# Time Series Data Augmentation as an Imbalanced Learning Problem

arXiv: https://arxiv.org/abs/2404.18537 · HTML: https://mlsanity.com/paper/2404.18537/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 29 Apr 2024.

## Builds on (most cited first, 6 of 6)

- [Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions](https://mlsanity.com/paper/1909.00590/) (2019)
- [Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis](https://mlsanity.com/paper/1606.04316/) (2016)
- [Criteria for Classifying Forecasting Methods](https://mlsanity.com/paper/2212.03523/) (2022)
- [Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation](https://mlsanity.com/paper/2008.02663/) (2020)
- [LSTM-MSNet: Leveraging Forecasts on Sets of Related Time Series with Multiple Seasonal Patterns](https://mlsanity.com/paper/1909.04293/) (2019)
- [Ensembles of Localised Models for Time Series Forecasting](https://mlsanity.com/paper/2012.15059/) (2020)

## Built on by (most cited first, 2 of 2)

- [Meta-learning and Data Augmentation for Stress Testing Forecasting Models](https://mlsanity.com/paper/2406.17008/) (2024)
- [Bridging Neural Networks and Dynamic Time Warping for Adaptive Time Series Classification](https://mlsanity.com/paper/2507.09826/) (2025)

## Abstract (arXiv)

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.