MetricBase creates a model by training a
representative sample of your time series data to determine the model parameters. The
training process determines the model parameters that best fit your data, to distinguish
normal data from anomalous data. MetricBase supports the following model
types:
- Probabilistic Exponentially Weighted Moving Average (PEWMA), a moving average
algorithm that uses a probability factor to determine how it reacts to change in
data
- Autoregressive Integrated Moving Average (ARIMA), a moving average algorithm
that factors in previous errors and values
- Seasonal Trend decomposition using Loess (STL), a seasonal algorithm for
decomposing time series data into seasonal and trend components
- Holt-Winters (HW), a seasonal algorithm that decomposes the trend and seasonal
components to determine the level
Note: MetricBase selects the most appropriate model type when you
select Find Best Fit Model from the model class
list.
After you have a model trained from your data, you can trigger flows when
new data is significantly different than the trained data.