Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation

• URL: http://arxiv.org/abs/2008.02663v1
• Date: Thu, 6 Aug 2020 13:52:20 GMT
• Title: Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation
• Authors: Kasun Bandara, Hansika Hewamalage, Yuan-Hao Liu, Yanfei Kang, Christoph Bergmeir
• Abstract summary: Forecasting models that are trained across sets of many time series, known as Global Forecasting Models (GFM), have shown promising results in forecasting competitions and real-world applications. We propose a novel, data augmentation based forecasting framework that is capable of improving the baseline accuracy of GFM models in less data-abundant settings.
• Score: 7.38079566297881
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Forecasting models that are trained across sets of many time series, known as Global Forecasting Models (GFM), have shown recently promising results in forecasting competitions and real-world applications, outperforming many state-of-the-art univariate forecasting techniques. In most cases, GFMs are implemented using deep neural networks, and in particular Recurrent Neural Networks (RNN), which require a sufficient amount of time series to estimate their numerous model parameters. However, many time series databases have only a limited number of time series. In this study, we propose a novel, data augmentation based forecasting framework that is capable of improving the baseline accuracy of the GFM models in less data-abundant settings. We use three time series augmentation techniques: GRATIS, moving block bootstrap (MBB), and dynamic time warping barycentric averaging (DBA) to synthetically generate a collection of time series. The knowledge acquired from these augmented time series is then transferred to the original dataset using two different approaches: the pooled approach and the transfer learning approach. When building GFMs, in the pooled approach, we train a model on the augmented time series alongside the original time series dataset, whereas in the transfer learning approach, we adapt a pre-trained model to the new dataset. In our evaluation on competition and real-world time series datasets, our proposed variants can significantly improve the baseline accuracy of GFM models and outperform state-of-the-art univariate forecasting methods.

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