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

Abstract

Accurate short-term forecasting of meteorological variables remains a major challenge in tropical regions characterized by strong diurnal variability, seasonal transitions, intermittent atmospheric fluctuations, and highly non-stationary temporal dynamics. This study proposes a hybrid deep-learning framework combining the Stationary Wavelet Transform (SWT), Multi-Head Attention (MHA) and Long Short-Term Memory (LSTM) networks’ forecasting layers for multivariate meteorological prediction over selected locations in Togo. The proposed architecture simultaneously forecasts temperature at 2 m, relative humidity, and wind speed using hourly ERA5 atmospheric variables enriched with lagged, rolling-statistical, and cyclic temporal features. SWT decomposition extracts multi-scale atmospheric structures while preserving temporal alignment, the attention mechanism dynamically emphasizes informative temporal sub-sequences, and the LSTM layers improve medium-range temporal dependency modeling. The forecasting task was reformulated under a strictly causal prediction protocol for 24 h and 72 h forecasting horizons. The proposed framework achieved competitive forecasting performance with MAE = 2.99, RMSE = 5.22, and R2=0.77 for the 24 h horizon, and MAE = 3.44, RMSE = 6.11, and R2=0.73 for the 72 h horizon. Temperature forecasting exhibited the highest stability, whereas wind-speed prediction remained more challenging because of stronger atmospheric intermittency. The results demonstrate the relevance of multi-scale temporal representation learning for ERA5-based tropical meteorological forecasting while highlighting important methodological and operational limitations.