2026-10-12T18:36:39Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33940779 2026-09-18T19:55:20Z category_811 category_69 category_106 category_122 portal_87 item_type_3 month_year_09_2026

Approximate Factor Models for Functional Time Series Sven Otto (6243185) Nazarii Salish (25073931) Mathematical Sciences not elsewhere classified Inorganic Chemistry Science Policy Mental Health Curve data functional data analysis identification information criterion forecasting yield curve modeling C32 C38 C55 E43 <p>We propose a novel approximate factor model tailored for analyzing time-dependent curve data. Our model decomposes such data into two distinct components: a low-dimensional predictable factor component and an unpredictable error term. These components are identified through the autocovariance structure of the underlying functional time series. The model parameters are consistently estimated using the eigencomponents of a cumulative autocovariance operator and an information criterion is proposed to determine the appropriate number of factors. Applications to mortality and yield curve modeling illustrate key advantages of our approach over the widely used functional principal component analysis, as it offers parsimonious structural representations of the underlying dynamics along with gains in out-of-sample forecast performance.</p> 2026-09-18T19:55:20Z Dataset Dataset 10.6084/m9.figshare.33940779.v1 https://figshare.com/articles/dataset/Approximate_Factor_Models_for_Functional_Time_Series/33940779 CC BY 4.0