numalogic
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numaproj
Collection of operational time series ML models and tools
# Summary Support detecting data drift in training vs real-time data, automatically using statistical methods to start with. # Use Cases Data drift is natural, and can help determine when...
Thresholding techniques can vary from basic ones like mean + std thresholding, median based methods to more complex ones. Decoupling the threshold calculation from Autoencoder models can provide more flexibility.
# Summary What change needs making? # Use Cases When would you use this? --- **Message from the maintainers**: If you wish to see this enhancement implemented please add a...
# Summary What change needs making? # Use Cases When would you use this? --- **Message from the maintainers**: If you wish to see this enhancement implemented please add a...
Changes to code and numalogic-python can cause numalogic examples to potentially break. Need to make sure that the examples run as expected. Can be done either as a part of...
# Summary Local file based registry by overloading the base ArtifactManager class. # Use Cases Will be useful in testing out the registry saving/loading pattern for quick start guides. ---...
Due to the random numbers generated in the `AnomalyGenerator` class, the coverage varies with every run. Ideally we should have have a random seed in the test file, to have...
GRU based autoencoder will be a good addition, since it is simpler than LSTM, hence is quicker to train.
Intermittently this test case throws this error, mostly because of the random number that is generated. ``` =================================== FAILURES =================================== _________________ TestTransformers.test_staticpowertransformer _________________ self = def test_staticpowertransformer(self): x = 1...
# Summary Support redis for artifact caching. # Use Cases This will speedup model loading and inference. --- **Message from the maintainers**: If you wish to see this enhancement implemented...