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Abstract Details
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Activity Number:
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490
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 1, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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| Abstract - #305013 |
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Title:
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Flow Field Forecasting: An Introduction
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Author(s):
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Michael Frey*+ and Kyle A Caudle
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Companies:
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Bucknell University and South Dakota School of Mines and Technology
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Address:
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Department of Mathematics, Lewisburg, PA, 17837, United States
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Keywords:
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forecasting ;
Gaussian process regression ;
inductivism ;
penalized spline regression ;
time series
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Abstract:
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A statistical learning methodology, called flow field forecasting, is introduced for predicting the future of a univariate time series. Flow field forecasting draws information from the interpolated flow field of an observed time series to build a forecast step-by-step. Flow field forecasting is premised on the principle of inductivism. We present flow field forecasting, along with a discussion of its motivating principle, examples with comparisons to other major forecasting techniques and a statistical error analysis.
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