How to optimize Long Short Term Memory Model (CNN) for anomaly detection. Any possibility to prepare light weight model ?
Please check the following:
Article Mixed convolutional and long short-term memory network for t...
Conference Paper Long Short Term Memory Networks for Anomaly Detection in Time Series
https://scholarworks.rit.edu/cgi/viewcontent.cgi?article=10451&context=theses
Hi,
I suggest you to see links and attached files un topic.
https://www.sciencedirect.com/science/article/abs/pii/S1568494620300569?dgcid=rss_sd_all
https://ieeexplore.ieee.org/document/8416441
https://arxiv.org/abs/1811.08055
Best regards
Hi,
You would need to perform hyper-parameter tuning of the model to get good results hopefully. I also believe it is vital to know very well the nature of your data (is it highly imbalanced?). If so, I would suggest using some oversampling techniques to see how it works, as we cannot simply tell without knowing the type and nature of the dataset. This paper Article Deep learning with knowledge transfer for explainable anomal...
here might help, as it utilises LSTMs for a real-world anomaly prediction application. Try out other similar models such as GRUs and CNNs as well as more conventional techniques (e.g. decision trees). Hope this helps,Best Wishes,
Joyjit
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