Online Data Augmentation for Forecasting with Deep Learning
This work addresses data scarcity in time series forecasting for practitioners using deep learning, offering an incremental improvement over existing augmentation techniques.
The paper tackles the problem of insufficient training data in deep learning for time series forecasting by introducing an online data augmentation framework that generates synthetic samples during training, which improved forecasting performance compared to offline or no augmentation methods, as validated on 3797 time series from 6 benchmark datasets.
Deep learning approaches are increasingly used to tackle forecasting tasks involving datasets with multiple univariate time series. A key factor in the successful application of these methods is a large enough training sample size, which is not always available. Synthetic data generation techniques can be applied in these scenarios to augment the dataset. Data augmentation is typically applied offline before training a model. However, when training with mini-batches, some batches may contain a disproportionate number of synthetic samples that do not align well with the original data characteristics. This work introduces an online data augmentation framework that generates synthetic samples during the training of neural networks. By creating synthetic samples for each batch alongside their original counterparts, we maintain a balanced representation between real and synthetic data throughout the training process. This approach fits naturally with the iterative nature of neural network training and eliminates the need to store large augmented datasets. We validated the proposed framework using 3797 time series from 6 benchmark datasets, three neural architectures, and seven synthetic data generation techniques. The experiments suggest that online data augmentation leads to better forecasting performance compared to offline data augmentation or no augmentation approaches. The framework and experiments are publicly available.