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I am new to time series and forecasting and I have been assigned to determine the model and order for a data object. The ACF, PACF, and EACF are below: ACF

PACF

EACF

I was thinking it was an AR(1), but I am not sure. Thanks for any help!

Richard Hardy
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Sean
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  • Is this an assignment for a class? If so, you should include the `self-stuy` tag as it will probably get more views! – André.B Nov 13 '19 at 19:22
  • add your data ... and I will try and help further . auto,arima is of little value as it ASSUMES that the model residuals are free of latent deterministic structure sucha as pulses, level.step shifts , seasonal pulses and/or local time trends .See @Adamo 's wise words https://stats.stackexchange.com/questions/317734/interrupted-time-series-analysis-arimax-for-high-frequency-biological-data/317804#317804 – IrishStat Nov 13 '19 at 22:00

1 Answers1

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By the rough rules of thumb in section 8.5 of Forecasting: Principles and Practice (2nd ed.) by Athanasopoulos & Hyndman, this indeed looks like an AR(1) process:

The data may follow an ARIMA(p,d,0) model if the ACF and PACF plots of the differenced data show the following patterns:

  • the ACF is exponentially decaying or sinusoidal;
  • there is a significant spike at lag p in the PACF, but none beyond lag p.

Try fitting an AR(1) and checking residuals, again using (P)ACF plots.

Alternatively, consider using an automatic ARIMA fitting tool, like auto.arima() in the forecast package for R.

Stephan Kolassa
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