本文是法国上阿尔萨斯大学发表于 IEEE Big Data 2018 上的工作。迁移学习和深度学习已经被广泛应用于计算机视觉和自然语言处理领域。但是在时间序列分类方面，至今没有完整的有代表性的工作。

本文是第一篇系统探讨基于深度迁移学习进行时间序列分类的论文。在内容上与今年 CVPR 最佳论文 Taskonomy: Disentangling Task Transfer Learning 相似，都是做了大量实验来验证一些迁移学习方面的结论。

Transfer learning for time series classification

Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, Pierre-Alain Muller
(Submitted on 5 Nov 2018)

Transfer learning for deep neural networks is the process of first training a base network on a source dataset, and then transferring the learned features (the network’s weights) to a second network to be trained on a target dataset. This idea has been shown to improve deep neural network’s generalization capabilities in many computer vision tasks such as image recognition and object localization. Apart from these applications, deep Convolutional Neural Networks (CNNs) have also recently gained popularity in the Time Series Classification (TSC) community. However, unlike for image recognition problems, transfer learning techniques have not yet been investigated thoroughly for the TSC task. This is surprising as the accuracy of deep learning models for TSC could potentially be improved if the model is fine-tuned from a pre-trained neural network instead of training it from scratch. In this paper, we fill this gap by investigating how to transfer deep CNNs for the TSC task. To evaluate the potential of transfer learning, we performed extensive experiments using the UCR archive which is the largest publicly available TSC benchmark containing 85 datasets. For each dataset in the archive, we pre-trained a model and then fine-tuned it on the other datasets resulting in 7140 different deep neural networks. These experiments revealed that transfer learning can improve or degrade the model’s predictions depending on the dataset used for transfer. Therefore, in an effort to predict the best source dataset for a given target dataset, we propose a new method relying on Dynamic Time Warping to measure inter-datasets similarities. We describe how our method can guide the transfer to choose the best source dataset leading to an improvement in accuracy on 71 out of 85 datasets.

Comments: Accepted at IEEE International Conference on Big Data 2018
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
Cite as: arXiv:1811.01533 [cs.LG]
(or arXiv:1811.01533v1 [cs.LG] for this version)

论文相关资源

标题	说明	附加
Transfer learning for time series classification	原始论文	20181105
hfawaz/bigdata18	官方论文代码	20181010
基于深度迁移学习进行时间序列分类	论文解读	20181119