ARfit is a collection of Matlab modules for modeling and analyzing multivariate time series with autoregressive (AR) models. ARfit contains modules to given time series data, for analyzing eigen modes of a fitted model, and for simulating AR processes. ARfit estimates the parameters of AR models from given time series data with a stepwise least squares algorithm that is computationally efficient, in particular when the data are high-dimensional. ARfit modules construct approximate confidence intervals for the estimated parameters and compute statistics with which the adequacy of a fitted model can be assessed. Dynamical characteristics of the modeled time series can be examined by means of a decomposition of a fitted AR model into eigenmodes and associated oscillation periods, damping times, and excitations. The ARfit module that performs the eigendecomposition of a fitted model also constructs approximate confidence intervals for the eigenmodes and their oscillation periods and damping times.

References in zbMATH (referenced in 38 articles , 1 standard article )

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  1. Hsiao, Hsiao-Fen; Huang, Jiang-Chuan; Lin, Zheng-Wei: Portfolio construction using bootstrapping neural networks: evidence from global stock market (2020)
  2. Nicholson, William B.; Wilms, Ines; Bien, Jacob; Matteson, David S.: High dimensional forecasting via interpretable vector autoregression (2020)
  3. Hu, Guannan; Bódai, Tamás; Lucarini, Valerio: Effects of stochastic parametrization on extreme value statistics (2019)
  4. Cabuz, Simona; Abreu, Giuseppe: Causal inference for multivariate stochastic process prediction (2018)
  5. Matsuda, Takeru; Komaki, Fumiyasu: Multivariate time series decomposition into oscillation components (2017)
  6. Cremers, Daniel: Image segmentation with shape priors: explicit versus implicit representations (2015)
  7. Harlim, John; Hong, Hoon; Robbins, Jacob L.: An algebraic method for constructing stable and consistent autoregressive filters (2015)
  8. Graef, A.; Hartmann, M.; Flamm, C.; Baumgartner, Christoph; Deistler, M.; Kluge, T.: A novel method for the identification of synchronization effects in multichannel ECoG with an application to epilepsy (2013)
  9. He, Lin; Yu, Zhuliang; Gu, Zhenghui; Li, Yuanqing: Long-tail distribution based multiscale-multiband autoregressive detection for hyperspectral imagery (2013)
  10. Li, Xinyang; Zhang, Haihong; Guan, Cuntai; Ong, Sim Heng; Ang, Kai Keng; Pan, Yaozhang: Discriminative learning of propagation and spatial pattern for motor imagery EEG analysis (2013)
  11. Anagnostopoulos, Christos; Hadjiefthymiades, Stathes; Georgas, Panagiotis: PC3: principal component-based context compression. Improving energy efficiency in wireless sensor networks (2012)
  12. Meerbach, Eike; Latorre, Juan C.; Schütte, Christof: Sequential change point detection in molecular dynamics trajectories (2012)
  13. Orsingher, Enzo; Polito, Federico: Compositions, random sums and continued random fractions of Poisson and fractional Poisson processes (2012)
  14. Szabó, Zoltán; Póczos, Barnabás; Lőrincz, András: Separation theorem for independent subspace analysis and its consequences (2012)
  15. Galka, Andreas; Wong, Kin Foon Kevin; Ozaki, Tohru; Muhle, Hiltrud; Stephani, Ulrich: Decomposition of neurological multivariate time series by state space modelling (2011)
  16. García-Hiernaux, Alfredo: Forecasting linear dynamical systems using subspace methods (2011)
  17. Gençağa, Deniz; Kuruoğlu, Ercan E.; Ertüzün, Ayşın: Modeling non-Gaussian time-varying vector autoregressive processes by particle filtering (2010)
  18. Ndiour, Ibrahima J.; Teizer, Jochen; Vela, Patricio A.: A probabilistic contour observer for online visual tracking (2010)
  19. Szabó, Zoltán; Póczos, Barnabás; Lőrincz, András: Auto-regressive independent process analysis without combinatorial efforts (2010) ioport
  20. Taxidis, Jiannis; Coomber, Ben; Mason, Rob; Owen, Markus: Assessing cortico-hippocampal functional connectivity under anesthesia and kainic acid using generalized partial directed coherence (2010)

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