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.
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
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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