# hfpca

> Principal component analysis (PCA) for high-frequency time-series data, as proposed in Aït-Sahalia and Xiu (2019).

## Docs

### API Reference

#### Main function
> Run the high-frequency PCA algorithm

- [pca_high_freq](reference/pca_high_freq.html): Run high-frequency PCA on a data matrix
- [pca_start_evectors](reference/pca_start_evectors.html): Calculate the starting eigenvectors for the high-frequency PCA routine

#### Analysis functions
> Analyze the results

- [plot_loadings](reference/plot_loadings.html): Plot the loading contribution between variables and principal components over time
- [plot_pcs](reference/plot_pcs.html): Plot the principal component trajectories colored by explained variance
- [quantiles_cut](reference/quantiles_cut.html): Compute the fraction of values above the PCA cutoff per feature