More on model evaluation

In the previous sections, we discussed other methods to prepare data, test and validate models. In this section, we will discuss how to validate time series models and introduce several methods for validating time series models. We will cover the following methods for model evaluation: resampling, shifting, optimized persistence forecasting, and rolling window forecasting.

The real-world dataset considered in this section is Coca Cola stock data collected from Yahoo Finance databases from 01/19/1962 to 12/19/2021 for stock price prediction. This is a time series analysis to forecast the future stock value of a given stock. The reader can download the dataset from the Kaggle platform for this analysis. To motivate the study, we first go to explore the Coco Cola stock dataset:

data = pd.read_csv("COCO COLA.csv", parse_dates=["Date"], index_col="Date")

Figure 11.26 – Coco Cola dataset

The...