  We investigate ensembling techniques in forecasting and examine their
potential for use in nonseasonal time-series similar to those in the early days
of the COVID-19 pandemic. Developing improved forecast methods is essential as
they provide data-driven decisions to organisations and decision-makers during
critical phases. We propose using late data fusion, using a stacked ensemble of
two forecasting models and two meta-features that prove their predictive power
during a preliminary forecasting stage. The final ensembles include a Prophet
and long short term memory (LSTM) neural network as base models. The base
models are combined by a multilayer perceptron (MLP), taking into account
meta-features that indicate the highest correlation with each base model's
forecast accuracy. We further show that the inclusion of meta-features
generally improves the ensemble's forecast accuracy across two forecast
horizons of seven and fourteen days. This research reinforces previous work and
demonstrates the value of combining traditional statistical models with deep
learning models to produce more accurate forecast models for time-series from
different domains and seasonality.
