pTSE: A Multi-model Ensemble Method for Probabilistic Time Series Forecasting

Autor:	Zhou, Yunyi, Chu, Zhixuan, Ruan, Yijia, Jin, Ge, Huang, Yuchen, Li, Sheng
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Various probabilistic time series forecasting models have sprung up and shown remarkably good performance. However, the choice of model highly relies on the characteristics of the input time series and the fixed distribution that the model is based on. Due to the fact that the probability distributions cannot be averaged over different models straightforwardly, the current time series model ensemble methods cannot be directly applied to improve the robustness and accuracy of forecasting. To address this issue, we propose pTSE, a multi-model distribution ensemble method for probabilistic forecasting based on Hidden Markov Model (HMM). pTSE only takes off-the-shelf outputs from member models without requiring further information about each model. Besides, we provide a complete theoretical analysis of pTSE to prove that the empirical distribution of time series subject to an HMM will converge to the stationary distribution almost surely. Experiments on benchmarks show the superiority of pTSE overall member models and competitive ensemble methods. Comment: The 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2305.11304 Zobrazit plný text záznamu View this record from Arxiv