Visual Exploration of Time-Series Forecasts through Structured Navigation

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Evaluating the forecasting ability of time-series involves observations of multiple charts representing different aspects of model accuracy. However, the sequence of the charts observed by users is not controlled and it is difficult for users to discover relations among charts. Therefore, we propose a method for constructing a navigation structure that shows these relations based on the syntax and semantics of the charts. An excerpt from the structure is used as a context menu that allows users to navigate through a series of charts and explore their relations in a structured way. A qualitative study is conducted to evaluate the system and the results show that our approach helps users explore the connections among charts and enhances the understanding of time-series forecasting performance.

Original languageEnglish
Title of host publicationProceedings of the Working Conference on Advanced Visual Interfaces, AVI 2020
EditorsGenny Tortora, Giuliana Vitiello, Marco Winckler
PublisherAssociation for Computing Machinery
Publication date2020
Article number38
ISBN (Electronic)9781450375351
Publication statusPublished - 2020
Event2020 International Conference on Advanced Visual Interfaces, AVI 2020 - Salerno, Italy
Duration: 28 Sep 20202 Oct 2020


Conference2020 International Conference on Advanced Visual Interfaces, AVI 2020
SponsorACM Special Interest Group on Computer-Human Interaction (SIGCHI), ACM Special Interest Group on Hypertext, Hypermedia, and Web (SIGWEB), ACM Special Interest Group on Multimedia (SIGMM), Association for Computing Machinery (ACM)
SeriesACM International Conference Proceeding Series

    Research areas

  • model evaluation, navigation, time series

ID: 258325738