Single State Update Predictive Coding training for Time Series Forecasting and Anomaly Detection
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Computer Science > Machine Learning
Title:Single State Update Predictive Coding training for Time Series Forecasting and Anomaly Detection
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.
| Subjects: | Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE) |
| Cite as: | arXiv:2608.24697 [cs.LG] |
| (or arXiv:2608.24697v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.24697
arXiv-issued DOI via DataCite (pending registration)
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