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Activity Number:
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250
- Topics in Statistical Learning
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2018 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #327281
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Presentation
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Title:
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Predicting Disease Incidence with Natural Cubic Splines
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Author(s):
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Noah Kochanski* and Yew-Meng Koh
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Companies:
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Hope College and Hope College
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Keywords:
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Cross validation;
Smoothing splines;
Time series;
Dengue fever
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Abstract:
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High-degree polynomials provide great flexibility and potentially perfect fit of historical time series data. Such flexibility, however, often leads to overfitting and results in models with poor predictive performance. Splines are a low-degree polynomial smoothing method which reduces these overfitting effects. We use a cross-validation method for time series in order to compare the performance of various models which utilize smoothing splines with regard to their forecast accuracy of Singaporean dengue fever counts.
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Authors who are presenting talks have a * after their name.
