On-the-fly Data Augmentation Improves Forecasting Performance of Deep Learning Models

Core Concepts

Applying data augmentation during the training process of deep learning models, rather than before, can improve forecasting performance by better exploring the underlying data generation process.

Abstract

The paper introduces OnDAT, a novel approach for applying data augmentation during the training process of deep neural networks for univariate time series forecasting.
The key insights are:

Traditional data augmentation methods create a single augmented dataset before training, which may not cover the data generation process well.

OnDAT performs data augmentation on-the-fly during both the training and validation stages. This process generates a constantly changing augmented dataset, enabling better exploration of the data space and reducing overfitting.

OnDAT uses a data augmentation technique based on seasonal decomposition and moving blocks bootstrapping, though the method is agnostic to the underlying data augmentation approach.

Experiments on 75,797 time series across 8 benchmark datasets show that OnDAT outperforms both a standard approach without data augmentation and an approach that applies data augmentation before training.

Additional analyses demonstrate that applying data augmentation during validation also improves performance estimates and the effectiveness of early stopping and model checkpointing.

While OnDAT takes slightly longer to execute compared to the standard approach, the difference is not substantial, especially when considering the significant gains in forecasting accuracy.

Overall, the results suggest that on-the-fly data augmentation is a promising direction for building more accurate deep learning forecasting models, particularly when dealing with limited training data.

Stats

The datasets contain a total of 14,062,121 observations across 75,797 univariate time series.
The average value of the time series ranges from 40.9 to 298.5.

Quotes

"By generating a new augmented dataset on each iteration, the model is exposed to a constantly changing augmented data variations."
"We hypothesize this process enables a better exploration of the data space, which reduces the potential for overfitting and improves forecasting performance."

Key Insights Distilled From

On-the-fly Data Augmentation for Forecasting with Deep Learning

by Vito... at arxiv.org 04-29-2024

https://arxiv.org/pdf/2404.16918.pdf

Deeper Inquiries

How can the on-the-fly data augmentation process be further improved, for example by weighting time series based on their impact on the training process?

The on-the-fly data augmentation process can be enhanced by incorporating a mechanism to weight time series based on their influence on the training process. This can be achieved by implementing a dynamic weighting scheme that assigns different weights to each time series based on their historical performance or relevance. By assigning higher weights to time series that have shown to be more impactful in the training process, the model can focus more on learning from these influential series, potentially leading to better forecasting accuracy. Additionally, the weighting scheme can be adaptive, adjusting the weights during the training process based on the model's performance on each time series. This adaptive weighting approach can help the model prioritize learning from time series that are currently more challenging or informative, leading to improved forecasting performance.

What are the potential drawbacks or limitations of on-the-fly data augmentation that the authors did not address?

While on-the-fly data augmentation offers several advantages, there are potential drawbacks and limitations that the authors did not address in their study. One limitation is the computational overhead associated with performing data augmentation during each training iteration. This can lead to increased training times and resource requirements, especially for large datasets. Additionally, the randomness of the data augmentation process may introduce variability in the training procedure, potentially making it harder to interpret and reproduce results. Moreover, the effectiveness of on-the-fly data augmentation may be dependent on the choice of augmentation technique and parameters, which could impact the generalizability of the approach across different datasets and forecasting tasks. Lastly, the authors did not discuss the potential challenges of implementing on-the-fly data augmentation in real-world applications, such as data privacy concerns or regulatory constraints that may limit the use of synthetic data.

How could the proposed approach be extended to handle multivariate time series forecasting problems?

To extend the proposed approach to handle multivariate time series forecasting problems, several modifications and enhancements can be made. One approach is to adapt the data augmentation process to generate synthetic multivariate time series by incorporating dependencies and relationships between different variables. This can involve creating joint synthetic variations of multiple time series within the same batch, considering the interdependencies between the variables. Additionally, the neural network architecture can be modified to accommodate multivariate inputs and outputs, allowing the model to learn from the interactions between different time series. Furthermore, the data preprocessing steps can be adjusted to handle multivariate data, such as normalizing and scaling multiple variables appropriately. By incorporating these modifications, the proposed approach can be effectively extended to address the complexities of multivariate time series forecasting tasks.