Abstract Due to the extensive practical value of time series prediction, many excellent algorithms have been proposed. Most of these methods are developed assuming that massive labeled training data are available. However, this assumption might be invalid in some actual situations. To address this limitation, a transfer learning framework with deep architectures is proposed. Since convolutional neural network (CNN) owns favorable feature extraction capability and can implement parallelization more easily, we propose a deep transfer learning method resorting to the architecture of CNN, termed as DTr-CNN for short. It can effectively alleviate the available labeled data absence and leverage useful knowledge to the current prediction. Notably, in our method, transfer learning process is implemented across different datasets. For a given target domain, in real-world scenarios, relativity of truly available potential source datasets may not be obvious, which is challenging and rarely referred to in most existing time series prediction methods. Aiming at this problem, the incorporation of Dynamic Time Warping (DTW) and Jensen-Shannon (JS) divergence is adopted for the selection of the appropriate source domain. Effectiveness of the proposed method is empirically underpinned by the experiments conducted on one group of synthetic and two groups of practical datasets. Besides, an additional experiment on NN5 dataset is conducted.

中文翻译：

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跨不同数据集实施迁移学习以进行时间序列预测

摘要 由于时间序列预测具有广泛的实用价值，人们提出了许多优秀的算法。大多数这些方法是在假设有大量标记的训练数据可用的情况下开发的。但是，这种假设在某些实际情况下可能是无效的。为了解决这个限制，提出了一种具有深度架构的迁移学习框架。由于卷积神经网络 (CNN) 具有良好的特征提取能力并且可以更容易地实现并行化，因此我们提出了一种求助于 CNN 架构的深度迁移学习方法，简称为 DTr-CNN。它可以有效地减少可用标记数据的缺失，并将有用的知识用于当前的预测。值得注意的是，在我们的方法中，迁移学习过程是在不同的数据集上实现的。对于给定的目标域，在现实世界的场景中，真正可用的潜在源数据集的相关性可能并不明显，这是具有挑战性的，并且在大多数现有的时间序列预测方法中很少提及。针对这个问题，结合动态时间扭曲（DTW）和詹森-香农（JS）散度来选择合适的源域。所提出方法的有效性得到了对一组合成数据集和两组实际数据集进行的实验的经验支持。此外，还对 NN5 数据集进行了额外的实验。采用动态时间扭曲 (DTW) 和 Jensen-Shannon (JS) 散度的结合来选择合适的源域。所提出方法的有效性得到了对一组合成数据集和两组实际数据集进行的实验的经验支持。此外，还对 NN5 数据集进行了额外的实验。采用动态时间规整 (DTW) 和 Jensen-Shannon (JS) 散度的结合来选择合适的源域。所提出方法的有效性得到了对一组合成数据集和两组实际数据集进行的实验的经验支持。此外，还对 NN5 数据集进行了额外的实验。