More Web Proxy on the site http://driver.im/

research-article

TimeFork: Interactive Prediction of Time Series

Authors:

Sriram Karthik Badam,

Niklas Elmqvist,

David EbertAuthors Info & Claims

CHI '16: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

Pages 5409 - 5420

https://doi.org/10.1145/2858036.2858150

Published: 07 May 2016 Publication History

Abstract

We present TimeFork, an interactive prediction technique to support users predicting the future of time-series data, such as in financial, scientific, or medical domains. TimeFork combines visual representations of multiple time series with prediction information generated by computational models. Using this method, analysts engage in a back-and-forth dialogue with the computational model by alternating between manually predicting future changes through interaction and letting the model automatically determine the most likely outcomes, to eventually come to a common prediction using the model. This computer-supported prediction approach allows for harnessing the user's knowledge of factors influencing future behavior, as well as sophisticated computational models drawing on past performance. To validate the TimeFork technique, we conducted a user study in a stock market prediction game. We present evidence of improved performance for participants using TimeFork compared to fully manual or fully automatic predictions, and characterize qualitative usage patterns observed during the user study.

Supplementary Material

ZIP File (pn0679-file4.zip)

pn0679-file4.zip

Download
229.06 KB

suppl.mov (pn0679-file3.mp4)

Supplemental video

Download
44.16 MB

References

[1]

Heather Arthur. 2013. Brain Node.js Library. (2013). https://www.npmjs.com/package/brain, accessed December 2015.

[2]

Guilherme A. Barreto. 2007. Time series prediction with the self-organizing map: A review. In Perspectives of Neural-Symbolic Integration. Springer, 135-158.

[3]

Johan Bollen, Huina Mao, and Xiaojun Zeng. 2011. Twitter mood predicts the stock market. Journal of Computational Science 2, 1 (2011), 1-8.

[4]

John Bollinger. 2001. Bollinger On Bollinger Band. (2001).

[5]

Michael Bostock, Vadim Ogievetsky, and Jeffrey Heer. 2011. D3 : Data-Driven Documents. IEEE Transactions on Visualization and Computer Graphics 17, 12 (2011), 2301-2309.

Digital Library

[6]

Paolo Buono, Aleks Aris, Catherine Plaisant, Amir Khella, and Ben Shneiderman. 2005. Interactive pattern search in time series. In Proceedings of SPIE Electronic Imaging. 175-186.

[7]

Paolo Buono, Catherine Plaisant, Adalberto Simeone, Aleks Aris, Ben Shneiderman, Galit Shmueli, and Wolfgang Jank. 2007. Similarity-based forecasting with simultaneous previews: A river plot interface for time series forecasting. In Proceedings of the International Conference on Information Visualization. 191-196.

Digital Library

[8]

Philip K. Chan and Matthew V. Mahoney. 2005. Modeling multiple time series for anomaly detection. In Proceedings of the IEEE International Conference on Data Mining. 90-97.

Digital Library

[9]

Bill Chiu, Eamonn Keogh, and Stefano Lonardi. 2003. Probabilistic discovery of time series motifs. In Proceedings of the ACM International Conference on Knowledge Discovery and Data Mining. 493-498.

Digital Library

[10]

Herbert H Clark. 1973. The language-as-fixed-effect fallacy: A critique of language statistics in psychological research. Journal of verbal learning and verbal behavior 12, 4 (1973), 335-359.

[11]

Nick Desaulniers. 2013. Historic Node.js Library. (2013). https://www.npmjs.com/package/historic, accessed December 2015.

[12]

Alex Endert, Patrick Fiaux, and Chris North. 2012. Semantic interaction for visual text analytics. In Proceedings of the ACM Conference on Human Factors in Computing Systems. 473-482.

Digital Library

[13]

Tak-chung Fu. 2011. A review on time series data mining. Engineering Applications of Artificial Intelligence 24, 1 (2011), 164-181.

Digital Library

[14]

Tak-chung Fu, Fu-lai Chung, Ka-yan Kwok, and Chak-man Ng. 2008. Stock time series visualization based on data point importance. Engineering Applications of Artificial Intelligence 21, 8 (2008), 1217-1232.

Digital Library

[15]

Spence Green, Jeffrey Heer, and Christopher D Manning. 2013. The efficacy of human post-editing for language translation. In Proceedings of the ACM Conference on Human Factors in Computing Systems. 439-448.

Digital Library

[16]

Ming Hao, Halldor Janetzko, Ratnesh Sharma, Umeshwar Dayal, Daniel Keim, and Malu Castellanos. 2009. Poster: visual prediction of time series. In Poster Compendium of the IEEE Symposium on Visual Analytics Science and Technology.

