TY - GEN A1 - Chen, Ying A1 - Koch, Thorsten A1 - Xu, Xiaofei T1 - Regularized partially functional autoregressive model with application to high-resolution natural gas forecasting in Germany N2 - We propose a partially functional autoregressive model with exogenous variables (pFAR) to describe the dynamic evolution of the serially correlated functional data. It provides a unit� ed framework to model both the temporal dependence on multiple lagged functional covariates and the causal relation with ultrahigh-dimensional exogenous scalar covariates. Estimation is conducted under a two-layer sparsity assumption, where only a few groups and elements are supposed to be active, yet without knowing their number and location in advance. We establish asymptotic properties of the estimator and investigate its unite sample performance along with simulation studies. We demonstrate the application of pFAR with the high-resolution natural gas flows in Germany, where the pFAR model provides insightful interpretation as well as good out-of-sample forecast accuracy. T3 - ZIB-Report - 19-34 Y1 - 2019 UR - https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/7488 UR - https://nbn-resolving.org/urn:nbn:de:0297-zib-74880 SN - 1438-0064 ER -