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Refers to the AutoRegressive Integrated Moving Average model used in time series modeling both for data description and for forecasting. This model generalizes the ARMA model by including a term for differencing, which is useful for removing trends and handling some types of non-stationarity.
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Question about ARIMA(1,1,0)
So, I've been looking at different ARIMA models, mainly ones with d=1, and I came across the ARI(1,1) model, which is an AR(1) model that has been differenced once. …
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What kind of ARIMA Model is this?
I've got the time series $Y_t = 3 + Y_{t-1} + e_t - 0.75e_{t-1}$ and I'm trying to figure out what p,d,q are for the ARIMA(p,d,q) model. … My other guess is an ARIMA(1,1,1) …
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Write Process as ARMA(p,q)
I have this process: $Y_t = \frac{2}{5}Y_{t-1}+ \frac{9}{20}Y_{t-2} + e_t+ e_{t-1}+\frac{1}{4}e_{t-2}$
which is an ARMA(2,2) model, and I'd like to write it as an ARMA(1,1).
I found the AR polynomial …
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Accepted
Write Process as ARMA(p,q)
I realized, you can factor: $(-9B+10)(B+2)Y_{t} = (B+2)^2 e_t$ which yields $(-9B+10)Y_t = (B+2)e_t$ which is an ARMA(1,1)
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Determining order of SARIMA model by ACF/PACF (and dealing with seasonality)
I have a ts that has the average monthly measure of pollutants in the air and i'm trying to use a SARIMA$(p,d,q)(P,D,Q)$ to model it but I'm having trouble determining the order because I'm somehow no …
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Determining order of ARIMA(p,d,q) from ACF and PACF
If I had to guess I would propose an ARIMA(1,1,0), but again I'm really not sure. …