Single time series forecasting
To illustrate the procedure of forecasting, we will start with a dataset that is a single time series. While this dataset is generic, you could imagine that it could represent a system performance metric, the number of transactions processed by a system, or even sales revenue data. The important aspect of this dataset is that it contains several distinct time-based trends—a daily trend, a weekly trend, and an overall increasing trend. Elastic ML will discover all three trends and will effectively predict those into the future. It is good to note that the dataset also contains some anomalies, but (of course) future anomalies cannot be predicted as they are surprise events by definition. Since our discussion here is purely focused on forecasting, we will ignore the existence of any anomalies found in our dataset while building the models for forecasting.
With that said, let’s jump into an example by using the forecast_example dataset from...
