  With large quantities of data typically available nowadays, forecasting
models that are trained across sets of time series, known as Global Forecasting
Models (GFM), are regularly outperforming traditional univariate forecasting
models that work on isolated series. As GFMs usually share the same set of
parameters across all time series, they often have the problem of not being
localised enough to a particular series, especially in situations where
datasets are heterogeneous. We study how ensembling techniques can be used with
generic GFMs and univariate models to solve this issue. Our work systematises
and compares relevant current approaches, namely clustering series and training
separate submodels per cluster, the so-called ensemble of specialists approach,
and building heterogeneous ensembles of global and local models. We fill some
gaps in the approaches and generalise them to different underlying GFM model
types. We then propose a new methodology of clustered ensembles where we train
multiple GFMs on different clusters of series, obtained by changing the number
of clusters and cluster seeds. Using Feed-forward Neural Networks, Recurrent
Neural Networks, and Pooled Regression models as the underlying GFMs, in our
evaluation on six publicly available datasets, the proposed models are able to
achieve significantly higher accuracy than baseline GFM models and univariate
forecasting methods.
