H-Fuse: Efficient Fusion of Aggregated Historical Data
H-Fuse: Efficient Fusion of Aggregated Historical Data
复制标题
H-Fuse：聚合历史数据的高效融合
DOI：
10.1137/1.9781611974973.88
复制
发表时间：
2017
影响因子：
1
通讯作者：
N. Sidiropoulos
中科院分区：
文献类型：
--
作者：
Zongge Liu;H. Song;V. Zadorozhny;C. Faloutsos;N. Sidiropoulos
版权所有©在本文中，我们解决了从总体数据中恢复时间序列的挑战。一般而言，在缺失价值观的情况下，从总体上恢复历史的最佳方法是什么？重建？我们提出了H-Fuse，一种新的方法，可以通过主要的方式注入域知识，并将任务变成明确的优化问题。 ，以高精度从汇总报告中恢复历史数据；关于真实数据的实验（流行病学从Tycho Project [13]计数）表明，H -Fuse重建原始数据30-81％比最小二乘方法好。
Copyright © by SIAM. In this paper, we address the challenge of recovering a time sequence of counts from aggregated historical data. For example, given a mixture of the monthly and weekly sums, how can we find the daily counts of people infected with flu? In general, what is the best way to recover historical counts from aggregated, possibly overlapping historical reports, in the presence of missing values? Equally importantly, how much should we trust this reconstruction? We propose H-FUSE, a novel method that solves above problems by allowing injection of domain knowledge in a principled way, and turning the task into a welldefined optimization problem. H-FUSE has the following desirable properties: (a) Effectiveness, recovering historical data from aggregated reports with high accuracy; (b) Self-awareness, providing an assessment of when the recovery is not reliable; (c) Scalability, computationally linear on the size of the input data. Experiments on the real data (epidemiology counts from the Tycho project [13]) demonstrates that H-FUSE reconstructs the original data 30 - 81% better than the least squares method.
DOI：
10.1056/nejmms1215400
发表时间：
2013-11-28
期刊：
The New England journal of medicine
影响因子：
--
作者：
van Panhuis WG;Grefenstette J;Jung SY;Chok NS;Cross A;Eng H;Lee BY;Zadorozhny V;Brown S;Cummings D;Burke DS
通讯作者：
Burke DS