Wavelet-Enhanced Deep Learning for Multi-Variable Meteorological Time-Series Forecasting in Togo

Accurate short-term forecasting of key meteorological variables—air temperature, relative humidity, and wind speed—remains challenging in tropical and sub-Saharan regions due to strong diurnal cycles, seasonal variability, and non-stationary dynamics. To address these limitations, this study proposes a hybrid deep learning model combining Stationary Wavelet Transform (SWT), Multi-Head Attention (MHA), and LSTM networks. First, SWT decomposes meteorological time series into multi-scale components, capturing both low-frequency trends and high-frequency fluctuations while preserving temporal resolution. Then, the attention mechanism dynamically weights the importance of these multi-scale features across time, enhancing the model’s ability to focus on the most relevant patterns and interactions. Finally, LSTM layers model long-term dependencies and nonlinear temporal structures to generate multi-output predictions. The model is trained on hourly data enriched with lagged and statistical features. Experimental results show strong predictive performance (MAE = 1.21, RMSE = 2.01, R² = 94%), with notable improvements in modeling rapid variations, especially for wind speed and humidity. This work represents one of the first integrations of SWT, attention mechanisms, and LSTM for multi-variable forecasting in tropical climates, with practical applications in energy forecasting and precision agriculture.