[2N4-GS-10] AI application

[2N4-GS-10-05] End-to-End Embedding Models for Time Series and Stationary Data

〇Yuya Inaba¹, Koji Eguchi¹ (1. Hiroshima University)

Keywords:Multivariate Time Series Analysis, Financial Informatics

This work aims to construct a classification model based on embeddings of multivariate time series in order to extract features more effectively from a dataset with a mixture of time-series and stationary data.
The model is based on the Graph Deviation Network (GDN) and learns an embedding representation of each time-series attribute while considering the interdependence of multiple time-series attributes.
The GDN-applied model is constructed as an End-to-End model with transfer learning in the pretraining and fine-tuning framework in order to "learn embedded representations of time-series attributes" and "learn a classifier considering stationary attributes" in a series of learning processes.
In comparison with several conventional methods, the proposed GDN-applied model outperforms the conventional method (gradient boosting) in prediction accuracy, demonstrating the usefulness of the GDN-applied model.
In an additional experiment with class imbalance, replacing the loss function of the GDN-applied model from Cross Entropy to Dice Loss resulted in an improvement of effectiveness, indicating that the application of Dice Loss to the GDN-applied model is effective in dealing with class imbalance.