分解趋势与季节成分，用轻量可解释的框架提升时序预测精度。
DecoVAE: a Lightweight Interpretable Trend-Seasonal VAE Framework for Efficient Probabilistic Time Series Forecasting
- 分离趋势与季节分量，分别用微分正则化和平面高斯变分自编码器建模
- 短程预测CRPS降14.96%，长程预测误差降52.68%，同时模型大小减93%
- 适合需要高效可解释时序建模的工业场景
概率性时序预测仍具挑战，因趋势与季节动态需特殊处理。现有方法常无法捕捉其内在特性，缺乏可解释性或存在高内存与运行开销。为此，我们提出DecoVAE，一种轻量且可解释的趋势-季节变分自编码器框架，通过引入领域特定归纳偏置，显式分解时间序列为趋势与季节成分。趋势分支在潜空间中使用微分正则化强制结构平滑，类比霍德里克-普雷斯科特滤波器；季节分支在频域中采用复高斯变分自编码器，原生捕获周期模式的振幅与相位。在七个真实世界基准上的广泛评估显示，DecoVAE持续优于强基线：短程预测中CRPS降低最多14.96%，NMAE降低23.30%；长程预测中分别降低52.68%与26.51%。关键在于，该框架实现精度提升的同时高度高效，相较次优方法模型权重减少高达93%，推理速度加快74%。
原文摘要 · Abstract (English)
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.
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