Visual Exploration of Time-Series Forecasts through Structured Navigation

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

Visual Exploration of Time-Series Forecasts through Structured Navigation. / Wang, Xiaoyi; Hornbæk, Kasper.

Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 2020. ed. / Genny Tortora; Giuliana Vitiello; Marco Winckler. Association for Computing Machinery, 2020. p. 1-9 38 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Wang, X & Hornbæk, K 2020, Visual Exploration of Time-Series Forecasts through Structured Navigation. in G Tortora, G Vitiello & M Winckler (eds), Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 2020., 38, Association for Computing Machinery, ACM International Conference Proceeding Series, pp. 1-9, 2020 International Conference on Advanced Visual Interfaces, AVI 2020, Salerno, Italy, 28/09/2020. https://doi.org/10.1145/3399715.3399906

APA

Wang, X., & Hornbæk, K. (2020). Visual Exploration of Time-Series Forecasts through Structured Navigation. In G. Tortora, G. Vitiello, & M. Winckler (Eds.), Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 2020 (pp. 1-9). [38] Association for Computing Machinery. ACM International Conference Proceeding Series https://doi.org/10.1145/3399715.3399906

Vancouver

Wang X, Hornbæk K. Visual Exploration of Time-Series Forecasts through Structured Navigation. In Tortora G, Vitiello G, Winckler M, editors, Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 2020. Association for Computing Machinery. 2020. p. 1-9. 38. (ACM International Conference Proceeding Series). https://doi.org/10.1145/3399715.3399906

Author

Wang, Xiaoyi ; Hornbæk, Kasper. / Visual Exploration of Time-Series Forecasts through Structured Navigation. Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 2020. editor / Genny Tortora ; Giuliana Vitiello ; Marco Winckler. Association for Computing Machinery, 2020. pp. 1-9 (ACM International Conference Proceeding Series).

Bibtex

@inproceedings{8185a1bc6f3d447288a7ccfedbefdf75,
title = "Visual Exploration of Time-Series Forecasts through Structured Navigation",
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. ",
keywords = "model evaluation, navigation, time series",
author = "Xiaoyi Wang and Kasper Hornb{\ae}k",
year = "2020",
doi = "10.1145/3399715.3399906",
language = "English",
series = "ACM International Conference Proceeding Series",
pages = "1--9",
editor = "Genny Tortora and Giuliana Vitiello and Marco Winckler",
booktitle = "Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 2020",
publisher = "Association for Computing Machinery",
note = "2020 International Conference on Advanced Visual Interfaces, AVI 2020 ; Conference date: 28-09-2020 Through 02-10-2020",

}

RIS

TY - GEN

T1 - Visual Exploration of Time-Series Forecasts through Structured Navigation

AU - Wang, Xiaoyi

AU - Hornbæk, Kasper

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - model evaluation

KW - navigation

KW - time series

U2 - 10.1145/3399715.3399906

DO - 10.1145/3399715.3399906

M3 - Article in proceedings

AN - SCOPUS:85093068943

T3 - ACM International Conference Proceeding Series

SP - 1

EP - 9

BT - Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 2020

A2 - Tortora, Genny

A2 - Vitiello, Giuliana

A2 - Winckler, Marco

PB - Association for Computing Machinery

T2 - 2020 International Conference on Advanced Visual Interfaces, AVI 2020

Y2 - 28 September 2020 through 2 October 2020

ER -

ID: 258325738