Deep learning approaches are increasingly used to tackle forecasting tasks. A
key factor in the successful application of these methods is a large enough
training sample size, which is not always available. In these scenarios,
synthetic data generation techniques are usually applied to augment the
dataset. Data augmentation is typically applied before fitting a model.
However, these approaches create a single augmented dataset, potentially
limiting their effectiveness. This work introduces OnDAT (On-the-fly Data
Augmentation for Time series) to address this issue by applying data
augmentation during training and validation. Contrary to traditional methods
that create a single, static augmented dataset beforehand, OnDAT performs
augmentation on-the-fly. 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. We validated the proposed approach using a
state-of-the-art deep learning forecasting method and 8 benchmark datasets
containing a total of 75797 time series. The experiments suggest that OnDAT
leads to better forecasting performance than a strategy that applies data
augmentation before training as well as a strategy that does not involve data
augmentation. The method and experiments are publicly available.

利用 OnDAT (即时数据增强) 方法，在深度学习模型的训练和验证过程中动态生成不断变化的增强数据集，以减少过拟合并提高预测性能。通过实验证明，与在训练之前应用数据增强的策略以及不采用数据增强的策略相比，OnDAT 方法能够获得更好的预测性能。

深度学习中的即时数据增强技术用于预测

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

To succeed in the real world, robots must cope with situations that differ
from those seen during training. We study the problem of adapting on-the-fly to
such novel scenarios during deployment, by drawing upon a diverse repertoire of
previously learned behaviors. Our approach, RObust Autonomous Modulation
(ROAM), introduces a mechanism based on the perceived value of pre-trained
behaviors to select and adapt pre-trained behaviors to the situation at hand.
Crucially, this adaptation process all happens within a single episode at test
time, without any human supervision. We provide theoretical analysis of our
selection mechanism and demonstrate that ROAM enables a robot to adapt rapidly
to changes in dynamics both in simulation and on a real Go1 quadruped, even
successfully moving forward with roller skates on its feet. Our approach adapts
over 2x as efficiently compared to existing methods when facing a variety of
out-of-distribution situations during deployment by effectively choosing and
adapting relevant behaviors on-the-fly.

现实世界的机器人需要在训练中未曾见过的情况下适应，本文研究了在部署过程中如何通过采用多样化的先前学习行为库来实时适应新奇场景的问题，提出了一种基于预训练行为的感知价值来选择和调整行为的 ROAM 机制，该机制在测试时的单个情节内完成自适应过程，无需任何人类监督，理论分析和实验表明 ROAM 方法能够使机器人迅速适应动力学变化 - 在模拟和真实环境中成功地在滑轮上行进，比现有方法在面对多样的分布情况时适应性提高了 2 倍以上。