Detecting anomalies in raw, streaming time series data requires some data transformation. The most obvious way to do this is to select a time window and sample a time series with a fixed length. In the next step, we want to compare a new time series to our previously collected set to detect whether something is out of the ordinary.
The comparison can be done with various techniques, as follows:
- Forecasting the most probable following value, as well as the confidence intervals (for example, Holt-Winters exponential smoothing). If a new value is out of the forecasted confidence interval, it is considered anomalous.
- Cross-correlation compares a new sample to a library of positive samples, and it looks for ...
