# A Robust Time Series Model with Outliers and Missing Entries

## TL;DR

This paper introduces a robust time series modeling approach that effectively handles outliers and missing data by using sparsity constraints and uncertainty modeling, validated through simulations.

## Contribution

It proposes a novel robust modeling framework for univariate time series with outliers and missing entries, incorporating sparsity and uncertainty constraints.

## Key findings

- Validated with simulated results showing robustness
- Effectively handles outliers and missing data
- Reduces active coefficients via sparsity constraints

## Abstract

This paper studies the problem of robustly learning the correlation function for a univariate time series with the presence of noise, outliers and missing entries. The outliers or anomalies considered here are sparse and rare events that deviate from normality which is depicted by a correlation function and an uncertainty condition. This general formulation is applied to univariate time series of event counts (or non-negative time series) where the correlation is a log-linear function with the uncertainty condition following the Poisson distribution. Approximations to the sparsity constraint, such as $\ell^r, 0< r\le 1$, are used to obtain robustness in the presence of outliers. The $\ell^r$ constraint is also applied to the correlation function to reduce the number of active coefficients. This task also helps bypassing the model selection procedure. Simulated results are presented to validate the model.