In the first part of this article, we briefly review the history of seasonal adjustment and
statistical time series analysis in order to understand why seasonal adjustment methods …

Time Series Analysis | The Oxford Handbook of Quantitative Methods in Psychology: Vol.
2Statistical Analysis | Oxford Academic Skip to Main Content Advertisement Oxford Academic …

Time series data are often subject to uncontrolled or unexpected interventions, from which
various types of outlying observations are produced. Outliers in time series, depending on …

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 …

The Wiley-Interscience Paperback Series consists of selected books that have been made
more accessible to consumers in an effort to increase global appeal and general circulation …

Outliers in time series can be regarded as being generated by dynamic intervention models
at unknown time points. Two special cases, innovational outlier (IO) and additive outlier …

In a number of practical problems where clustering or choosing from a set of dynamic
structures is needed, the introduction of a distance between the data is an early step in the …

Outliers are commonplace in data analysis. Time series analysis is no exception. Noting that
the effect of outliers on model identification statistics could be serious, this article is …

A smoothness priors modeling of time series with trends and seasonalities is shown. An
observed time series is decomposed into local polynomial trend, seasonal, globally …

This paper describes in detail a flexible approach to nonstationary time series analysis
based on a Dynamic Harmonic Regression (DHR) model of the Unobserved Components …