Research Area: Machine Learning
Prediction of response to input drivers by unmonitored entities has been recognized as one of the most important problems in many scientific problems. This problem is challenging due to the non-stationary processes that underlie the dynamics of data observations over space and time. Hence, directly transferring models from well-observed data entities to unmonitored target entity often lead to sub-optimal performance due to the shift in data distribution. This paper proposes a new meta-transfer learning framework that automatically estimates the similarity amongst entities to transfer knowledge from well-observed entities to unmonitored entities. A sequence autoencoder embeds temporal behaviors of time series data and simulations generated by traditional physics-based models. This embedding model is trained in a meta-transfer learning framework under the guidance of source-to-source transferring experiences. We tested this method in streamflow prediction for multiple river segments in the Delaware River Basin, an ecologically diverse region along the eastern coast of the United States. The experimental results demonstrate the superiority of the proposed method in predicting streamflow for unmonitored stream segments compared to a diverse set of baselines. Our method also creates meaningful similarity estimates amongst segments to guide the transfer learning process.
Keywords:
Representation learning
Biological system modeling
Transfer learning
Time series analysis
Poles and towers
Predictive models
Data models
Author(s) Name: Rahul Ghosh; Bangyan Li; Kshitij Tayal; Vipin Kumar
Journal name:
Conferrence name: IEEE International Conference on Data Mining
Publisher name: IEEE
DOI: 10.1109/ICDM54844.2022.00026
Volume Information:
