Table 8 Steps for residual error minimization in univariate time-series forecasting

Step 1: Creation of lagged datasets

Step 2: Split data into train and test with a 60:20:20 ratio using the stratification technique

Step 3: Apply the persistence model by predicting the output value (Y) as a replica of the input value

(X)

Step 4: Calculate residuals

Step 5: Model the training set residuals with a defined lag value, predict RE with the AR model, and

defined window size ⋲ Z + 

Step 6: Walk forward over time steps in the test dataset

Step 7: Correct forecasts with the designed model of RE using the following equation:

\(improved\;forecast=forecast+estimated\;\;error\)  

Step 8: Calculate metrics for the corrected forecasts and compare them with the forecasts without REM to observe the improvements