Siyue Yang

Qi Sima, Xinze Zhang, Yukun Bao et al.

Sequence-to-sequence architectures built upon recurrent neural networks have become a standard choice for multi-step-ahead time series prediction. In these models, the decoder produces future values conditioned on contextual inputs, typically either actual historical observations (ground truth) or previously generated predictions. During training, feeding ground-truth values helps stabilize learning but creates a mismatch between training and inference conditions, known as exposure bias, since such true values are inaccessible during real-world deployment. On the other hand, using the model's own outputs as inputs at test time often causes errors to compound rapidly across prediction steps. To mitigate these limitations, we introduce a new training paradigm grounded in reinforcement learning: a policy gradient-based method to learn an adaptive input selection strategy for sequence-to-sequence prediction models. Auxiliary models first synthesize plausible input candidates for the decoder, and a trainable policy network optimized via policy gradients dynamically chooses the most beneficial inputs to maximize long-term prediction performance. Empirical evaluations on diverse time series datasets confirm that our approach enhances both accuracy and stability in multi-step forecasting compared to conventional methods.

Siyue Yang, Yukun Bao

Epidemics of influenza are major public health concerns. Since influenza prediction always relies on the weekly clinical or laboratory surveillance data, typically the weekly Influenza-like illness (ILI) rate series, accurate multi-step-ahead influenza predictions using ILI series is of great importance, especially, to the potential coming influenza outbreaks. This study proposes Comprehensive Learning Particle Swarm Optimization based Machine Learning (CLPSO-ML) framework incorporating support vector regression (SVR) and multilayer perceptron (MLP) for multi-step-ahead influenza prediction. A comprehensive examination and comparison of the performance and potential of three commonly used multi-step-ahead prediction modeling strategies, including iterated strategy, direct strategy and multiple-input multiple-output (MIMO) strategy, was conducted using the weekly ILI rate series from both the Southern and Northern China. The results show that: (1) The MIMO strategy achieves the best multi-step-ahead prediction, and is potentially more adaptive for longer horizon; (2) The iterated strategy demonstrates special potentials for deriving the least time difference between the occurrence of the predicted peak value and the true peak value of an influenza outbreak; (3) For ILI in the Northern China, SVR model implemented with MIMO strategy performs best, and SVR with iterated strategy also shows remarkable performance especially during outbreak periods; while for ILI in the Southern China, both SVR and MLP models with MIMO strategy have competitive prediction performance