发表机构
The Pennsylvania State University（宾夕法尼亚州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出SMart框架，通过多阶段递归图恢复任务和多源数据集选择机制改进TSRL，在时间序列分类、回归任务中较先进模型表现更优，误差降低、准确率提升。
AI 中文摘要
近年来，时间序列表示学习（TSRL）受到越来越多的研究关注。TSRL领域近期有两项探索：i）利用基于Transformer的框架学习时间序列；ii）不再仅使用目标数据集，而是从其他数据集借用时间序列以促进表示迁移。尽管这两项探索被证明有效，但（i）中的自监督时间序列恢复任务和（ii）中使用的单源数据集在技术上较为简单，可通过新想法加以改进。本研究提出一种新的TSRL框架，即多源多阶段时间序列表示迁移（SMart），该框架包含两种新颖机制以解决上述缺陷：1）一种多阶段递归图恢复任务，具有三种可选模式，用于引导编码器将时间序列动态嵌入时间序列表示；2）一种源数据集选择器，用于选择多个合适的源数据集以补充原始目标数据集，用于预训练TSRL编码器。实验结果表明，SMart在单变量和多变量时间序列数据集上，在时间序列表示学习、分类和回归任务中均优于若干最先进模型：时间序列回归任务的平均绝对误差最高降低19.5%，时间序列分类任务的平均准确率最高提升1.34%。
英文摘要
Time series representation learning (TSRL) has attracted growing research interests in recent years. Two recent explorations in TSRL are: i) exploiting a transformer-based framework to learn time series; ii) instead of using only the targeted dataset, borrowing time series from other datasets to to facilitate representation transfer. While these two explorations are shown effective, the self-supervised time series recovery task in (i) and the single-source dataset used in (ii) are technically simple and thus can be enhanced with new ideas. In this work, we propose a new TSRL framework, namely multi-source multi-phase time series representation transfer (SMart), which has two novel mechanisms to address the aforementioned deficiencies: 1) a multi-phase recurrence plots recovery task, in three alternative modes, for guiding the encoder to embed time series dynamics into the time series representation; and 2) a source dataset selector to select multiple suitable source datasets to supplement the original target dataset for pre-training the TSRL encoder. Experimental results show that SMart outperforms several state-of-the-art models for time series representation learning, classification and regression on both uni-variate and multi-variate time series datasets, reducing mean absolute error up to 19.5% for time series regression, and increasing average accuracy up to 1.34\% for time series classification.
Comments11 pages
