Visual Interpretation of Recurrent Neural Network on Multi-dimensional Time-series Forecast

Qiaomu Shen, Yanhong Wu, Yuzhe Jiang, Wei Zeng, Alexis K H LAU, A. Vilanova, Huamin Qu

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)

Abstract

Recent attempts at utilizing visual analytics to interpret Recurrent Neural Networks (RNNs) mainly focus on natural language processing (NLP) tasks that take symbolic sequences as input. However, many real-world problems like environment pollution forecasting apply RNNs on sequences of multi-dimensional data where each dimension represents an individual feature with semantic meaning such as PM 2.5 and SO 2 . RNN interpretation on multi-dimensional sequences is challenging as users need to analyze what features are important at different time steps to better understand model behavior and gain trust in prediction. This requires effective and scalable visualization methods to reveal the complex many-to-many relations between hidden units and features. In this work, we propose a visual analytics system to interpret RNNs on multi-dimensional time-series forecasts. Specifically, to provide an overview to reveal the model mechanism, we propose a technique to estimate the hidden unit response by measuring how different feature selections affect the hidden unit output distribution. We then cluster the hidden units and features based on the response embedding vectors. Finally, we propose a visual analytics system which allows users to visually explore the model behavior from the global and individual levels. We demonstrate the effectiveness of our approach with case studies using air pollutant forecast applications.
Original languageEnglish
Title of host publication2020 IEEE Pacific Visualization Symposium (PacificVis)
EditorsFabian Beck, Jinwook Seo, Chaoli Wang
PublisherInstitute of Electrical and Electronics Engineers
Pages61-70
Number of pages10
ISBN (Electronic)9781728156972
DOIs
Publication statusPublished - Jun 2020
Event13th IEEE Pacific Visualization Symposium, PacificVis 2020 - Tianjin, China
Duration: 14 Apr 202017 Apr 2020

Conference

Conference13th IEEE Pacific Visualization Symposium, PacificVis 2020
CountryChina
CityTianjin
Period14/04/2017/04/20

Keywords

  • air pollutant forecast
  • interpretable machine learning
  • multi-dimensional time series
  • recurrent neural networks

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