GATE: A guided approach for time series ensemble forecasting

In this article, a new ensemble learning model called GATE is proposed to improve the accuracy and stability of time-series forecasting, which is a crucial aspect of modern engineering practices. Despite the promise of deep learning (DL) models in this area, their performance can be volatile due to the diversity of time series data. To address this, the GATE model combines the strengths of recurrent neural networks (RNN), long short-term memory network (LSTM), and convolution-LSTM (Conv-LSTM) structures and utilizes an unsupervised learning strategy to steer the ensemble output using a guided network. To prevent overfitting in DL models, GATE optimizes the sample loss function and the weight updating function for each individual model within the ensemble structure. A comprehensive evaluation of the proposed GATE method on four real-world datasets is presented. The experimental results unequivocally demonstrate that GATE surpasses state-of-the-art ensemble methods and individual models, exhibiting the best performance in terms of testing errors. Notably, GATE outperforms existing single models in addressing long-term prediction tasks. To validate the effectiveness of GATE, ablation studies is carried out, comparing different ensemble combinations involving two distinct models. Through systematic analysis, these studies provided valuable insights into the performance of various ensemble configurations, further confirming the effectiveness and superiority of the proposed GATE method.