ftsa

R package, ftsa: Functional Time Series Analysis. Functions for visualizing, modeling, forecasting and hypothesis testing of functional time series.

Keywords for this software

Anything in here will be replaced on browsers that support the canvas element

References in zbMATH (referenced in 12 articles )

Showing results 1 to 12 of 12.
Sorted by year (citations)

  1. Bingham, N. H.: Prediction theory for stationary functional time series (2022)
  2. Elías, Antonio; Jiménez, Raúl; Shang, Han Lin: On projection methods for functional time series forecasting (2022)
  3. Meintanis, Simos G.; Hušková, Marie; Hlávka, Zdeněk: Fourier-type tests of mutual independence between functional time series (2022)
  4. Sang, Peijun; Cao, Jiguo: Functional single-index quantile regression models (2020)
  5. Slaoui, Yousri: Recursive nonparametric regression estimation for dependent strong mixing functional data (2020)
  6. Ivanescu, Andrada E.; Crainiceanu, Ciprian M.; Checkley, William: Dynamic child growth prediction: a comparative methods approach (2017)
  7. Aue, Alexander; Norinho, Diogo Dubart; Hörmann, Siegfried: On the prediction of stationary functional time series (2015)
  8. Ugarte, Maria D.; Aguilera, Ana M.: More on functional data analysis and other aspects in OODA (2014)
  9. Shang, Han Lin: Functional time series approach for forecasting very short-term electricity demand (2013)
  10. Manuel Febrero-Bande; Manuel de la Fuente: Statistical Computing in Functional Data Analysis: The R Package fda.usc (2012) not zbMATH
  11. Shang, Han Lin; Hyndman, Rob. J.: Nonparametric time series forecasting with dynamic updating (2011)
  12. Hyndman, Rob J.; Shang, Han Lin: Forecasting functional time series (2009)