# Testing Serial Independence of Object-Valued Time Series

## TL;DR

This paper introduces a fully nonparametric, general method for testing serial independence in object-valued time series within metric spaces, utilizing distance covariance and spectral analysis.

## Contribution

It extends distance covariance to object-valued time series and develops a new spectral density-based test with bootstrap calibration, applicable beyond Euclidean spaces.

## Key findings

- Effective in detecting nonlinear dependence
- Applicable to complex, non-Euclidean data
- Validated through simulations and real data

## Abstract

We propose a novel method for testing serial independence of object-valued time series in metric spaces, which is more general than Euclidean or Hilbert spaces. The proposed method is fully nonparametric, free of tuning parameters, and can capture all nonlinear pairwise dependence. The key concept used in this paper is the distance covariance in metric spaces, which is extended to auto distance covariance for object-valued time series. Furthermore, we propose a generalized spectral density function to account for pairwise dependence at all lags and construct a Cramer-von Mises type test statistic. New theoretical arguments are developed to establish the asymptotic behavior of the test statistic. A wild bootstrap is also introduced to obtain the critical values of the non-pivotal limiting null distribution. Extensive numerical simulations and two real data applications are conducted to illustrate the effectiveness and versatility of our proposed method.