Three-way PCA of interval data for dynamic features extraction in futures market

Author

Jie, Meng

Author_Institution

Sch. of Stat., Central Univ. of Finance & Econ., Beijing

fYear

2008

fDate

2-4 July 2008

Firstpage

1083

Lastpage

1086

Abstract

By applying Symbolic Data Analysis (SDA) methods, this paper investigates the dynamic features of Copper futures market of Shanghai Futures Exchange (SHFE) during 2005 to 2006. First, we pack up mass futures contracts as their different residual time (monthly) to the expiration dates, which forms the interval symbolic data and greatly reduces the dimension of the sample space. Based on that, three-way principal component analysis (PCA) of interval data is adopted to extract the dynamic principal characteristics of Copper futures market, which reduces the dimension of the variable space. The results of the case study, which are rightly coincident with the realities, verify the application value of SDA in analyzing mass, dynamic and complex database.

Keywords

commodity trading; copper; data analysis; feature extraction; principal component analysis; Shanghai future exchange; copper future market; dynamic feature extraction; interval symbolic data analysis; three-way principal component analysis; Contracts; Copper; Data analysis; Data mining; Databases; Feature extraction; Finance; Large-scale systems; Principal component analysis; Statistical analysis; Futures Market; Interval Data; Principal Component Analysis; Symbolic Data Analysis; Three-way;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference, 2008. CCDC 2008. Chinese

Conference_Location

Yantai, Shandong

Print_ISBN

978-1-4244-1733-9

Electronic_ISBN

978-1-4244-1734-6

Type

conf

DOI

10.1109/CCDC.2008.4597480

Filename

4597480