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

arXiv: https://arxiv.org/abs/2008.02663 · HTML: https://mlsanity.com/paper/2008.02663/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2020.

## Built on by (most cited first, 30 of 31)

- [Synthetic Data -- what, why and how?](https://mlsanity.com/paper/2205.03257/) (2022)
- [Data Augmentation techniques in time series domain: A survey and taxonomy](https://mlsanity.com/paper/2206.13508/) (2022)
- [Data Augmentation for Electrocardiograms](https://mlsanity.com/paper/2204.04360/) (2022)
- [Towards Diverse and Coherent Augmentation for Time-Series Forecasting](https://mlsanity.com/paper/2303.14254/) (2023)
- [Adaptive Dependency Learning Graph Neural Networks](https://mlsanity.com/paper/2312.03903/) (2023)
- [Robust Augmentation for Multivariate Time Series Classification](https://mlsanity.com/paper/2201.11739/) (2022)
- [Model Selection for Time Series Forecasting: Empirical Analysis of Different Estimators](https://mlsanity.com/paper/2104.00584/) (2021)
- [Big Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Management](https://mlsanity.com/paper/2110.04256/) (2021)
- [LoMEF: A Framework to Produce Local Explanations for Global Model Time Series Forecasts](https://mlsanity.com/paper/2111.07001/) (2021)
- [Parameterizing the cost function of Dynamic Time Warping with application to time series classification](https://mlsanity.com/paper/2301.10350/) (2023)
- [ST-FiT: Inductive Spatial-Temporal Forecasting with Limited Training Data](https://mlsanity.com/paper/2412.10912/) (2024)
- [On the retraining frequency of global models in retail demand forecasting](https://mlsanity.com/paper/2505.00356/) (2025)
- [A novel weighted approach for time series forecasting based on visibility graph](https://mlsanity.com/paper/2103.13870/) (2021)
- [Amercing: An Intuitive, Elegant and Effective Constraint for Dynamic Time Warping](https://mlsanity.com/paper/2111.13314/) (2021)
- [Time Series Data Augmentation as an Imbalanced Learning Problem](https://mlsanity.com/paper/2404.18537/) (2024)
- [Dominant Shuffle: A Simple Yet Powerful Data Augmentation for Time-series Prediction](https://mlsanity.com/paper/2405.16456/) (2024)
- [Evaluating the Role of Data Enrichment Approaches Towards Rare Event Analysis in Manufacturing](https://mlsanity.com/paper/2407.01644/) (2024)
- [The cost of ensembling: is it always worth combining?](https://mlsanity.com/paper/2506.04677/) (2025)
- [Data Augmentation in Time Series Forecasting through Inverted Framework](https://mlsanity.com/paper/2507.11439/) (2025)
- [Instruction-based Time Series Editing](https://mlsanity.com/paper/2508.01504/) (2025)
- [Grasynda: Graph-based Synthetic Time Series Generation](https://mlsanity.com/paper/2601.19668/) (2026)
- [Data augmentation through multivariate scenario forecasting in Data Centers using Generative Adversarial Networks](https://mlsanity.com/paper/2201.06147/) (2022)
- [Financial Time Series Data Augmentation with Generative Adversarial Networks and Extended Intertemporal Return Plots](https://mlsanity.com/paper/2205.08924/) (2022)
- [Causal Effect Estimation with Global Probabilistic Forecasting: A Case Study of the Impact of Covid-19 Lockdowns on Energy Demand](https://mlsanity.com/paper/2209.08885/) (2022)
- [Synthetically Generating Human-like Data for Sequential Decision Making Tasks via Reward-Shaped Imitation Learning](https://mlsanity.com/paper/2304.07280/) (2023)
- [Fractal interpolation in the context of prediction accuracy optimization](https://mlsanity.com/paper/2403.00403/) (2024)
- [Meta-learning and Data Augmentation for Stress Testing Forecasting Models](https://mlsanity.com/paper/2406.17008/) (2024)
- [A Novel Hybrid Approach to Contraceptive Demand Forecasting: Integrating Point Predictions with Probabilistic Distributions](https://mlsanity.com/paper/2502.09685/) (2025)
- [Enhancing Strawberry Yield Forecasting with Backcasted IoT Sensor Data and Machine Learning](https://mlsanity.com/paper/2504.18451/) (2025)
- [A Review of the Long Horizon Forecasting Problem in Time Series Analysis](https://mlsanity.com/paper/2506.12809/) (2025)

## Abstract (arXiv)

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.