Probabilistic Decomposition Transformer for Time Series Forecasting
This work addresses time series forecasting challenges for applications like disaster warning and energy consumption, presenting an incremental improvement by integrating existing techniques.
The paper tackles the problem of complex temporal patterns and cumulative errors in Transformer-based time series forecasting by proposing a probabilistic decomposition Transformer that combines Transformer with a conditional generative model, achieving hierarchical and interpretable probabilistic forecasts with results showing effectiveness and robustness comparable to state-of-the-art methods.
Time series forecasting is crucial for many fields, such as disaster warning, weather prediction, and energy consumption. The Transformer-based models are considered to have revolutionized the field of sequence modeling. However, the complex temporal patterns of the time series hinder the model from mining reliable temporal dependencies. Furthermore, the autoregressive form of the Transformer introduces cumulative errors in the inference step. In this paper, we propose the probabilistic decomposition Transformer model that combines the Transformer with a conditional generative model, which provides hierarchical and interpretable probabilistic forecasts for intricate time series. The Transformer is employed to learn temporal patterns and implement primary probabilistic forecasts, while the conditional generative model is used to achieve non-autoregressive hierarchical probabilistic forecasts by introducing latent space feature representations. In addition, the conditional generative model reconstructs typical features of the series, such as seasonality and trend terms, from probability distributions in the latent space to enable complex pattern separation and provide interpretable forecasts. Extensive experiments on several datasets demonstrate the effectiveness and robustness of the proposed model, indicating that it compares favorably with the state of the art.