Researchers led by Miguel Araujo, of Carnegie Mellon University, developed TensorCast, a method which addresses the forecasting problem on big time-evolving datasets when contextual information is available. They demonstrated TensorCast's forecasting results and anomaly detection using Project Tycho data.
Authors
Miguel Araujo
Pedro Ribeiro
Hyun Ah Song
Christos Faloutsos
Related Project Tycho Datasets
United States of America - Acute type A viral hepatitis
United States of America - Congenital rubella syndrome
United States of America - Measles
United States of America - Mumps United States of America - Pertussis United States of America - Rubella United States of America - Viral hepatitis, type A
Abstract
Given an heterogeneous social network, can we forecast its future? Can we predict who will start using a given hashtag on twitter? Can we leverage side information, such as who retweets or follows whom, to improve our membership forecasts? We present TensorCast, a novel method that forecasts time-evolving networks more accurately than current state-of-the-art methods by incorporating multiple data sources in coupled tensors. TensorCast is (a) scalable, being linearithmic on the number of connections; (b) effective, achieving over 20% improved precision on top-1000 forecasts of community members; (c) general, being applicable to data sources with different structure. We run our method on multiple real-world networks, including DBLP, epidemiology data, power grid data, and a Twitter temporal network with over 310 million nonzeros, where we predict the evolution of the activity of the use of political hashtags.
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