Single State Update Predictive Coding training for Time Series Forecasting and Anomaly Detection

2026-08-25

AI Summary

为解决预测编码网络的反向误差传播瓶颈，提出一种并行训练技术，通过匹配生成与编码PCN的神经激活来进行时间序列异常检测。

Abstract

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.

Problem

• Predictive Coding
• Time Series Forecasting
• Anomaly Detection

Innovation

• Single State Update
• Predictive Coding
• parallel training
• time series forecasting
• anomaly detection