# Dimensionality reduction for prediction: Application to Bitcoin and Ethereum

arXiv: https://arxiv.org/abs/2112.15036 · HTML: https://mlsanity.com/paper/2112.15036/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 30 Dec 2021.

## Builds on (most cited first, 4 of 4)

- [A Tutorial on Canonical Correlation Methods](https://mlsanity.com/paper/1711.02391/) (2017)
- [Canonical Correlation Analysis (CCA) Based Multi-View Learning: An Overview](https://mlsanity.com/paper/1907.01693/) (2019)
- [Asymmetric tail dependence modeling, with application to cryptocurrency market data](https://mlsanity.com/paper/1905.05056/) (2019)
- [Robust smoothed canonical correlation analysis for functional data](https://mlsanity.com/paper/2011.10576/) (2020)

## Built on by (most cited first, 1 of 1)

- [Deep State Space Recurrent Neural Networks for Time Series Forecasting](https://mlsanity.com/paper/2407.15236/) (2024)

## Abstract (arXiv)

The objective of this paper is to assess the performances of dimensionality reduction techniques to establish a link between cryptocurrencies. We have focused our analysis on the two most traded cryptocurrencies: Bitcoin and Ethereum. To perform our analysis, we took log returns and added some covariates to build our data set. We first introduced the pearson correlation coefficient in order to have a preliminary assessment of the link between Bitcoin and Ethereum. We then reduced the dimension of our data set using canonical correlation analysis and principal component analysis. After performing an analysis of the links between Bitcoin and Ethereum with both statistical techniques, we measured their performance on forecasting Ethereum returns with Bitcoin s features.