提出可抗噪的时序生成模型，提升复杂时间序列建模能力
Approximate Probabilistic Inference for Time-Series Data A Robust Latent Gaussian Model With Temporal Awareness
- 基于深层潜变量高斯架构，引入时序感知机制捕捉动态关系
- 在含噪声和错误数据下仍保持良好重构与生成性能
- 适合处理非平稳、多变的工业或金融时间序列数据
针对高度变化且非平稳的时间序列数据，构建稳健的生成模型是一项复杂而关键的任务。传统方法如长短期记忆网络（LSTM）效率低且泛化能力差，难以捕捉复杂的时序依赖。本文提出一种概率生成模型——时序深层潜变量高斯模型（tDLGM），其架构受深层潜变量高斯模型（DLGM）启发。通过最小化负对数损失函数进行训练，模型具备对数据趋势的正则化能力，从而增强鲁棒性。实验表明，tDLGM能有效重建与生成复杂时间序列数据，并在存在噪声和异常数据时表现稳定。
原文摘要 · Abstract (English)
The development of robust generative models for highly varied non-stationary time series data is a complex yet important problem. Traditional models for time series data prediction, such as Long Short-Term Memory (LSTM), are inefficient and generalize poorly as they cannot capture complex temporal relationships. In this paper, we present a probabilistic generative model that can be trained to capture temporal information, and that is robust to data errors. We call it Time Deep Latent Gaussian Model (tDLGM). Its novel architecture is inspired by Deep Latent Gaussian Model (DLGM). Our model is trained to minimize a loss function based on the negative log loss. One contributing factor to Time Deep Latent Gaussian Model (tDLGM) robustness is our regularizer, which accounts for data trends. Experiments conducted show that tDLGM is able to reconstruct and generate complex time series data, and that it is robust against to noise and faulty data.
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