第二場演講：
題 目：TVICA - Time Varying Independent Component Analysis and
Its Application to Financial Data
主講人：陳瑞彬教授 (成功大學統計系)
時 間：101年2月24日(星期五)上午11:10-12:00
地 點：交大綜合一館427室
Abstract
Source extraction and dimensionality reduction are important in analyzing
high dimensional and complex financial time series that are neither Gaussian
distributed nor stationary. Independent component analysis (ICA) method can
be used to factorize the data into a linear combination of independent
components, so that the high dimensional problem is converted to a set of
univariate ones. However conventional ICA methods implicitly assume
stationarity or stochastic homogeneity of the analyzed time series, which
leads to a low accuracy of estimation in case of a changing stochastic
structure. A time varying ICA (TVICA) is proposed here. The key idea is to
allow the ICA filter to change over time, and to estimate it in so-called
local homogeneous intervals. The question of how to identify these intervals
is solved by the LCP (local change point) method. Compared to a static ICA,
the dynamic TVICA provides good performance both in simulation and real data
analysis. The data example is concerned with independent signal processing
and deals with a portfolio of highly traded stocks.
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※ 編輯: locust0923 來自: 140.113.114.147 (02/23 19:19)