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I have data for every 15 mins for 4 years. ADF test shows that my data is stationary. I tried fitting model using auto.arima and seasonal=F,and I get the output as ARIMA(3,1,2) but the residual acf and pacf both have significant peaks at lag=96.

enter image description here

I tried differencing my time series at lag=96 and fitted auto.arima again to get an output of ARIMA(3,0,6) but now the peaks became much more significant.

I have become clueless how to get rid of these peaks

P.S. auto.arima with seasonal=TRUE parameter hangs my R session.

Any help would be highly appreciated.

Thanks in advance enter image description here

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  • $\begingroup$ have you successfully resolved your problem ? If not it may help if you post your data in a csv file. $\endgroup$ – IrishStat Jul 29 '19 at 13:44
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How would you fit ARIMA model with lots of autocorrelations? discusses the need for anthropogenic variables (deterministic input series) rather than using memory or sines & cosines ( see Is Prophet from Facebook any different from a linear regression? for a discussion along these lines) in building models.

I have seen very successful applications where 96 hourly models are used in conjunction with daily patterns based upon calendar effects while incorporating user specified supporting predictors both at the interval level and at the daily level.

Take a look at Forecasting hourly time series with daily, weekly & annual periodicity where I built a model for 1 of the 24 hours ... you would have 96 models one for each of the 15 minute intervals.

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  • $\begingroup$ If you are happy with my answer , please accept it and close the question $\endgroup$ – IrishStat Aug 15 '19 at 8:01

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