迭代与指数加权移动主成分分析

ERN: Econometric Software (Topic) Pub Date : 2021-08-30 DOI:10.2139/ssrn.3913940

Paul Bilokon, David Finkelstein

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引用次数: 0

摘要

主成分分析(PCA)是金融领域主要的统计和无监督机器学习技术。PCA在金融环境中的应用有几个困难，如数值不稳定性和非平稳性。我们试图通过提出两种新的PCA变体来解决这些问题:迭代主成分分析(IPCA)和指数加权移动主成分分析(EWMPCA)。这两种变体都依赖于Ogita-Aishima迭代作为关键步骤。

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Iterated and Exponentially Weighted Moving Principal Component Analysis

The principal component analysis (PCA) is a staple statistical and unsupervised machine learning technique in finance. The application of PCA in a financial setting is associated with several difficulties, such as numerical instability and nonstationarity. We attempt to resolve them by proposing two new variants of PCA: an iterated principal component analysis (IPCA) and an exponentially weighted moving principal component analysis (EWMPCA). Both variants rely on the Ogita-Aishima iteration as a crucial step.

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ERN: Econometric Software (Topic)

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