Model not able to predict anomaly after more than few data points

[msplmansit98]

I was exploring Luminaire, But after using the opt_config from hyperopt and creating the model, the model is not able to predict the anomaly status after a certain data point It shows- {'Success': False, 'ErrorMessage': 'Current trained model object expired'} I have a daily data of 4.5k-5k data points What am I missing here ? Why for some data points anomaly status is not showing up ? What is the min and max data range that can be feed + how much it can predict ?

[sayanchk]

@msplmansit98 Luminaire is a time series anomaly detection tool that tracks temporal patterns and variations over time. The reliability of the model depend on the fact that it needs to be trained at a regular cadence. Therefore, by design, Luminaire has a model expiry concept (ModelFreshness score) that expires a model if an outdated model object is being used for serving. Please refer to the outlier detection documentation for more details.

[sayanchk]

@msplmansit98 If you have daily data with 4.5k-5k data points, that means your observations are coming at a higher frequency than an hour. Luminaire batch model currently supports till hourly cadence (reference). You might want to look into the streaming data module which is more flexible and could be a better option for your use case.