Adaptive Meta-Learning Probabilistic Inference Framework for Long Sequence Prediction
DOI:
https://doi.org/10.1609/aaai.v38i15.29661Keywords:
ML: Transfer, Domain Adaptation, Multi-Task Learning, DMKM: Mining of Spatial, Temporal or Spatio-Temporal Data, ML: Representation LearningAbstract
Long sequence prediction has broad and significant application value in fields such as finance, wind power, and weather. However, the complex long-term dependencies of long sequence data and the potential domain shift problems limit the effectiveness of traditional models in practical scenarios. To this end, we propose an Adaptive Meta-Learning Probabilistic Inference Framework (AMPIF) based on sequence decomposition, which can effectively enhance the long sequence prediction ability of various basic models. Specifically, first, we decouple complex sequences into seasonal and trend components through a frequency domain decomposition module. Then, we design an adaptive meta-learning task construction strategy, which divides the seasonal and trend components into different tasks through a clustering-matching approach. Finally, we design a dual-stream amortized network (ST-DAN) to capture shared information between seasonal-trend tasks and use the support set to generate task-specific parameters for rapid generalization learning on the query set. We conducted extensive experiments on six datasets, including wind power and finance scenarios, and the results show that our method significantly outperforms baseline methods in prediction accuracy, interpretability, and algorithm stability and can effectively enhance the long sequence prediction capabilities of base models. The source code is publicly available at https://github.com/Zhu-JP/AMPIF.Downloads
Published
2024-03-24
How to Cite
Zhu, J., Guo, X., Chen, Y., Yang, Y., Li, W., Jin, B., & Wu, F. (2024). Adaptive Meta-Learning Probabilistic Inference Framework for Long Sequence Prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 38(15), 17159-17166. https://doi.org/10.1609/aaai.v38i15.29661
Issue
Section
AAAI Technical Track on Machine Learning VI