研究人员提出了 DecoVAE，一个专门用于时间序列预测的轻量级 VAE 框架。它把时间序列拆成趋势和季节性两部分来处理，在多个基准测试上比其他方法更准，而且模型更小、速度更快。
DecoVAE 是一个轻量级可解释的趋势-季节性 VAE 框架。该模型通过领域特定的归纳偏置，将时间序列显式分解为趋势和季节性成分。在七个真实世界基准测试中，DecoVAE 在短期预测上实现了 14.96% 的 CRPS 和 23.30% 的 NMAE 降低。在长期预测上，其 CRPS 和 NMAE 降低分别高达 52.68% 和 26.51%。同时，该模型在保持高效率方面表现突出，模型权重减少了 93%，速度提升了 74%。
DecoVAE: a Lightweight Interpretable Trend-Seasonal VAE Framework for Efficient Probabilistic Time Series Forecasting
Probabilistic time series forecasting remains challenging, largely because modeling distinct trend and seasonal dynamics requires specialized approaches. Existing methods often fail to capture the unique inner properties of these components, lack interpretability, or suffer from heavy memory and runtime overhead. To address these limitations, we propose DecoVAE, a lightweight interpretable trend-seasonal VAE framework that explicitly decomposes time series into trend and seasonal components by applying domain-specific inductive biases. The trend stream enforces structural smoothness using a differential regularizer on the latent trajectory, analogous to the Hodrick-Prescott filter. Concurrently, the seasonal stream operates in the frequency domain via a complex Gaussian VAE, natively capturing the amplitude and phase of periodic patterns. Extensive evaluations across seven real-world benchmarks show that DecoVAE consistently outperforms strong baselines. It achieves reductions of up to 14.96\% in CRPS and 23.30\% in NMAE for short-term forecasting, and up to 52.68\% and 26.51\% for long-term horizons. Crucially, DecoVAE yields these accuracy gains while remaining highly efficient, reducing model weight by up to 93\% and accelerating speed by up to 74\% compared to the second-best method.