Somebody said that ARIMA models like discussed above are easy to implement on a
spreadsheet.
The prediction formula is simply a linear equation that refers to past values
of original time series and past values of the errors.
Thus, setting up an spreadsheet by stroing the data in one column, the
Somebody said that ARIMA models like discussed above are easy to
implement on a spreadsheet.
The prediction formula is simply a linear equation that refers to past
values of original time series and past values of the errors.
Thus, setting up an spreadsheet by stroing the data in one column, the
fo
Somebody suggest that all the intial values are zero.
So I followed this suggestion and used below formulas to compute the
forcast in Excel
when t < 46,
a(t)=0;
when t >= 46,
a(t)=X(t) - ar1*X(t-1) - X(t-45) + ar1*X(t-46) + ma1*a(t-1) + sma1*a
(t-45) - ma1*sma1*a(t-46);
X(predict) = ar1*X(t-1) +
First of all, sorry to *Gerard.
*I have changed my email account, and I don't know how to reply to my posted
thread before. So I just create a new message here.
Thanks again for your help! Now I realized where my mistake is.
I forgot to include the seasonal differencing order.
After I corrected th
Saji,
This may help.
Your model is
(1,0,1)X(0,1,1)S
giving difference polynomials
nonseasonal (1,0,1) = (1-ar1*B) = (1-ma1*B)
seasonal (0,1,1)S = (1-B**S)= (1-sma1*B**S)
giving: (1-ar1*B)X(1-B**S) x_t = (1-ma1*B)X(1-sma1*B**S) a_t
multiplying out:
x_t - x_(t-S) - ar1*x_(
Hello, Guys:
I'm from China, my English is poor and I'm new to R. The first message I sent
to R help meets some problems, so I send again.
Hope that I can get useful suggestions from you warm-hearted guys.
Thanks.
I builded a multiplicative seasonal ARMA model to a series named "cDownRange".
And
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