A Time and Frequency Domain Based Dual-Attention Neural Network for Tropical Cyclone Track Prediction

Due to the influence of various dynamic meteorological factors, accurate Tropical Cyclone (TC) track prediction is a significant challenge. However, current deep learning based time series prediction models fail to simultaneously capture both short-term and long-term dependencies, while also neglecting the change in meteorological environment pattern associated with TC motion. This limitation becomes particularly pronounced during sudden turning in the TC track, resulting in significant deterioration of prediction accuracy. To overcome these limitations, we propose LFInformer, a hybrid deep learning framework that integrates an Informer backbone, a Frequency-Enhanced Channel Attention Mechanism (FECAM), and a Long Short-Term Memory (LSTM) network for TC track prediction. The Informer backbone is underpinned by ProbSparse Self-Attention in both the encoder and the causally masked decoder, prioritizing the most informative query–key interactions to deliver robust long-range modeling and sharper detection of turning signals. FECAM enhances meteorological inputs via discrete cosine transforms, band-wise weighting, and channel-wise reweighting, then projects the enhanced signals back into the time domain to produce frequency-aware representations. The LSTM branch captures short-term variations and localized temporal dynamics through its recurrent structure. Together, these components sustain high accuracy during both steady evolution and sudden turnin. Experiments based on the JMA and IBTrACS 1951–2022 Northwest Pacific TC data show that the proposed model achieves an average absolute position error (APE) of 72.39 km, 117.72 km, 145.31 km and 168.64 km for the 6-h, 12-h, 24-h and 48-h forecasting tasks, respectively. The proposed model enhances the accuracy of TC track predictions, offering an innovative approach that optimally balances precision and efficiency in forecasting sudden turning points.