Ensembling and Stacking

In the previous chapter, we looked at a few machine learning algorithms and used them to generate forecasts on the London Smart Meters dataset. Now that we have multiple forecasts for all the households in the dataset, how do we come up with a single forecast by choosing or combining these different forecasts? That is what we will be doing in this chapter – we will learn how to leverage combinatorial and mathematical optimization to come up with a single forecast.

In this chapter, we will cover the following topics:

  • Strategies for combining forecasts
  • Stacking or blending

Technical requirements

You will need to set up the Anaconda environment following the instructions in the Preface of the book to get a working environment ...

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