Traditional time series forecasting follows the same workflow as most of the fields of predictive analysis, such as regression or classification, and typically includes the following steps:
- Data preparation: Here, we prepare the data for the training and testing process of the model. This step includes splitting the series into training (in-sample) and testing (out-sample) partitions, creating new features (when applicable), and applying a transformation if needed (for example, log transformation, scaling, and so on).
- Train the model: Here, we used the training partition to train a statistical model. The main goal of this step is to utilize the training set to train, tune, and estimate the model coefficients that minimize the selected error criteria (later on in this chapter, we will discuss common error metrics in detail). The fitted values and the model...
