  Recent state-of-the-art forecasting methods are trained on collections of
time series. These methods, often referred to as global models, can capture
common patterns in different time series to improve their generalization
performance. However, they require large amounts of data that might not be
readily available. Besides this, global models sometimes fail to capture
relevant patterns unique to a particular time series. In these cases, data
augmentation can be useful to increase the sample size of time series datasets.
The main contribution of this work is a novel method for generating univariate
time series synthetic samples. Our approach stems from the insight that the
observations concerning a particular time series of interest represent only a
small fraction of all observations. In this context, we frame the problem of
training a forecasting model as an imbalanced learning task. Oversampling
strategies are popular approaches used to deal with the imbalance problem in
machine learning. We use these techniques to create synthetic time series
observations and improve the accuracy of forecasting models. We carried out
experiments using 7 different databases that contain a total of 5502 univariate
time series. We found that the proposed solution outperforms both a global and
a local model, thus providing a better trade-off between these two approaches.
