A nature-inspired and noise-assisted feature extraction integrating spatiotemporal attention-based sequence2sequence for multivariate wind speed prediction - Sibtain, Muhammad; Li, Xianshan; Saleem, Snoober; Qurat-ul-Ain; Shi, Qiang; Li, Fei; Apaydin, Halit (Stochastic environmental research and risk assessment, 2025)
An efficient and reliable multistage feature extraction facilitates the selection of useful features from multivariate time series data, resulting in the enhanced predicting ability of prediction models. For this reason, this study proposes a hybrid prediction model (HPM), namely, CA-HHO-ICEEMDAN-STA-Seq2Seq based on multistage feature extraction, to enhance the prediction outcomes of multivariate wind speed prediction (MWSP). The proposed HPM integrates correlation analysis (CA), Harris Hawks Optimizer (HHO), improved complete ensemble empirical mode decomposition with additive noise (ICEEMDAN), and spatiotemporal attention (STA) based sequence2sequence (Seq2Seq). First, the CA selects variables demonstrating a significant correlation with the wind speed data that undergo further selection through HHO. Afterward, noise-assisted ICEEMDAN decomposes the variables selected through HHO into subcomponents. The subsequent stage extracts spatial and temporal features through STA. The last stage employs Seq2Seq to finalize the prediction task. The superior prediction outcomes of the proposed model over eleven other counterpart models using different plots and performance metrics substantiate its viability for MWSP. The CA-HHO-ICEEMDAN-STA-Seq2Seq model reduced MAE by 0.674 m/s, 0.648 m/s, 0.618 m/s, 0.593 m/s, 0.297 m/s, 0.269 m/s, 0.168 m/s, 0.094 m/s, 0.089 m/s, 0.086 m/s, and 0.017 m/s compared with the MLR, SVM, ANN, EGB, GRU, LSTM, Seq2Seq, SA-Seq2Seq, TA-Seq2Seq, STA-Seq2Seq, and CA-ICEEMDAN-STA-Seq2Seq models, respectively. Similarly, in terms of NSE, the CA-HHO-ICEEMDAN-STA-Seq2Seq model was 0.340%, 0.332%, 0.308%, 0.287%, 0.124%, 0.116%, 0.064%, 0.032%, 0.035%, 0.027%, and 0.001% more efficient than the MLR, SVM, ANN, EGB, GRU, LSTM, Seq2Seq, SA-Seq2Seq, TA-Seq2Seq, STA-Seq2Seq, and CA-ICEEMDAN-STA-Seq2Seq models. Besides MWSP, the proposed model is feasible for similar tasks, including solar irradiance, hydro, GHG emissions, and load predicting.