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Activity Number: 547
Type: Contributed
Date/Time: Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract #316971 View Presentation
Title: Use of Flow Field Forecasting for Bivariate Responses
Author(s): Kyle Caudle* and Michael Frey and Patrick Fleming
Companies: South Dakota School of Mines and Technology and Bucknell University and South Dakota School of Mines and Technology
Keywords: Forecasting ; Machine Learning ; Gaussian Process Regression
Abstract:

Flow field forecasting was first developed in 2011 as a method to forecast a univariate time series. The original version of flow field forecasting which is available on the Comprehensive R Archive Network (CRAN) was shown to be a competitive alternative to Box-Jenkins ARIMA, exponential smoothing and neural networks. Flow field forecasting has several very nice features such as, (1) reduction of historical archived data, (2) autonomous operation, and (3) computational efficiency. This talk will focus on the next version of flow field forecasting which will forecast a bivariate response (e.g. latitude, longitude). Other advancements to be touched on will be the inclusion of external environmental factors such as weather and geography.


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

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