Why can't ARIMA model large lags and/or long range dependence? Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsTime series prediction using ARIMA vs LSTMHow can I use machine learning methods on modelling time series data?MSE and time delay relationPlot of ACF & PACFHow to improve a regression prediction model for data with parameters which are out of sample range?Time Series prediction using LSTMs: Importance of making time series stationaryHow to identify ARIMA model parametersTime series on syslogsHow to Identify p (lag order) for ARIMA Model in PythonFitting model to differenced time series

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Why can't ARIMA model large lags and/or long range dependence?



Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsTime series prediction using ARIMA vs LSTMHow can I use machine learning methods on modelling time series data?MSE and time delay relationPlot of ACF & PACFHow to improve a regression prediction model for data with parameters which are out of sample range?Time Series prediction using LSTMs: Importance of making time series stationaryHow to identify ARIMA model parametersTime series on syslogsHow to Identify p (lag order) for ARIMA Model in PythonFitting model to differenced time series










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ARIMA cannot model large lags(obtained from autocorrelation plot) and long range dependency(hurst exponent H > 0.5). Why is it so?










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    ARIMA cannot model large lags(obtained from autocorrelation plot) and long range dependency(hurst exponent H > 0.5). Why is it so?










    share|improve this question









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      ARIMA cannot model large lags(obtained from autocorrelation plot) and long range dependency(hurst exponent H > 0.5). Why is it so?










      share|improve this question









      $endgroup$




      ARIMA cannot model large lags(obtained from autocorrelation plot) and long range dependency(hurst exponent H > 0.5). Why is it so?







      time-series prediction






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      asked Jul 28 '17 at 8:48









      VaibVaib

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          An ARIMA model with order of differencing d equal to 1 can model a time series with a unit root, which is a form of long-range dependence. If d=0 one has an ARMA model, which cannot model long-range dependence unless the number or AR or MA parameters is very high. To model long range dependence in a parsimonious manner, look at fractionally integrated ARMA models, known as ARFIMA models.






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            $begingroup$

            An ARIMA model with order of differencing d equal to 1 can model a time series with a unit root, which is a form of long-range dependence. If d=0 one has an ARMA model, which cannot model long-range dependence unless the number or AR or MA parameters is very high. To model long range dependence in a parsimonious manner, look at fractionally integrated ARMA models, known as ARFIMA models.






            share|improve this answer









            $endgroup$

















              0












              $begingroup$

              An ARIMA model with order of differencing d equal to 1 can model a time series with a unit root, which is a form of long-range dependence. If d=0 one has an ARMA model, which cannot model long-range dependence unless the number or AR or MA parameters is very high. To model long range dependence in a parsimonious manner, look at fractionally integrated ARMA models, known as ARFIMA models.






              share|improve this answer









              $endgroup$















                0












                0








                0





                $begingroup$

                An ARIMA model with order of differencing d equal to 1 can model a time series with a unit root, which is a form of long-range dependence. If d=0 one has an ARMA model, which cannot model long-range dependence unless the number or AR or MA parameters is very high. To model long range dependence in a parsimonious manner, look at fractionally integrated ARMA models, known as ARFIMA models.






                share|improve this answer









                $endgroup$



                An ARIMA model with order of differencing d equal to 1 can model a time series with a unit root, which is a form of long-range dependence. If d=0 one has an ARMA model, which cannot model long-range dependence unless the number or AR or MA parameters is very high. To model long range dependence in a parsimonious manner, look at fractionally integrated ARMA models, known as ARFIMA models.







                share|improve this answer












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                answered Jan 22 '18 at 22:04









                FortrannerFortranner

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