How to interpret these acf and pacf plots?
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I don't know which model to fit to these ACF and PACF. Is it an AR(3) or something else?
time-series arima acf-pacf box-jenkins
edited 39 mins ago
Stephan Kolassa
48.8k8102185
asked 46 mins ago
AimAim
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add a comment |
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I don't know which model to fit to these ACF and PACF. Is it an AR(3) or something else?
time-series arima acf-pacf box-jenkins
edited 39 mins ago
Stephan Kolassa
48.8k8102185
asked 46 mins ago
AimAim
111
New contributor
Aim is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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could you provide more background information on what you were doing to generate these?
$endgroup$
– ReneBt
11 mins ago
add a comment |
$begingroup$
I don't know which model to fit to these ACF and PACF. Is it an AR(3) or something else?
time-series arima acf-pacf box-jenkins
edited 39 mins ago
Stephan Kolassa
48.8k8102185
asked 46 mins ago
AimAim
111
New contributor
Aim is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I don't know which model to fit to these ACF and PACF. Is it an AR(3) or something else?
time-series arima acf-pacf box-jenkins
time-series arima acf-pacf box-jenkins
edited 39 mins ago
Stephan Kolassa
48.8k8102185
asked 46 mins ago
AimAim
111
New contributor
Aim is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited 39 mins ago
Stephan Kolassa
48.8k8102185
asked 46 mins ago
AimAim
111
New contributor
Aim is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited 39 mins ago
Stephan Kolassa
48.8k8102185
edited 39 mins ago
Stephan Kolassa
48.8k8102185
edited 39 mins ago
Stephan Kolassa
48.8k8102185
48.8k8102185
asked 46 mins ago
AimAim
111
New contributor
Aim is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 46 mins ago
AimAim
111
asked 46 mins ago
AimAim
111
111
New contributor
Aim is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Aim is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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Check out our Code of Conduct.
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could you provide more background information on what you were doing to generate these?
$endgroup$
– ReneBt
11 mins ago
add a comment |
$begingroup$
could you provide more background information on what you were doing to generate these?
$endgroup$
– ReneBt
11 mins ago
$begingroup$
could you provide more background information on what you were doing to generate these?
$endgroup$
– ReneBt
11 mins ago
$begingroup$
could you provide more background information on what you were doing to generate these?
$endgroup$
– ReneBt
11 mins ago
add a comment |
2 Answers
2
active
oldest
votes
$begingroup$
There are suspicious peaks at lag 12. I suspect you have monthly data, and these peaks come from yearly seasonality. Consider taking seasonal differences, then plotting the ACF and PACF plots for these differences. Here are some hints on using (P)ACF to determine ARIMA orders.
In general, the Box-Jenkins approach is outdated. Consider using a more modern approach like choosing models based on AIC, as implemented in auto.arima() in the forecast package for R.
answered 39 mins ago
Stephan KolassaStephan Kolassa
48.8k8102185
$endgroup$
$begingroup$
Thanks. Yes this is monthly data. I am trying to forecast for a time series!
$endgroup$
– Aim
36 mins ago
$begingroup$
I recommend the excellent free online book Forecasting: Principles and Practice (2nd ed.) by Athanasopoulos & Hyndman.
$endgroup$
– Stephan Kolassa
35 mins ago
add a comment |
$begingroup$
The ACF and the PACF are summary statistics and trying to parse the appropriate model is nigh impossible unless you restrict yourself to very simple (pure arima models) and even then it is a daunting task. Fitting a set of presumptive models using a list based approach seldomly is satisfactory except in the rare case of no katent deterministic structure..
Your data may need auto-regressive or moving-average structure along with regular differencing , seasonal differencing, and deterministic structure like level/step shifts , local time trends, pulses , seasonal pulses. Additionally there may be the need for the data to be partitioned due to changes in parameters over time or error variance changes over time or error variance-expected value linkages. Only your data knows for sure ... which is why I ask you to post your data and I will try and help further.
You might want to follow my guidance here Significant lags at ACF and PACF plots in GLM: what should I do? regarding the ways to match asc/pacf to simple arima models.
The more comprehensive/modern way to identify models is to integreate arima with deterministic structure as outlined here https://autobox.com/pdfs/ARIMA%20FLOW%20CHART.pdf reflecting an iterative approach not a one-step approach as is often suggested here.
If the data is deemed confidential simply scale it by subtracting a constant and dividing it by another constant
answered 36 mins ago
IrishStatIrishStat
21.7k42342
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add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
There are suspicious peaks at lag 12. I suspect you have monthly data, and these peaks come from yearly seasonality. Consider taking seasonal differences, then plotting the ACF and PACF plots for these differences. Here are some hints on using (P)ACF to determine ARIMA orders.
