City Research Online

Abstract

Predicting how temporally varying phenomena will evolve over time, or in other terms forecasting, is one of the fundamental tasks in time series analysis. Prediction has gained particular importance with the advent of real time data collection activities. Although there exist several sophisticated methodologies to predict time series, the success of a predictive analysis process remains mostly dependent on whether a particular phenomena is predictable. This paper introduces a methodology where visualizations coupled with a partition-based sampling strategy informs the analyst on the predictability of time series through the communication of prediction results applied on varying parts of data. We then discuss opportunities and research directions in supporting predictive tasks through visualization and interaction.

Publication Type:	Conference or Workshop Item (Paper)
Additional Information:	© © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	School of Science & Technology > Computer Science School of Science & Technology > Computer Science > giCentre

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