An implementation of the Random Cut Forest data structure for sketching streaming data, with support for anomaly detection, density estimation, imputation, and more.
The summarization function is useful in the missing value interpolation/forecast for multiple variables since it accounts for scenarios. It Is an useful summarization primitive because it does not need to know the target number of clusters/scenarios and may be used elsewhere. The summarization already accepts arbitrary distance functions. Goal is to generalize to instead of being fixed to <float[]>.
sudiptoguha
commented
1 year ago
The summarization function is useful in the missing value interpolation/forecast for multiple variables since it accounts for scenarios. It Is an useful summarization primitive because it does not need to know the target number of clusters/scenarios and may be used elsewhere. The summarization already accepts arbitrary distance functions. Goal is to generalize to instead of being fixed to <float[]>.