Abstract

This paper describes a new transfer learning method for modeling sensor time series following multiple different distributions, e.g. originating from multiple different tool settings. The method aims at removing distribution specific information before the modeling of the individual time series takes place. This is done by mapping the data to a new space such that the representations of different distributions are aligned. Domain knowledge is incorporated by means of corresponding parameters, e.g. physical dimensions of tool settings. Results on a real-world problem of industrial manufacturing show that our method is able to significantly improve the performance of regression models on time series following previously unseen distributions.

Graphic abstract

摘要

本文介绍了一种新的转移学习方法，用于建模遵循多个不同分布（例如源自多个不同工具设置）的传感器时间序列。该方法旨在在对单个时间序列进行建模之前删除特定于分布的信息。通过将数据映射到新空间以对齐不同分布的表示形式，可以完成此操作。通过相应的参数（例如，工具设置的物理尺寸）来合并领域知识。关于工业制造的实际问题的结果表明，我们的方法能够显着改善按照先前看不见的分布的时间序列上的回归模型的性能。

图形摘要