[17]

Ming C. Hao, Umeshwar Dayal, Daniel A. Keim, and Tobias Schreck. 2007. Multi-resolution techniques for visual exploration of large time-series data. In Proceedings of the Joint Eurographics / IEEE VGTC Conference on Visualization. 27-34.

Digital Library

[18]

Ming C. Hao, Halldor Janetzko, Sebastian Mittelstadt, Water Hill, Umeshwar Dayal, Daniel A. Keim, Manish Marwah, and Ratnesh K. Sharma. 2011. A Visual Analytics Approach for Peak-Preserving Prediction of Large Seasonal Time Series. In Computer Graphics Forum, Vol. 30. 691-700.

Digital Library

[19]

Jeffrey Heer, Michael Bostock, and Vadim Ogievetsky. 2010. A tour through the visualization zoo. Communications of the ACM 53, 6 (2010), 59-67.

Digital Library

[20]

Jeffrey Heer, Nicholas Kong, and Maneesh Agrawala. 2009. Sizing the horizon: the effects of chart size and layering on the graphical perception of time series visualizations. In Proceedings of the ACM Conference on Human Factors in Computing Systems. 1303-1312.

Digital Library

[21]

Magnus Lie Hetland and Pål Sætrom. 2005. Evolutionary rule mining in time series databases. Machine Learning 58, 2--3 (2005), 107-125.

Digital Library

[22]

Harry Hochheiser and Ben Shneiderman. 2004. Dynamic query tools for time series data sets: timebox widgets for interactive exploration. Information Visualization 3, 1 (2004), 1-18.

Digital Library

[23]

Eric Horvitz. 1999. Principles of mixed-initiative user interfaces. In Proceedings of the ACM Conference on Human Factors in Computing Systems. 159-166.

Digital Library

[24]

Eric J. Horvitz. 2007. Reflections on challenges and promises of mixed-initiative interaction. AI Magazine 28, 2 (2007), 3.

[25]

Waqas Javed, Bryan McDonnel, and Niklas Elmqvist. 2010. Graphical perception of multiple time series. IEEE Transactions on Visualization and Computer Graphics 16, 6 (2010), 927-934.

Digital Library

[26]

Young-Seon Jeong, Myong K. Jeong, and Olufemi A. Omitaomu. 2011. Weighted dynamic time warping for time series classification. Pattern Recognition 44, 9 (2011), 2231-2240.

Digital Library

[27]

Ashish Kapoor, Bongshin Lee, Desney S. Tan, and Eric Horvitz. 2012. Performance and Preferences: Interactive Refinement of Machine Learning Procedures. In Proceedings of the AAAI Conference on Artificial Intelligence.

Digital Library

[28]

Daniel Keim, Florian Mansmann, Jorn Schneidewind, and Hartmut Ziegler. 2006. Challenges in visual data analysis. In Proceedings of the International Conference on Information Visualization. 9-16.

Digital Library

[29]

Teuvo Kohonen. 2001. Self-Organizing Maps. Springer.

Digital Library

[30]

T. Warren Liao. 2005. Clustering of time series data--a survey. Pattern Recognition 38, 11 (2005), 1857-1874.

Digital Library

[31]

Jessica Lin, Eamonn Keogh, and Stefano Lonardi. 2005. Visualizing and discovering non-trivial patterns in large time series databases. Information Visualization 4, 2 (2005), 61-82.

Digital Library

[32]

Andrew C. Lorenc. 1986. Analysis methods for numerical weather prediction. Royal Meteorological Society, Quarterly Journal 112 (1986), 1177-1194.

[33]

Junshui Ma and Simon Perkins. 2003. Online novelty detection on temporal sequences. In Proceedings of the ACM International Conference on Knowledge Discovery and Data Mining. 613-618.

Digital Library

[34]

Abish Malik, Ross Maciejewski, Yun Jang, Whitney Huang, Niklas Elmqvist, and David Ebert. 2012. A Correlative Analysis Process in a Visual Analytics Environment. In Proceedings of the IEEE Conference on Visual Analytics Science and Technology. 33-42.

Digital Library

[35]

Andrew Vande Moere. 2004. Time-varying data visualization using information flocking boids. In Proceedings of the IEEE Symposium on Information Visualization. 97-104.

Digital Library

[36]

Steve Nison. 2001. Japanese candlestick charting techniques: a contemporary guide to the ancient investment techniques of the Far East. Penguin.

