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kLabUM
🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams
On the streaming data, is it necessary to design the dynamic threshold of the anomaly score to adapt over time as the input data changes? Thank you.
While saving an empty tree `TypeError` is raised. Here's the code: ```py import rrcf a = rrcf.RCTree() a.to_dict() ``` and the stack trace: ```py Traceback (most recent call last): File...
In certain cases, a stream may continue to get constant values for a while. Sometimes, in this case xmin=xmax resulting in l=nan, thereby leading to an exception in the following...
I have been spending some time to understand rrcf. When we consider streaming data, what is the intuition behind shingle_size? At first I understand that it is kind of rolling...
Hi, I tested the streaming code( https://klabum.github.io/rrcf/streaming.html) and found that all trees are similar to each other compared to the batch processing code (https://klabum.github.io/rrcf/batch.html). I spent some time understanding the...
Hi, Thanks for implementing this. I have a use case that need train the rrcf using some given dataset, and then predict on unseen data samples this haven't been seen...
Hi, Thanks for this wonderful library. I have a use case where my time series is in the hierarchical format. means first i have to predict on country level then...
I am using the streaming mode of rrcf to detect anomalies from data generated by a sensor. I was able to implement it. Yet is there a built-in function to...
Hi Guys. Thanks for this RCCF library. It is so useful. I have a question here. How do we integrate this with realtime streaming data such as from mqtt server.?...
