Understanding anomaly detection in time series data
Anomaly detection is the process of identifying the points in data that don’t fit the normal data behavioral patterns. To make this effective, we can automate the whole process. The important point to note here is that this process will be more efficient when the size of the data has increased. The Elastic Stack supports several data analysis use cases that use supervised and unsupervised machine learning, as follows:
- Anomaly detection
- Outlier detection
- Fraud detection
- Forecasting
- Language detection
Our main intention behind putting various techniques to use is to bring out the insights from the most normal-looking data. When we look into anomaly detection, we identify patterns and unusual behavior in the near real-time current and historical data. An unusual data point can be seen in the form of a high spike or very low data behavior, as shown here:
Figure 6.1 –...