[37]

Ajoy K. Palit and Dobrivoje Popovic. 2006. Computational intelligence in time series forecasting: theory and engineering applications. Springer Science & Business Media.

Digital Library

[38]

Predictive Analytics for Movie Openings 2013. VAST Mini Challenge 1: Visualize the box office. (2013). http://vacommunity.org/VAST+Challenge+2013, accessed December 2015.

[39]

Glenn Proctor and Chris Winter. 1998. Information flocking: Data visualisation in virtual worlds using emergent behaviours. In Virtual Worlds. Springer, 168-176.

[40]

Tobias Schreck, Tatiana Tekusova, Jorn Kohlhammer, and Dieter Fellner. 2007. Trajectory-based visual analysis of large financial time series data. ACM SIGKDD Explorations Newsletter 9, 2 (2007), 30-37.

Digital Library

[41]

Shobhit Seth. 2014. The Best Technical Analysis Trading Software. (2014). http://www.investopedia. com/articles/active-trading/121014/best-technical-analysis-trading-software.asp?header_alt=c, accessed December 2015.

[42]

Ben Shneiderman. 1996. The eyes have it: A task by data type taxonomy for information visualizations. In Proceedings of the IEEE Symposium on Visual Languages. 336-343.

Digital Library

[43]

Ben Shneiderman and Pattie Maes. 1997. Direct manipulation vs. interface agents: A debate. Interactions 4, 6 (1997), 42-61.

Digital Library

[44]

K. Srinivas, B. Kavihta Rani, and A. Govrdhan. 2010. Applications of data mining techniques in healthcare and prediction of heart attacks. International Journal on Computer Science and Engineering 2, 02 (2010), 250-255.

[45]

Yoshiki Tanaka, Kazuhisa Iwamoto, and Kuniaki Uehara. 2005. Discovery of time-series motif from multi-dimensional data based on MDL principle. Machine Learning 58, 2 (2005), 269-300.

Digital Library

[46]

James J. Thomas and Kristin A. Cook. 2005. Illuminating the Path: The Research and Development Agenda for Visual Analytics. IEEE Computer Society.

[47]

Jarke J. Van Wijk and Edward R. Van Selow. 1999. Cluster and calendar based visualization of time series data. In Proceedings of IEEE Symposium on Information Visualization. 4-9.

Digital Library

[48]

K. R. Venugopal, K. G. Srinivasa, and L. M. Patnaik. 2009. Fuzzy Based Neuro - Genetic Algorithm for Stock Market Prediction. In Soft Computing for Data Mining Applications. 139-166.

[49]

Marc Weber, Marc Alexa, and Wolfgang Muller. 2001. Visualizing Time-Series on Spirals. In Proceedings of IEEE Symposium on Information Visualization. 7-14.

Digital Library

[50]

Li Wei, Nitin Kumar, Venkata Lolla, Eamonn J. Keogh, Stefano Lonardi, and Chotirat Ratanamahatana. 2005. Assumption-Free Anomaly Detection in Time Series. In Proceedings of the International Conference on Scientific and Statistical Database Management. 237-242.

Digital Library

[51]

Michael Zasso. 2014. ML-SOM Node.js Library. (2014). https://www.npmjs.com/package/ml-som, accessed December 2015.

[52]

Jian Zhao, Fanny Chevalier, and Ravin Balakrishnan. 2011a. KronoMiner: using multi-foci navigation for the visual exploration of time-series data. In Proceedings of the ACM Conference on Human Factors in Computing Systems. 1737-1746.

Digital Library

[53]

Jian Zhao, Fanny Chevalier, Emmanuel Pietriga, and Ravin Balakrishnan. 2011b. Exploratory analysis of time-series with Chrono Lenses. IEEE Transactions on Visualization and Computer Graphics 17, 12 (2011), 2422-2431.

Digital Library

Cited By

Das Antar AMolaei SChen YLee MBanovic N(2024)VIME: Visual Interactive Model Explorer for Identifying Capabilities and Limitations of Machine Learning Models for Sequential Decision-MakingProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676323(1-21)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3654777.3676323
Sun YXiong HYiu SLam K(2023)BitAnalysis: A Visualization System for Bitcoin Wallet InvestigationIEEE Transactions on Big Data10.1109/TBDATA.2022.31886609:2(621-636)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TBDATA.2022.3188660
Secco CSina LBlazevic MNazemi K(2023)Visual Analytics for Forecasting Technological Trends from Text2023 27th International Conference Information Visualisation (IV)10.1109/IV60283.2023.00051(251-258)Online publication date: 25-Jul-2023
https://doi.org/10.1109/IV60283.2023.00051
Show More Cited By

