This post is a continuation of my previous post Extreme Rare Event Classification using Autoencoders. In the previous post, we talked about the challenges in an extremely rare event data with less than 1% positively labeled data. We built an Autoencoder Classifier for such processes using the concepts of Anomaly Detection. However, the data we have is a time series. But earlier we used a Dense layer Autoencoder that does not use the temporal features in the data.

This post is a continuation of my previous post Extreme Rare Event Classification using Autoencoders. In the previous post, we talked about the challenges in an extremely rare event data with less than 1% positively labeled data. We built an Autoencoder Classifier for such processes using the concepts of Anomaly Detection. However, the data we have is a time series. But earlier we used a Dense layer Autoencoder that does not use the temporal features in the data.