通过并行训练双编码器，实现时间序列的稳定在线学习与异常检测。
Single State Update Predictive Coding training for Time Series Forecasting and Anomaly Detection
- 用生成式与编码器双预测编码网络并行训练，避免反向传播依赖
- 在真实时间序列上实现持续在线学习，稳定性显著提升
- 适合需要实时更新的工业异常检测场景
预测编码（Predictive Coding, PC）是一种可并行更新神经网络层的学习范式。然而，其主要瓶颈在于反向误差传播的串行性。为解决该问题，本文提出一种新训练方法：将生成式预测编码网络（Generative PCN）与支持编码器预测编码网络（support Encoding PCN）配对，两者并行训练以匹配神经激活，无需串行传播。该方法应用于时间序列异常检测，结果表明，所提方法实现了更稳定、连续的在线学习。
原文摘要 · Abstract (English)
Predictive Coding (PC) is a neural learning paradigm that enables parallelizable neural network layer updates. However, the main bottleneck of PC Networks (PCN) is the sequential backwards error propagation. To tackle this, we introduce a training technique that pairs a Generative PCN with a support Encoding PCN. The two PCNs are trained in parallel to match their neural activations, without sequential propagation. We apply this to time series anomaly detection and show that our approach results in more stable, continuous, online learning.
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