In general, the Box-Jenkins approach is outdated. Consider using a more modern approach like choosing models based on AIC, as implemented in auto.arima() in the forecast package for R.
answered 39 mins ago
Stephan KolassaStephan Kolassa
48.8k8102185
$endgroup$
$begingroup$
Thanks. Yes this is monthly data. I am trying to forecast for a time series!
$endgroup$
– Aim
36 mins ago
$begingroup$
I recommend the excellent free online book Forecasting: Principles and Practice (2nd ed.) by Athanasopoulos & Hyndman.
$endgroup$
– Stephan Kolassa
35 mins ago
add a comment |
$begingroup$
There are suspicious peaks at lag 12. I suspect you have monthly data, and these peaks come from yearly seasonality. Consider taking seasonal differences, then plotting the ACF and PACF plots for these differences. Here are some hints on using (P)ACF to determine ARIMA orders.
In general, the Box-Jenkins approach is outdated. Consider using a more modern approach like choosing models based on AIC, as implemented in auto.arima() in the forecast package for R.
answered 39 mins ago
Stephan KolassaStephan Kolassa
48.8k8102185
$endgroup$
$begingroup$
Thanks. Yes this is monthly data. I am trying to forecast for a time series!
$endgroup$
– Aim
36 mins ago
$begingroup$
I recommend the excellent free online book Forecasting: Principles and Practice (2nd ed.) by Athanasopoulos & Hyndman.
$endgroup$
– Stephan Kolassa
35 mins ago
add a comment |
$begingroup$
There are suspicious peaks at lag 12. I suspect you have monthly data, and these peaks come from yearly seasonality. Consider taking seasonal differences, then plotting the ACF and PACF plots for these differences. Here are some hints on using (P)ACF to determine ARIMA orders.
In general, the Box-Jenkins approach is outdated. Consider using a more modern approach like choosing models based on AIC, as implemented in auto.arima() in the forecast package for R.
answered 39 mins ago
Stephan KolassaStephan Kolassa
48.8k8102185
$endgroup$
There are suspicious peaks at lag 12. I suspect you have monthly data, and these peaks come from yearly seasonality. Consider taking seasonal differences, then plotting the ACF and PACF plots for these differences. Here are some hints on using (P)ACF to determine ARIMA orders.
In general, the Box-Jenkins approach is outdated. Consider using a more modern approach like choosing models based on AIC, as implemented in auto.arima() in the forecast package for R.
answered 39 mins ago
Stephan KolassaStephan Kolassa
48.8k8102185
answered 39 mins ago
Stephan KolassaStephan Kolassa
48.8k8102185
answered 39 mins ago
Stephan KolassaStephan Kolassa
48.8k8102185
answered 39 mins ago
Stephan KolassaStephan Kolassa
48.8k8102185
48.8k8102185
$begingroup$
Thanks. Yes this is monthly data. I am trying to forecast for a time series!
$endgroup$
– Aim
36 mins ago
$begingroup$
I recommend the excellent free online book Forecasting: Principles and Practice (2nd ed.) by Athanasopoulos & Hyndman.
$endgroup$
– Stephan Kolassa
35 mins ago
add a comment |
$begingroup$
Thanks. Yes this is monthly data. I am trying to forecast for a time series!
$endgroup$
– Aim
36 mins ago
$begingroup$
I recommend the excellent free online book Forecasting: Principles and Practice (2nd ed.) by Athanasopoulos & Hyndman.
$endgroup$
– Stephan Kolassa
35 mins ago
$begingroup$
Thanks. Yes this is monthly data. I am trying to forecast for a time series!
$endgroup$
– Aim
36 mins ago
$begingroup$
Thanks. Yes this is monthly data. I am trying to forecast for a time series!
$endgroup$
– Aim
36 mins ago
$begingroup$
I recommend the excellent free online book Forecasting: Principles and Practice (2nd ed.) by Athanasopoulos & Hyndman.
$endgroup$
– Stephan Kolassa
35 mins ago
$begingroup$
I recommend the excellent free online book Forecasting: Principles and Practice (2nd ed.) by Athanasopoulos & Hyndman.
$endgroup$
– Stephan Kolassa
35 mins ago
add a comment |
$begingroup$
The ACF and the PACF are summary statistics and trying to parse the appropriate model is nigh impossible unless you restrict yourself to very simple (pure arima models) and even then it is a daunting task. Fitting a set of presumptive models using a list based approach seldomly is satisfactory except in the rare case of no katent deterministic structure..
Your data may need auto-regressive or moving-average structure along with regular differencing , seasonal differencing, and deterministic structure like level/step shifts , local time trends, pulses , seasonal pulses. Additionally there may be the need for the data to be partitioned due to changes in parameters over time or error variance changes over time or error variance-expected value linkages. Only your data knows for sure ... which is why I ask you to post your data and I will try and help further.
