Hi everyone,
I am working on a project where I need to implement an anomaly detection system for streaming data. After exploring several approaches, I came across Hierarchical Temporal Memory & its potential for detecting anomalies in real time data streams. I am interested in learning how HTM can be effectively applied in this scenario for non stationary data with evolving patterns.
Could anyone share insights or resources on how to structure the input data for HTM? I am curious about the best practices for encoding the data & selecting parameters for real time applications. How does HTM compare with traditional machine learning models like LSTMs for anomaly detection in terms of accuracy and adaptability??
As well, I found these resources when doing research on this; Performance testing for HTM anomaly detection & if anyone have any resources, tutorials or personal experiences please share with me, It would be greatly appreciated!!
Thank you…… 