Added synthetic anomaly generation #3
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fixed threshold setting in discern and added a small test.
added null model and made period to be automatically determined.
added support for specifying config via a command line.
Added support for not setting the period
Modified the null model to always predict 0
fixed the naive model time-filtering.
Remove jar from scm, download through maven distribution.
Typo ;-)
Update README.md
Maven Build Fix
fix picking best model
initial travis.yml
Fix bias, negative values are treated as error (where treated as "very small error") Fix coparison of NaN (NaN were not handled) Refactor betterThan()
Fix TimeSeriesAbstractModel.betterThan()
The original path seems to be getting redirected.
Updating the path to the paper
Update README.md
methods. Signed-off-by: Chris Larsen <[email protected]>
Signed-off-by: Chris Larsen <[email protected]>
selections to build as a library or a fat jar. Modify the distribution to go through Yahoo's Bintray account.
Fix issue 37: correct the model name in DoubleExponentialSmoothingModel
LGTM. Could use some docs though.
model that properly aligns time stamps based on timezone changes. Also allows for weighting more recenter periods higher than older periods using the WeightedValue class. Signed-off-by: Chris Larsen <[email protected]>
Signed-off-by: Chris Larsen <[email protected]>
Update the docs to the proper class path for launching. Signed-off-by: Chris Larsen <[email protected]>
Signed-off-by: Chris Larsen <[email protected]>
Mostly to see if this will make it to JCenter. Also add some deployment plugins.
* update to return anomalies as list
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Python code for synthetic anomaly generation in real time-series. It also allows to smooth out the real time-series and generates anomalies in smoothen synthetic time-series. One can set parameters like type, frequency and severity of anomalies using config file.