Designing the time series data model

We are going to explain the general steps to building a time series model using training data. Use your judgment and experience to discern from the chart whether the data is autoregressive before applying the Durbin-Watson statistical test.

The sequential steps required to build the predictive model with time series machine learning are as follows:

1. Plot the data to inspect the possible autocorrelation relationship.
2. Use the Durbin-Watson statistical test to see whether the data is autocorrelated.
3. Calculate the centered moving average of each period lag of the data.
4. Determine the separation between the data and the centered moving average. This is known as seasonal irregularity.
5. Get the trending component of the time series using the regression model line.
6. Multiply the seasonal irregularity value by the trending result to make the forecast.

We use the centered moving average to smooth or to take the general...