, and past temperature values—to predict ambient temperature for future time horizons, such as one week and six months ahead. The SARIMAX model, which is grounded in digital signal processing theory, is particularly examined for its ability to capture seasonality and trend components in structured data. Meanwhile, deep learning models excel in learning complex, nonlinear temporal dependencies. Experimental results show that while LSTM performs well in short-term predictions (mean absolute error (MAE): 2.27, mean squared error (MSE): 6.63), the attention-based Transformer model is superior in capturing the predictions in the long term (MAE: 2.99, MSE: 14.92). SARIMAX, on the other hand, demonstrates a reliable performance in the short term compared to LSTM. These findings provide valuable insights into the strengths and limitations of each modeling approach, guiding future efforts in temperature forecasting and time series analysis.