Avik Basu
Avik Basu
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...
# Summary Support streaming generation of time series in synthetic module # Use Cases This can be useful in generating a streaming example, mostly coupled in a numaflow pipeline ---...
# Summary Currently LSTM and Vanilla/Conv1d based models require a different shape of input. This creates a bit of confusion and can be simplified. What change needs making? Support same...
# Summary Add forecast based anomaly detection models, both uni-channel and muti-channel.
Explain what this PR does.
 ### Snyk has created this PR to fix 2 vulnerabilities in the dockerfile dependencies of this project. Keeping your Docker base image up-to-date means you’ll benefit from security fixes...
 ### Snyk has created this PR to fix 1 vulnerabilities in the dockerfile dependencies of this project. Keeping your Docker base image up-to-date means you’ll benefit from security fixes...