Visualising Deep Network's Time-Series Representations

TL;DR

This paper introduces a visualization method for deep learning models that effectively displays how multi-dimensional time-series data is represented internally, aiding interpretability especially for large datasets like stock market data.

Contribution

The paper presents a novel visualization technique specifically designed for deep networks handling multi-dimensional time-series data, enabling quick and comprehensive insights into learned representations.

Findings

Method provides fast, discernible visualizations.

Large datasets can be visualized on a single plot.

Successfully applied to stock market data.

Abstract

Despite the popularisation of machine learning models, more often than not, they still operate as black boxes with no insight into what is happening inside the model. There exist a few methods that allow to visualise and explain why a model has made a certain prediction. Those methods, however, allow visualisation of the link between the input and output of the model without presenting how the model learns to represent the data used to train the model as whole. In this paper, a method that addresses that issue is proposed, with a focus on visualising multi-dimensional time-series data. Experiments on a high-frequency stock market dataset show that the method provides fast and discernible visualisations. Large datasets can be visualised quickly and on one plot, which makes it easy for a user to compare the learned representations of the data. The developed method successfully combines known techniques to provide an insight into the inner workings of time-series classification models.