提出可识别的时序因子模型，解决多变量时间序列因子难定位问题。
Conditionally Identifiable Latent Representation for Multivariate Time Series with Structural Dynamics
- 通过条件化动态创新过程实现因子可识别性
- 在合成数据上因子恢复更准确，真实数据预测表现优
- 适合需要解释性因子分析的工业时序建模场景
我们提出可识别变分动态因子模型（iVDFM），从多变量时间序列中学习具有可识别保证的潜在因子。通过将iVAE风格的条件化应用于驱动动态的创新过程，而非潜在状态，我们证明因子在排列及分量仿射（或单调可逆）变换下可识别。线性对角动态保持该可识别性，并可通过伴随矩阵和克雷洛夫方法实现可扩展计算。我们在合成数据上验证了更优的因子恢复效果，在合成结构因果模型（SCMs）上实现了稳定的干预预测准确率，并在真实世界基准上达到竞争性概率预测性能。
原文摘要 · Abstract (English)
We propose the Identifiable Variational Dynamic Factor Model (iVDFM), which learns latent factors from multivariate time series with identifiability guarantees. By applying iVAE-style conditioning to the innovation process driving the dynamics rather than to the latent states, we show that factors are identifiable up to permutation and component-wise affine (or monotone invertible) transformations. Linear diagonal dynamics preserve this identifiability and admit scalable computation via companion-matrix and Krylov methods. We demonstrate improved factor recovery on synthetic data, stable intervention accuracy on synthetic SCMs, and competitive probabilistic forecasting on real-world benchmarks.
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