WorldTS: マルチモーダル共変量を考慮した時系列予測のための世界モデリング
WorldTS: World Modeling for Multimodal Covariate-aware Time Series Forecasting
マルチモーダルな外部情報を潜在状態の形成と進化に直接組み込む世界モデルベースの時系列予測フレームワークWorldTSを提案し、21の実世界データセットで有効性を示した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
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著者: Yuhan Zhu, Xiangfei Qiu, Hanyin Cheng, Wangmeng Shen, Chenjuan Guo, Bin Yang, Jilin Hu, Christian S. Jensen
分類: cs.LG
原文アブストラクト
Time series forecasting is typically framed as learning a direct mapping from historical to future observations in the observation space. However, sequences of observations generally provide only a partial view of the dynamics of the underlying system, with future observations being shaped by latent dynamics. Recent latent-space forecasting methods thus achieve improved performance by predicting future observations from latent-space representations of historical observations rather than directly forecasting future observations in the observation space. Next, while future observations are also shaped by external factors, how to incorporate external, often multimodal, information into forecasting, so that it can shape latent-state formation and evolution directly, remains underexplored. We propose WorldTS, a world-modeling based forecasting framework that integrates multimodal covariates directly into the forecasting to further improve forecasting performance. Specifically, WorldTS employs a two-stage training strategy. First, it learns forecasting-relevant latent state dynamics conditioned on multimodal covariates, yielding encoded future states. Next, the learned state dynamics are frozen, and an observation decoder is trained to map the predicted future states back to future observations. Extensive experiments on 21 real-world datasets offer insight into WorldTS and its effectiveness.