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  • We suggest a new approach to parameter estimation in time series models with large number of parameters. We use a modified version of the Basis Pursuit Algorithm (BPA) by Chen et al [SIAM Review 43 (2001), No. 1] to verify its applicability to times series modeling. For simplicity we restrict to ARIMA models of univariate stationary time series. After having accomplished and analyzed a lot of numerical simulations we can draw the following conclusions: (1) We were able to reliably identify nearly zero parameters in the model allowing us to reduce the originally badly conditioned overparametrized model. Among others we need not take care about model orders the fixing of which is a common preliminary step used by standard techniques. For short time series paths (100 or less samples) the sparse parameter estimates provide more precise predictions compared with those based on standard maximum likelihood estimators from MATLAB's System Identification Toolbox (IDENT).
  • We suggest a new approach to parameter estimation in time series models with large number of parameters. We use a modified version of the Basis Pursuit Algorithm (BPA) by Chen et al [SIAM Review 43 (2001), No. 1] to verify its applicability to times series modeling. For simplicity we restrict to ARIMA models of univariate stationary time series. After having accomplished and analyzed a lot of numerical simulations we can draw the following conclusions: (1) We were able to reliably identify nearly zero parameters in the model allowing us to reduce the originally badly conditioned overparametrized model. Among others we need not take care about model orders the fixing of which is a common preliminary step used by standard techniques. For short time series paths (100 or less samples) the sparse parameter estimates provide more precise predictions compared with those based on standard maximum likelihood estimators from MATLAB's System Identification Toolbox (IDENT). (en)
Title
  • Sparse Parameter Estimation in Overcomplete Time Series Models
  • Sparse Parameter Estimation in Overcomplete Time Series Models (en)
skos:prefLabel
  • Sparse Parameter Estimation in Overcomplete Time Series Models
  • Sparse Parameter Estimation in Overcomplete Time Series Models (en)
skos:notation
  • RIV/00216224:14560/06:00017505!RIV10-MSM-14560___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • Z(MSM0021622418)
http://linked.open...iv/cisloPeriodika
  • 2&3
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 500627
http://linked.open...ai/riv/idVysledku
  • RIV/00216224:14560/06:00017505
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Overcomplete ARIMA model; sparse estimate; time series; forecasting; algorithm (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • AT - Rakouská republika
http://linked.open...ontrolniKodProRIV
  • [8FB550F33786]
http://linked.open...i/riv/nazevZdroje
  • Austrian Journal of Statistics
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 35/2006
http://linked.open...iv/tvurceVysledku
  • Tonner, Jaromír
  • Veselý, Vítězslav
http://linked.open...n/vavai/riv/zamer
issn
  • 1026-597X
number of pages
http://localhost/t...ganizacniJednotka
  • 14560
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