Outliers, level shifts, and variance changes in time series

TLDR

Outliers, level shifts, and variance changes are common in applied time series analysis but are frequently overlooked because simple detection methods are lacking. The study addresses the problem of detecting outliers, level shifts, and variance changes in a univariate time series. The authors use only least‑squares techniques and residual variance ratios, providing extremely simple yet useful methods. The effectiveness of these simple methods is demonstrated by analysing three real data sets.

Abstract

Abstract Outliers, level shifts, and variance changes are commonplace in applied time series analysis. However, their existence is often ignored and their impact is overlooked, for the lack of simple and useful methods to detect and handle those extraordinary events. The problem of detecting outliers, level shifts, and variance changes in a univariate time series is considered. The methods employed are extremely simple yet useful. Only the least squares techniques and residual variance ratios are used. The effectiveness of these simple methods is demonstrated by analysing three real data sets.