论文标题
使用综合数据生成的时间序列分类的转移学习
Transfer learning for time series classification using synthetic data generation
论文作者
论文摘要
在本文中，我们提出了时间序列分类方法的创新转移学习。我们没有使用UCR存档中的现有数据集作为源数据集，而是生成了15,000,000个合成单变量时间序列数据集，该数据集是使用我们唯一的合成时间序列生成器算法创建的，该数据可以生成具有不同模式和角度和角度和不同序列长度的数据。此外，我们没有像以前的研究一样使用UCR存档提供的分类任务作为源任务，而是使用自己的55个回归任务作为源任务，这比从UCR存档中选择分类任务更好
In this paper, we propose an innovative Transfer learning for Time series classification method. Instead of using an existing dataset from the UCR archive as the source dataset, we generated a 15,000,000 synthetic univariate time series dataset that was created using our unique synthetic time series generator algorithm which can generate data with diverse patterns and angles and different sequence lengths. Furthermore, instead of using classification tasks provided by the UCR archive as the source task as previous studies did,we used our own 55 regression tasks as the source tasks, which produced better results than selecting classification tasks from the UCR archive
