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Yunyi Zhou

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3

ECAI Conference 2024 Conference Paper

VMFTransformer: An Angle-Preserving and Auto-Scaling Machine for Multi-Horizon Probabilistic Forecasting

  • Yunyi Zhou
  • Ruohan Gao
  • Xinping Zheng
  • Yuchen Huang
  • Zhixuan Chu

As deep learning develops, the major research methodologies of time series forecasting can be divided into two categories, i. e. , iterative and direct methods. In the iterative methods, since a small amount of error is produced at each time step, the recursive structure can potentially lead to large error accumulations over longer forecasting horizons. Although the direct methods can avoid this puzzle involved in the iterative methods, they face abuse of conditional independence among time points. This impractical assumption can also lead to biased models. To solve these challenges, we propose a direct approach for multi-horizon probabilistic forecasting, which can effectively characterize the dependence across future horizons. Specifically, we consider the multi-horizon target as a random vector. The direction of the vector embodies the temporal dependence, and the length of the vector measures the overall scale across each horizon. Therefore, we respectively apply the von Mises-Fisher (VMF) distribution and the truncated normal distribution to characterize the target vector’s angle and magnitude in our model. Extensive results demonstrate the superiority of our framework over eight state-of-the-art methods.

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IJCAI Conference 2023 Conference Paper

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

  • Yunyi Zhou
  • Zhixuan Chu
  • Yijia Ruan
  • Ge Jin
  • Yuchen Huang
  • Sheng Li

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 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 over all member models and competitive ensemble methods.

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