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ARES: Automatic Disaggregation of Historical Data

Yang, Fan and Song, Hyun Ah and Liu, Zongge and Faloutsos, Christos and Zadorozhny, Vladimir and Sidiropoulos, Nicholas (2018) ARES: Automatic Disaggregation of Historical Data. In: The 34th IEEE International Conference on Data Engineering, 16-19 April 2018, Paris, France.

Abstract

We address the challenge of reconstructing historical counts from aggregated, possibly overlapping historical reports. For example, given the monthly and weekly sums, how can we find the daily counts of people infected with flu?
We propose an approach, called ARES (Automatic REStoration), that performs automatic data reconstruction in two phases: (1) first, it estimates the sequence of historical counts utilizing domain knowledge, such as smoothness and periodicity of historical events; (2) then, it uses the estimated sequence to learn notable patterns in the target sequence to refine the reconstructed time series. In order to derive such patterns, ARES uses an annihilating filter technique. The idea is to learn a linear shift-invariant operator whose response to the desired sequence is (approximately) zero – yielding a set of null-space equations that the desired signal should satisfy, without the need for the accompanying data. The reconstruction accuracy can be further improved by applying the second phase iteratively.
We evaluate ARES on the real epidemiological data from the Tycho project and demonstrate that ARES recovers historical data from aggregated reports with high accuracy. In particular, it considerably outperforms top competitors, including least squares approximation and the more advanced H-FUSE method (42% and 34% improvement based on average RMSE, respectively).


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Details

Item Type: Conference or Workshop Item (Paper)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Yang, Fanfay28@pitt.eduFAY28
Song, Hyun Ah
Liu, Zongge
Faloutsos, Christos
Zadorozhny, Vladimirviz@pitt.eduviz0000-0001-6420-1926
Sidiropoulos, Nicholas
Date: 2018
Date Type: Publication
Journal or Publication Title: Proceedings of the 34th IEEE International Conference on Data Engineering
Publisher: IEEE
Event Title: The 34th IEEE International Conference on Data Engineering
Event Dates: 16-19 April 2018
Event Type: Conference
Schools and Programs: School of Computing and Information > Information Science
Refereed: Yes
Related URLs:
Date Deposited: 05 Jul 2018 19:08
Last Modified: 05 Jul 2018 19:08
URI: http://d-scholarship.pitt.edu/id/eprint/34835

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