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?
time-series prediction
asked Jul 28 '17 at 8:48
VaibVaib
62
$endgroup$
bumped to the homepage by Community♦ 24 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
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
asked Jul 28 '17 at 8:48
VaibVaib
62
$endgroup$
bumped to the homepage by Community♦ 24 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
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
asked Jul 28 '17 at 8:48
VaibVaib
62
$endgroup$
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
time-series prediction
asked Jul 28 '17 at 8:48
VaibVaib
62
asked Jul 28 '17 at 8:48
VaibVaib
62
asked Jul 28 '17 at 8:48
VaibVaib
62
asked Jul 28 '17 at 8:48
VaibVaib
62
asked Jul 28 '17 at 8:48
VaibVaib
62
62
bumped to the homepage by Community♦ 24 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 24 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
add a comment |
1 Answer
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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.
answered Jan 22 '18 at 22:04
FortrannerFortranner
1213
$endgroup$
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1 Answer
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1 Answer
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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.
answered Jan 22 '18 at 22:04
FortrannerFortranner
1213
$endgroup$
add a comment |
$begingroup$
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.
answered Jan 22 '18 at 22:04
FortrannerFortranner
1213
$endgroup$
add a comment |
$begingroup$
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.
answered Jan 22 '18 at 22:04
FortrannerFortranner
1213
$endgroup$
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.
answered Jan 22 '18 at 22:04
FortrannerFortranner
1213
answered Jan 22 '18 at 22:04
FortrannerFortranner
1213
answered Jan 22 '18 at 22:04
FortrannerFortranner
1213
answered Jan 22 '18 at 22:04
FortrannerFortranner
1213
1213
add a comment |
add a comment |
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