# An Updated Literature Review of Distance Correlation and its Applications to Time Series

arXiv: https://arxiv.org/abs/1710.01146 · HTML: https://mlsanity.com/paper/1710.01146/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2017.

## Built on by (most cited first, 2 of 2)

- [A Distance Correlation-Based Approach to Characterize the Effectiveness of Recurrent Neural Networks for Time Series Forecasting](https://mlsanity.com/paper/2307.15830/) (2023)
- [Measuring Statistical Dependencies via Maximum Norm and Characteristic Functions](https://mlsanity.com/paper/2208.07934/) (2022)

## Abstract (arXiv)

The concept of distance covariance/correlation was introduced recently to characterize dependence among vectors of random variables. We review some statistical aspects of distance covariance/correlation function and we demonstrate its applicability to time series analysis. We will see that the auto-distance covariance/correlation function is able to identify nonlinear relationships and can be employed for testing the i.i.d. hypothesis. Comparisons with other measures of dependence are included.