Forecasting – theory of operation

The first thing to realize is that the act of invoking a forecast on data is that it is an extension of an existing job. In other words, you need to have an ML job configured and that job needs to have analyzed historical data before you can forecast on that data. This is because the forecasting process uses the models that are created by the ML job; the same ones that are used for anomaly detection. To forecast, you need to follow the same steps to create an ML job that has been described in other chapters. If anomalies were generated by the execution of that job, you can disregard them if your only purpose is to execute forecasting. Once the ML job has learned on some historical data, the model or models (if the ML job contains more than one time series) associated with that job are current and up to date, as represented by the following...