  Statistical methods such as the Box-Jenkins method for time-series
forecasting have been prominent since their development in 1970. Many
researchers rely on such models as they can be efficiently estimated and also
provide interpretability. However, advances in machine learning research
indicate that neural networks can be powerful data modeling techniques, as they
can give higher accuracy for a plethora of learning problems and datasets. In
the past, they have been tried on time-series forecasting as well, but their
overall results have not been significantly better than the statistical models
especially for intermediate length times series data. Their modeling capacities
are limited in cases where enough data may not be available to estimate the
large number of parameters that these non-linear models require. This paper
presents an easy to implement data augmentation method to significantly improve
the performance of such networks. Our method, Augmented-Neural-Network, which
involves using forecasts from statistical models, can help unlock the power of
neural networks on intermediate length time-series and produces competitive
results. It shows that data augmentation, when paired with Automated Machine
Learning techniques such as Neural Architecture Search, can help to find the
best neural architecture for a given time-series. Using the combination of
these, demonstrates significant enhancement in the forecasting accuracy of
three neural network-based models for a COVID-19 dataset, with a maximum
improvement in forecasting accuracy by 21.41%, 24.29%, and 16.42%,
respectively, over the neural networks that do not use augmented data.
