Multiple time series forecasting

To invoke forecasting on multiple time series, you simply just need an ML job that is modeling multiple time series. Let’s assume that we have an ML job that has analyzed web requests per country. In fact, using the built-in sample web logs (kibana_sample_data_logs) we used in Chapter 3, Anomaly Detection, we could easily create a multi-metric job that counts events, split on the source country code of the request (the field is called geo.src), as illustrated in the following screenshot:

Figure 4.26 – Creating a multi-metric job for forecasting

There are 183 unique source countries in this dataset. After creating and running this Anomaly Detection job in order to build baseline models for all 183 countries, we are now in a position to invoke a forecast. If we approach the invocation of a forecast in the same way as we did before (via Single Metric Viewer), we might erroneously think that a forecast will only be...