Online Data Augmentation for Forecasting with Deep Learning

• URL: http://arxiv.org/abs/2404.16918v2
• Date: Fri, 03 Jan 2025 11:56:29 GMT
• Title: Online Data Augmentation for Forecasting with Deep Learning
• Authors: Vitor Cerqueira, Moisés Santos, Luis Roque, Yassine Baghoussi, Carlos Soares,
• Abstract summary: This work introduces an online data augmentation framework that generates synthetic samples during the training of neural networks. We maintain a balanced representation between real and synthetic data throughout the training process. Experiments suggest that online data augmentation leads to better forecasting performance compared to offline data augmentation or no augmentation approaches.
• Score: 0.33554367023486936
• License:
• Abstract: 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.

Related papers

• Generating Realistic Tabular Data with Large Language Models [49.03536886067729]
  Large language models (LLM) have been used for diverse tasks, but do not capture the correct correlation between the features and the target variable. We propose a LLM-based method with three important improvements to correctly capture the ground-truth feature-class correlation in the real data. Our experiments show that our method significantly outperforms 10 SOTA baselines on 20 datasets in downstream tasks.
  arXiv  Detail & Related papers  (2024-10-29T04:14:32Z)
• Retrieval-Augmented Data Augmentation for Low-Resource Domain Tasks [66.87070857705994]
  In low-resource settings, the amount of seed data samples to use for data augmentation is very small. We propose a novel method that augments training data by incorporating a wealth of examples from other datasets. This approach can ensure that the generated data is not only relevant but also more diverse than what could be achieved using the limited seed data alone.
  arXiv  Detail & Related papers  (2024-02-21T02:45:46Z)
• Learning Defect Prediction from Unrealistic Data [57.53586547895278]
  Pretrained models of code have become popular choices for code understanding and generation tasks. Such models tend to be large and require commensurate volumes of training data. It has become popular to train models with far larger but less realistic datasets, such as functions with artificially injected bugs. Models trained on such data tend to only perform well on similar data, while underperforming on real world programs.
  arXiv  Detail & Related papers  (2023-11-02T01:51:43Z)
• Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality [78.6359306550245]
  We argue that using just one synthetic subset for distillation will not yield optimal generalization performance. PDD synthesizes multiple small sets of synthetic images, each conditioned on the previous sets, and trains the model on the cumulative union of these subsets. Our experiments show that PDD can effectively improve the performance of existing dataset distillation methods by up to 4.3%.
  arXiv  Detail & Related papers  (2023-10-10T20:04:44Z)
• A New Benchmark: On the Utility of Synthetic Data with Blender for Bare Supervised Learning and Downstream Domain Adaptation [42.2398858786125]
  Deep learning in computer vision has achieved great success with the price of large-scale labeled training data. The uncontrollable data collection process produces non-IID training and test data, where undesired duplication may exist. To circumvent them, an alternative is to generate synthetic data via 3D rendering with domain randomization.
  arXiv  Detail & Related papers  (2023-03-16T09:03:52Z)
• Dataset Distillation by Matching Training Trajectories [75.9031209877651]
  We propose a new formulation that optimize our distilled data to guide networks to a similar state as those trained on real data. Given a network, we train it for several iterations on our distilled data and optimize the distilled data with respect to the distance between the synthetically trained parameters and the parameters trained on real data. Our method handily outperforms existing methods and also allows us to distill higher-resolution visual data.
  arXiv  Detail & Related papers  (2022-03-22T17:58:59Z)
• Towards Synthetic Multivariate Time Series Generation for Flare Forecasting [5.098461305284216]
  One of the limiting factors in training data-driven, rare-event prediction algorithms is the scarcity of the events of interest. In this study, we explore the usefulness of the conditional generative adversarial network (CGAN) as a means to perform data-informed oversampling.
  arXiv  Detail & Related papers  (2021-05-16T22:23:23Z)
• Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoML [0.0]
  This paper presents an easy to implement data augmentation method to significantly improve the performance of neural networks. It shows that data augmentation, when paired Automated Machine Learning techniques such as Neural Architecture Search, can help to find the best neural architecture for a given time series.
  arXiv  Detail & Related papers  (2021-03-02T19:20:49Z)
• STAN: Synthetic Network Traffic Generation with Generative Neural Models [10.54843182184416]
  This paper presents STAN (Synthetic network Traffic generation with Autoregressive Neural models), a tool to generate realistic synthetic network traffic datasets. Our novel neural architecture captures both temporal dependencies and dependence between attributes at any given time. We evaluate the performance of STAN in terms of the quality of data generated, by training it on both a simulated dataset and a real network traffic data set.
  arXiv  Detail & Related papers  (2020-09-27T04:20:02Z)
• Dataset Condensation with Gradient Matching [36.14340188365505]
  We propose a training set synthesis technique for data-efficient learning, called dataset Condensation, that learns to condense large dataset into a small set of informative synthetic samples for training deep neural networks from scratch. We rigorously evaluate its performance in several computer vision benchmarks and demonstrate that it significantly outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-06-10T16:30:52Z)
• Omni-supervised Facial Expression Recognition via Distilled Data [120.11782405714234]
  We propose omni-supervised learning to exploit reliable samples in a large amount of unlabeled data for network training. We experimentally verify that the new dataset can significantly improve the ability of the learned FER model. To tackle this, we propose to apply a dataset distillation strategy to compress the created dataset into several informative class-wise images.
  arXiv  Detail & Related papers  (2020-05-18T09:36:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.