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Visual Exploration of Time-Series Forecasts Through Structured Navigation

Published: 02 October 2020 Publication History

Abstract

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.

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cover image ACM Other conferences
AVI '20: Proceedings of the 2020 International Conference on Advanced Visual Interfaces
September 2020
613 pages
ISBN:9781450375351
DOI:10.1145/3399715
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 October 2020

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Author Tags

  1. model evaluation
  2. navigation
  3. time series

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  • Research-article
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  • Refereed limited

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  • BIOPRO2

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AVI '20
AVI '20: International Conference on Advanced Visual Interfaces
September 28 - October 2, 2020
Salerno, Italy

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AVI '20 Paper Acceptance Rate 36 of 123 submissions, 29%;
Overall Acceptance Rate 128 of 490 submissions, 26%

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