English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Here, we address the problem of trend estimation for functional time series.Existing contributions either deal with detecting a functional trend orassuming a simple model. They consider neither the estimation of a generalfunctional trend nor the analysis of functional time series with a functionaltrend component. Similarly to univariate time series, we propose an alternativemethodology to analyze functional time series, taking into account a functionaltrend component. We propose to estimate the functional trend by using a tensorproduct surface that is easy to implement, to interpret, and allows to controlthe smoothness properties of the estimator. Through a Monte Carlo study, wesimulate different scenarios of functional processes to show that our estimatoraccurately identifies the functional trend component. We also show that thedependency structure of the estimated stationary time series component is notsignificantly affected by the error approximation of the functional trendcomponent. We apply our methodology to annual mortality rates in France.

Nonparametric Trend Estimation in Functional Time Series with Application to Annual Mortality Rates