Combining forecasts

We have generated forecasts using many techniques—some univariate, some machine learning, and so on. But at the end of the day, we would need a single forecast, and that means choosing a forecast or combining a variety. The most straightforward option is to choose the algorithm that does the best in the validation dataset, which in our case is LightGBM. We can think of this selection as another function that takes the forecasts that we generated as inputs and combines them into a final forecast. Mathematically, this can be represented as follows:

Y = F(Y₁, Y₂, …, Y_N)

Here, F is the function that combines N forecasts. We can use the F function to choose the best-performing model in the validation dataset. However, this function can be as complex as it wants to be, and choosing the right F function while balancing bias and variance is a must.

Notebook alert:

To follow along with the code, use the 01-Forecast_Combinations.ipynb...