We have already implemented one code sample which uses fbprophet to detect anomalies
We would want you to make more code examples using other predictive models. :pray:
List of Predictive Models that can be used
[x] ARIMA
[x] SARIMA
[x] ARCH
[ ] GARCH
[ ] VAR
[ ] VECM
[ ] LSTM
[ ] Convolutional Autoencoder model
Instructions to be followed
The Code examples on the implementations of most of the models mentioned above should be available in the internet. :octocat:
Build the respective predictive model on the NAB Dataset(RealAWSCloudwatch - Any CPU Utilization Data ONLY).
Use the Predictive Model to forecast future values.
Find the Error rate of the predictive (Use MAPE/MSE) and come up with a confidence interval for the predicted value.
Any Actual datapoint falling beyond the confidence interval, will be detected as an anomaly.
Additional instructions
Make a new jupyter notebook with the algorithm name and work in that only.
Initially above algorithms will be assigned on a First Come First Serve basis. The algorithm will be assigned to you if you create an "assign request" comment below with a small description of the algorithm you have requested for. :smiley: :1st_place_medal:
@JoeRishon & @manikyabard for any doubts and additional details
We have already implemented one code sample which uses fbprophet to detect anomalies We would want you to make more code examples using other predictive models. :pray:
List of Predictive Models that can be used
[x] ARIMA
[x] SARIMA
[x] ARCH
[ ] GARCH
[ ] VAR
[ ] VECM
[ ] LSTM
[ ] Convolutional Autoencoder model
Instructions to be followed
Additional instructions
@JoeRishon & @manikyabard for any doubts and additional details