The Arbitration Dynamic Ensemble for Time Series Forecasting

The Arbitration Dynamic Ensemble for Time Series Forecasting

Several types of ensemble techniques are available, ranging from very simple ones like weighted averaging or max voting to more complex ones like bagging, boosting and stacking. This blog post is an excellent starting point to get up to speed with the various techniques mentioned.

“N one of us is as strong as all of us”

Ensemble Techniques have become quite popular among the machine learning fraternity because of the simple fact that ‘one-size-fits-all’ can’t always practically hold good for individual models and there are almost always going to be some models which trade low variance for a high bias while others that do the opposite. The challenge is exacerbated for time series forecasting problems because even the best performing models may not perform consistently well throughout the forecast horizon. This is one of the motivations behind the topic of this article — the Arbitrated Dynamic Ensemble** (ADE)**. But more on that in a bit.

First, if you haven’t already, I’d recommend checking out this comprehensive guide to ensemble learning. Ensemble models are exactly that-an ensemble, or a collection of various base models (or weak learners as they are referred to in literature). Several weak learners combine to train a strong learner — as simple as that. How exactly they combine is what gives rise to the various types of ensemble techniques. Several types of ensemble techniques are available, ranging from very simple ones like weighted averaging or max voting to more complex ones like bagging, boosting and stacking.

This blog post is an excellent starting point to get up to speed with the various techniques mentioned.

Building Blocks

Let us now look at two common ensemble learning techniques at a very high level. Weighted Average and *Stacking *are the building blocks of ADE so we will start by spending some time on these two ensemble techniques first.

time-series-forecasting data-science ensemble-learning time-series-analysis machine-learning

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