Nonparametric robust monitoring of time series panel data

TL;DR

This paper introduces a nonparametric, bootstrap-based control chart method for monitoring panel time series data with noise and missing values, using SVMs to identify deviations, demonstrated on sunspot data.

Contribution

It develops a flexible, nonparametric control scheme for complex panel data with time-varying characteristics, incorporating machine learning for deviation estimation.

Findings

Effective detection of deviations in noisy, incomplete panel data.

Successful application to solar activity sunspot observations.

Robustness to absence of in-control data and strong noise.

Abstract

In many applications, a control procedure is required to detect potential deviations in a panel of serially correlated processes. It is common that the processes are corrupted by noise and that no prior information about the in-control data are available for that purpose. This paper suggests a general nonparametric monitoring scheme for supervising such a panel with time-varying mean and variance. The method is based on a control chart designed by block bootstrap, which does not require parametric assumptions on the distribution of the data. The procedure is tailored to cope with strong noise, potentially missing values and absence of in-control series, which is tackled by an intelligent exploitation of the information in the panel. Our methodology is completed by support vector machine procedures to estimate magnitude and form of the encountered deviations (such as stepwise shifts or functional drifts). This scheme, though generic in nature, is able to treat an important applied data problem: the control of deviations in a subset of sunspot number observations which are part of the International Sunspot Number, a world reference for long-term solar activity.