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Scaled PCA : A New Approach to Dimension Reduction

This paper proposes a novel supervised learning technique for forecasting: scaled principal component analysis (sPCA). The sPCA improves the traditional principal component analysis (PCA) by scaling each predictor with its predictive slope on the target to be forecasted. Unlike the PCA that maximizes the common variation of the predictors, the sPCA assigns more weight to those predictors with stronger forecasting power. In a general factor framework, we show that, under some appropriate conditions on data, the sPCA forecast beats the PCA forecast, and when these conditions break down, extensive simulations indicate that the sPCA still has a large chance to outperform the PCA. A real data example on macroeconomic forecasting shows that the sPCA has better performance in general.

Ketersediaan

Informasi Detail

Judul Seri

MANAGEMENT SCIENCE; Vol.68 No.3 March 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 1678-1695

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

FORECASTING
MACHINE LEARNING
PCA
DIMENSION REDUCTION
BIG DATA

Info Detail Spesifik

https://doi.org/10.1287/mnsc.2021.4020

Pernyataan Tanggungjawab

Dashan Huang, Fuwei Jiang, Kunpeng Li, Guoshi Tong, Guofu Zhou

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