  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 existing GFM localisation approaches, in particular by
incorporating varied clustering techniques such as feature-based clustering,
distance-based clustering and random clustering, and generalise them to use
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 eight publicly available
datasets, the proposed models are able to achieve significantly higher accuracy
than baseline GFM models and univariate forecasting methods.
