TimeFork: Mixed-initiative time-series prediction

Sriram Karthik Badam; Jieqiong Zhao; Niklas Elmqvist; David S. Ebert

doi:10.1109/VAST.2014.7042501

TimeFork: Mixed-initiative time-series prediction

Sriram Karthik Badam
, Jieqiong Zhao
, Niklas Elmqvist
, David S. Ebert

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

We present TimeFork, an analytics technique for predicting the behavior of multivariate time-series data originating from modern disciplines such as economics (stock market) and meteorology (climate), with human-in-the-loop. We identify two types of machine-generated predictions for such datasets: temporal prediction that predicts the future of an attribute; and spatial prediction that predicts an attribute based on the other attributes in the dataset. Visual exploration of this prediction space, constituting of these predictions of different confidences, by chunking and chaining predictions over time promises accurate user-guided predictions. In order to utilize TimeFork technique, we created a visual analytics application for user-guided prediction over different time periods, thus allowing for visual exploration of time-series data.

Original language	English (US)
Title of host publication	2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings
Editors	Min Chen, David Ebert, Chris North
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	223-224
Number of pages	2
ISBN (Electronic)	9781479962273
DOIs	https://doi.org/10.1109/VAST.2014.7042501
State	Published - Feb 13 2015
Externally published	Yes
Event	2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Paris, France Duration: Oct 9 2014 → Oct 14 2014

Publication series

Name	2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings

Conference

Conference	2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014
Country/Territory	France
City	Paris
Period	10/9/14 → 10/14/14

ASJC Scopus subject areas

Computer Vision and Pattern Recognition
Computer Science Applications

Access to Document

10.1109/VAST.2014.7042501

Cite this

Badam, S. K., Zhao, J., Elmqvist, N., & Ebert, D. S. (2015). TimeFork: Mixed-initiative time-series prediction. In M. Chen, D. Ebert, & C. North (Eds.), 2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings (pp. 223-224). Article 7042501 (2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VAST.2014.7042501

TimeFork: Mixed-initiative time-series prediction. / Badam, Sriram Karthik; Zhao, Jieqiong; Elmqvist, Niklas et al.
2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings. ed. / Min Chen; David Ebert; Chris North. Institute of Electrical and Electronics Engineers Inc., 2015. p. 223-224 7042501 (2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Badam, SK, Zhao, J, Elmqvist, N & Ebert, DS 2015, TimeFork: Mixed-initiative time-series prediction. in M Chen, D Ebert & C North (eds), 2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings., 7042501, 2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 223-224, 2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014, Paris, France, 10/9/14. https://doi.org/10.1109/VAST.2014.7042501

Badam SK, Zhao J, Elmqvist N, Ebert DS. TimeFork: Mixed-initiative time-series prediction. In Chen M, Ebert D, North C, editors, 2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2015. p. 223-224. 7042501. (2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings). doi: 10.1109/VAST.2014.7042501

Badam, Sriram Karthik ; Zhao, Jieqiong ; Elmqvist, Niklas et al. / TimeFork : Mixed-initiative time-series prediction. 2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings. editor / Min Chen ; David Ebert ; Chris North. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 223-224 (2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings).

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title = "TimeFork: Mixed-initiative time-series prediction",

abstract = "We present TimeFork, an analytics technique for predicting the behavior of multivariate time-series data originating from modern disciplines such as economics (stock market) and meteorology (climate), with human-in-the-loop. We identify two types of machine-generated predictions for such datasets: temporal prediction that predicts the future of an attribute; and spatial prediction that predicts an attribute based on the other attributes in the dataset. Visual exploration of this prediction space, constituting of these predictions of different confidences, by chunking and chaining predictions over time promises accurate user-guided predictions. In order to utilize TimeFork technique, we created a visual analytics application for user-guided prediction over different time periods, thus allowing for visual exploration of time-series data.",

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AB - We present TimeFork, an analytics technique for predicting the behavior of multivariate time-series data originating from modern disciplines such as economics (stock market) and meteorology (climate), with human-in-the-loop. We identify two types of machine-generated predictions for such datasets: temporal prediction that predicts the future of an attribute; and spatial prediction that predicts an attribute based on the other attributes in the dataset. Visual exploration of this prediction space, constituting of these predictions of different confidences, by chunking and chaining predictions over time promises accurate user-guided predictions. In order to utilize TimeFork technique, we created a visual analytics application for user-guided prediction over different time periods, thus allowing for visual exploration of time-series data.

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TimeFork: Mixed-initiative time-series prediction

Abstract

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