Iterated and exponentially weighted moving principal component analysis
| Autor: | Paul Bilokon, David Finkelstein |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
History
Statistical Finance (q-fin.ST) Polymers and Plastics Computer science business.industry Quantitative Finance - Statistical Finance Pattern recognition Industrial and Manufacturing Engineering FOS: Economics and business Exponential growth Iterated function Principal component analysis Unsupervised learning Artificial intelligence Business and International Management business Numerical stability |
| Popis: | 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 technical 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. 9 pages, 5 figures |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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