Unsupervised Transfer Learning for Time Series via Self-Predictive Modelling - First Results

Aswolinskiy W, Hammer B (2017)
In: Proceedings of the Workshop on New Challenges in Neural Computation (NC2).

Download
OA 425.58 KB
Conference Paper | English
Abstract
Real-world machine learning applications must be able to adapt to systematic changes in the data, e.g. a new subject or sensor displacement. This can be seen as a form of transfer learning, where the goal is to reuse the old (source) model by adapting the new (target) data. This is a challenging task, if no labels for the target data are available. Here, we propose to use the structure of the source and target data to find a transformation from the source to target space in an unsupervised manner. Our preliminary experiments on multivariate time series data show the feasibility of the approach, but also its limits.
Publishing Year
PUB-ID

Cite this

Aswolinskiy W, Hammer B. Unsupervised Transfer Learning for Time Series via Self-Predictive Modelling - First Results. In: Proceedings of the Workshop on New Challenges in Neural Computation (NC2). 2017.
Aswolinskiy, W., & Hammer, B. (2017). Unsupervised Transfer Learning for Time Series via Self-Predictive Modelling - First Results. Proceedings of the Workshop on New Challenges in Neural Computation (NC2)
Aswolinskiy, W., and Hammer, B. (2017). “Unsupervised Transfer Learning for Time Series via Self-Predictive Modelling - First Results” in Proceedings of the Workshop on New Challenges in Neural Computation (NC2).
Aswolinskiy, W., & Hammer, B., 2017. Unsupervised Transfer Learning for Time Series via Self-Predictive Modelling - First Results. In Proceedings of the Workshop on New Challenges in Neural Computation (NC2).
W. Aswolinskiy and B. Hammer, “Unsupervised Transfer Learning for Time Series via Self-Predictive Modelling - First Results”, Proceedings of the Workshop on New Challenges in Neural Computation (NC2), 2017.
Aswolinskiy, W., Hammer, B.: Unsupervised Transfer Learning for Time Series via Self-Predictive Modelling - First Results. Proceedings of the Workshop on New Challenges in Neural Computation (NC2). (2017).
Aswolinskiy, Witali, and Hammer, Barbara. “Unsupervised Transfer Learning for Time Series via Self-Predictive Modelling - First Results”. Proceedings of the Workshop on New Challenges in Neural Computation (NC2). 2017.
All files available under the following license(s):
Copyright Statement:
This Item is protected by copyright and/or related rights. [...]
Main File(s)
Access Level
OA Open Access
Last Uploaded
2017-11-08T21:05:53Z

This data publication is cited in the following publications:
This publication cites the following data publications:

Export

0 Marked Publications

Open Data PUB

Search this title in

Google Scholar