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• Empirical evaluation of data augmentation techniques for univariate time series forecasting. • Data augmentation techniques based on combining or upsampling the original time series improve the forecasting performance of neural networks. • Accuracy improvements from data augmentation are more significant when deeper neural networks are used. • Accuracy improvements from data augmentation become less significant as the initial size of the set increases. Neural networks have been proven particularly accurate in univariate time series forecasting settings, requiring however a significant number of training samples to be effectively trained. In machine learning applications where available data are limited, data augmentation techniques have been successfully used to generate synthetic data that resemble and complement the original train set. Since the potential of data augmentation has been largely neglected in univariate time series forecasting, in this study we investigate nine data augmentation techniques, ranging from simple transformations and adjustments to sophisticated generative models and a novel upsampling approach. We empirically evaluate the impact of data augmentation on forecasting accuracy considering both shallow and deep feed-forward neural networks and time series data sets of different sizes from the M4 and the Tourism competitions. Our results suggest that certain data augmentation techniques that build on upsampling and time series combinations can improve forecasting performance, especially when deep networks are used. However, these improvements become less significant as the initial size of the train set increases. [ABSTRACT FROM AUTHOR]