Index Terms

TimeFork: Interactive Prediction of Time Series
1. Computing methodologies
  1. Computer graphics
2. Human-centered computing
  1. Human computer interaction (HCI)

Recommendations

SMP Challenge: An Overview of Social Media Prediction Challenge 2019
MM '19: Proceedings of the 27th ACM International Conference on Multimedia

"SMP Challenge" aims to discover novel prediction tasks for numerous data on social multimedia and seek excellent research teams. Making predictions via social multimedia data (e.g. photos, videos or news) is not only helps us to make better strategic ...
Multistep-Ahead Prediction of Time Series Using Independent Models
ICEICE '12: Proceedings of the 2012 Second International Conference on Electric Information and Control Engineering - Volume 02

Multi step-ahead prediction of time series is widely used in real applications, such as finance, transportation and electrical load. A multi step-ahead independent prediction approach of time series is proposed. The step-by-step recurrent approach and ...
A pattern fusion model for multi-step-ahead CPU load prediction

In distributed systems, resource prediction is an important but difficult topic. In many cases, multiple prediction is needed rather than only performing prediction at a single future point in time. However, traditional approaches are not sufficient for ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

CHI '16: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

May 2016

6108 pages

ISBN:9781450333627

DOI:10.1145/2858036

General Chairs:
Jofish Kaye
Yahoo
,
Allison Druin
University of Maryland / National Park Service
,
Program Chairs:
Cliff Lampe
University of Michigan
,
Dan Morris
Microsoft
,
Juan Pablo Hourcade
University of Iowa

Copyright © 2016 ACM.

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]

Sponsors

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 May 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CHI'16

Sponsor:

SIGCHI

CHI'16: CHI Conference on Human Factors in Computing Systems

May 7 - 12, 2016

California, San Jose, USA

Acceptance Rates

CHI '16 Paper Acceptance Rate 565 of 2,435 submissions, 23%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

Upcoming Conference

CHI 2025

Sponsor:
sigchi

ACM CHI Conference on Human Factors in Computing Systems

April 26 - May 1, 2025

Yokohama , Japan

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
542
Total Downloads

Downloads (Last 12 months)33
Downloads (Last 6 weeks)0

Reflects downloads up to 02 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Das Antar AMolaei SChen YLee MBanovic N(2024)VIME: Visual Interactive Model Explorer for Identifying Capabilities and Limitations of Machine Learning Models for Sequential Decision-MakingProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676323(1-21)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3654777.3676323
Sun YXiong HYiu SLam K(2023)BitAnalysis: A Visualization System for Bitcoin Wallet InvestigationIEEE Transactions on Big Data10.1109/TBDATA.2022.31886609:2(621-636)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TBDATA.2022.3188660
Secco CSina LBlazevic MNazemi K(2023)Visual Analytics for Forecasting Technological Trends from Text2023 27th International Conference Information Visualisation (IV)10.1109/IV60283.2023.00051(251-258)Online publication date: 25-Jul-2023
https://doi.org/10.1109/IV60283.2023.00051
Ooge JVerbert K(2022)Visually Explaining Uncertain Price Predictions in Agrifood: A User-Centred Case-StudyAgriculture10.3390/agriculture1207102412:7(1024)Online publication date: 14-Jul-2022
https://doi.org/10.3390/agriculture12071024
Krake TKlotzl DEberhardt BWeiskopf D(2022)Constrained Dynamic Mode DecompositionIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.3209437(1-11)Online publication date: 2022
https://doi.org/10.1109/TVCG.2022.3209437
Prakash SUnnikrishnan VPryss RKraft RSchobel JHannemann RLangguth BSchlee WSpiliopoulou M(2021)Interactive System for Similarity-Based Inspection and Assessment of the Well-Being of mHealth UsersEntropy10.3390/e2312169523:12(1695)Online publication date: 17-Dec-2021
https://doi.org/10.3390/e23121695
Wambecke JBonneau GBlanch RVergne RRink KZeckzer DBujack RJänicke S(2018)What if we use the "what if" approach for eco-feedback?Proceedings of the Workshop on Visualisation in Environmental Sciences10.5555/3310180.3310193(73-80)Online publication date: 4-Jun-2018
https://dl.acm.org/doi/10.5555/3310180.3310193
Lu YGarcia RHansen BGleicher MMaciejewski R(2017)The State-of-the-Art in Predictive Visual AnalyticsComputer Graphics Forum10.1111/cgf.1321036:3(539-562)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1111/cgf.13210

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten