<ul><li>Wind direction forecasting plays a crucial role in optimizing wind energy production.</li><li>A novel model, WaveHiTS, has been developed to improve wind direction forecasting using wavelet transform and Neural Hierarchical Interpolation for Time Series (Hits).</li><li>Experiments conducted on real-world meteorological data show that WaveHiTS outperforms other deep learning models, transformer-based approaches, and hybrid models.</li><li>The proposed model achieves consistent accuracy for wind direction forecasting up to 60 minutes ahead, with significant improvements in RMSE values, vector correlation coefficients, and hit rates.</li></ul>

WaveHiTS: Wavelet-Enhanced Hierarchical Time Series Modeling for Wind Direction Nowcasting in Eastern Inner Mongolia

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