Discussing the article: "Eigenvectors and eigenvalues: Exploratory data analysis in MetaTrader 5"
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Check out the new article: Eigenvectors and eigenvalues: Exploratory data analysis in MetaTrader 5.
In this article we explore different ways in which the eigenvectors and eigenvalues can be applied in exploratory data analysis to reveal unique relationships in data.
Principal Component Analysis (PCA) is widely known for its role in dimensionality reduction during data exploration. However, its potential extends far beyond reducing large datasets. At the core of PCA are eigenvalues and eigenvectors, which play a crucial role in uncovering hidden relationships within data. In this article, we will explore techniques that leverage eigenstructure to reveal these hidden relationships.
We will start with factor analysis, demonstrating how eigenstructure helps identify latent variables, offering a more comprehensive understanding of the data's underlying structure. By identifying latent variables, we can expose redundancies among seemingly independent variables, showing how multiple variables might simply reflect the same underlying factor. Additionally, we will examine how eigenvectors and eigenvalues can be used to assess the relationships between variables over time. By analyzing the eigenstructure of data collected at different intervals, we can gain valuable insights into the dynamic relationships between variables. Allowing us to identify variables that move in tandem or exhibit contrasting behaviour over time.
Author: Francis Dube