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Activity Number: 556
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract #321130
Title: Modeling Temporal Dependence to Improve Learning Algorithms for Streaming Data
Author(s): Maggie Johnson* and Petrutza Caragea and Lisa Bramer and Bryan Stanfill and Sarah Reehl
Companies: Iowa State University and Iowa State University and Pacific Northwest National Laboratory and Pacific Northwest National Laboratory and Pacific Northwest National Laboratory
Keywords: streaming data ; machine learning ; time series
Abstract:

Machine learning algorithms can easily be implemented on streaming data. However, because learning algorithms have traditionally been used with static training and/or testing data sets, the temporal information included in streaming data often goes unused. In particular, many algorithms work under the strong assumption that sequential observations are independent. Leveraging the temporal information and structure into learning algorithms may help improve predictive analytic capabilities on streaming data. Applications for these methods are varied, ranging from real-time prediction of extreme hurricane events, to tracking traffic movement using GPS data, to online quality control monitoring of mass spectrometry data. We propose the use of statistical models incorporating temporal dependence to improve upon current state-of-the-art learning algorithms. We compare our results to the current methodologies to assess improvement of real-time forecasts in streaming data applications.


Authors who are presenting talks have a * after their name.

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