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Wednesday, July 5 • 11:36am - 11:54am
Curve Linear Regression with **clr**

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Keywords: Dimension reduction, Correlation dimension, Singular value decomposition, Load forecasting
We present a new R package for curve linear regression: the clr package.
This package implements a new methodology for linear regression with both curve response and curve regressors, which is described in Cho et al. (2013) and Cho et al. (2015).
The key idea behind this methodology is dimension reduction based on a singular value decomposition in a Hilbert Space, which reduces the curve regression problem to several scalar linear regression problems.
We apply curve linear regression with clr to model and forecast daily electricity loads.
References Bathia, N., Q. Yao, and F. Ziegelmann. 2010. “Identifying the Finite Dimensionality of Curve Time Series.” The Annals of Statistics 38: 3352–86.

Cho, H., Y. Goude, X. Brossat, and Q. Yao. 2013. “Modelling and Forecasting Daily Electricity Load Curves: A Hybrid Approach.” Journal of the American Statistical Association 108: 7–21.

———. 2015. “Modelling and Forecasting Daily Electricity Load via Curve Linear Regression.” In Modeling and Stochastic Learning for Forecasting in High Dimension, edited by Anestis Antoniadis and Xavier Brossat, 35–54. Springer.

Fan, J., and Q. Yao. 2003. Nonlinear Time Series: Nonparametric and Parametric Methods. Springer.

Hall, P., and J. L. Horowitz. 2007. “Methodology and Convergence Rates for Functional Linear Regression.” The Annals of Statistics 35: 70–91.




Speakers


Wednesday July 5, 2017 11:36am - 11:54am
2.01 Wild Gallery

Attendees (97)