Robust Nonparametric Regression via Sparsity Control with Application to Load Curve Data Cleansing

TL;DR

This paper introduces a robust nonparametric regression method that uses sparsity control to effectively identify and mitigate outliers, demonstrated on load curve data cleansing for smart grids.

Contribution

It develops a variational framework linking outlier detection with L0-regularized estimators and proposes convex relaxation techniques for robust nonparametric regression.

Findings

Effective outlier detection in load data

Reduced bias with nonconvex surrogate

Improved load curve cleansing performance

Abstract

Nonparametric methods are widely applicable to statistical inference problems, since they rely on a few modeling assumptions. In this context, the fresh look advocated here permeates benefits from variable selection and compressive sampling, to robustify nonparametric regression against outliers - that is, data markedly deviating from the postulated models. A variational counterpart to least-trimmed squares regression is shown closely related to an L0-(pseudo)norm-regularized estimator, that encourages sparsity in a vector explicitly modeling the outliers. This connection suggests efficient solvers based on convex relaxation, which lead naturally to a variational M-type estimator equivalent to the least-absolute shrinkage and selection operator (Lasso). Outliers are identified by judiciously tuning regularization parameters, which amounts to controlling the sparsity of the outlier vector along the whole robustification path of Lasso solutions. Reduced bias and enhanced generalization capability are attractive features of an improved estimator obtained after replacing the L0-(pseudo)norm with a nonconvex surrogate. The novel robust spline-based smoother is adopted to cleanse load curve data, a key task aiding operational decisions in the envisioned smart grid system. Computer simulations and tests on real load curve data corroborate the effectiveness of the novel sparsity-controlling robust estimators.