You might want to follow my guidance here Significant lags at ACF and PACF plots in GLM: what should I do? regarding the ways to match asc/pacf to simple arima models.
The more comprehensive/modern way to identify models is to integreate arima with deterministic structure as outlined here https://autobox.com/pdfs/ARIMA%20FLOW%20CHART.pdf reflecting an iterative approach not a one-step approach as is often suggested here.
If the data is deemed confidential simply scale it by subtracting a constant and dividing it by another constant
answered 36 mins ago
IrishStatIrishStat
21.7k42342
$endgroup$
add a comment |
$begingroup$
The ACF and the PACF are summary statistics and trying to parse the appropriate model is nigh impossible unless you restrict yourself to very simple (pure arima models) and even then it is a daunting task. Fitting a set of presumptive models using a list based approach seldomly is satisfactory except in the rare case of no katent deterministic structure..
Your data may need auto-regressive or moving-average structure along with regular differencing , seasonal differencing, and deterministic structure like level/step shifts , local time trends, pulses , seasonal pulses. Additionally there may be the need for the data to be partitioned due to changes in parameters over time or error variance changes over time or error variance-expected value linkages. Only your data knows for sure ... which is why I ask you to post your data and I will try and help further.
You might want to follow my guidance here Significant lags at ACF and PACF plots in GLM: what should I do? regarding the ways to match asc/pacf to simple arima models.
The more comprehensive/modern way to identify models is to integreate arima with deterministic structure as outlined here https://autobox.com/pdfs/ARIMA%20FLOW%20CHART.pdf reflecting an iterative approach not a one-step approach as is often suggested here.
If the data is deemed confidential simply scale it by subtracting a constant and dividing it by another constant
answered 36 mins ago
IrishStatIrishStat
21.7k42342
$endgroup$
add a comment |
$begingroup$
The ACF and the PACF are summary statistics and trying to parse the appropriate model is nigh impossible unless you restrict yourself to very simple (pure arima models) and even then it is a daunting task. Fitting a set of presumptive models using a list based approach seldomly is satisfactory except in the rare case of no katent deterministic structure..
Your data may need auto-regressive or moving-average structure along with regular differencing , seasonal differencing, and deterministic structure like level/step shifts , local time trends, pulses , seasonal pulses. Additionally there may be the need for the data to be partitioned due to changes in parameters over time or error variance changes over time or error variance-expected value linkages. Only your data knows for sure ... which is why I ask you to post your data and I will try and help further.
You might want to follow my guidance here Significant lags at ACF and PACF plots in GLM: what should I do? regarding the ways to match asc/pacf to simple arima models.
The more comprehensive/modern way to identify models is to integreate arima with deterministic structure as outlined here https://autobox.com/pdfs/ARIMA%20FLOW%20CHART.pdf reflecting an iterative approach not a one-step approach as is often suggested here.
If the data is deemed confidential simply scale it by subtracting a constant and dividing it by another constant
answered 36 mins ago
IrishStatIrishStat
21.7k42342
$endgroup$
The ACF and the PACF are summary statistics and trying to parse the appropriate model is nigh impossible unless you restrict yourself to very simple (pure arima models) and even then it is a daunting task. Fitting a set of presumptive models using a list based approach seldomly is satisfactory except in the rare case of no katent deterministic structure..
Your data may need auto-regressive or moving-average structure along with regular differencing , seasonal differencing, and deterministic structure like level/step shifts , local time trends, pulses , seasonal pulses. Additionally there may be the need for the data to be partitioned due to changes in parameters over time or error variance changes over time or error variance-expected value linkages. Only your data knows for sure ... which is why I ask you to post your data and I will try and help further.
You might want to follow my guidance here Significant lags at ACF and PACF plots in GLM: what should I do? regarding the ways to match asc/pacf to simple arima models.
The more comprehensive/modern way to identify models is to integreate arima with deterministic structure as outlined here https://autobox.com/pdfs/ARIMA%20FLOW%20CHART.pdf reflecting an iterative approach not a one-step approach as is often suggested here.
If the data is deemed confidential simply scale it by subtracting a constant and dividing it by another constant
answered 36 mins ago
IrishStatIrishStat
21.7k42342
edited 30 mins ago
answered 36 mins ago
IrishStatIrishStat
21.7k42342
answered 36 mins ago
IrishStatIrishStat
21.7k42342
answered 36 mins ago
IrishStatIrishStat
21.7k42342
21.7k42342
add a comment |
add a comment |
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Aim is a new contributor. Be nice, and check out our Code of Conduct.
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could you provide more background information on what you were doing to generate these?
$endgroup$
– ReneBt
11 mins ago