bara Tracker

Re: time series

bara — Sat, 11 Jul 2020 13:16:55 GMT

p=1 in the non seasonal ARIMA part indicates that the current observations of the series are correlated with themselves at lag 1

what is mean if P=1 in the seasonal ARIMA part ?

time series

bara — Fri, 03 Jul 2020 11:06:05 GMT

Hi!

How can I interpret the seasonal ARIMA Model (0,1,1)(1,0,0)[12] ?

ARIMA Model

bara — Sat, 11 Apr 2020 10:17:52 GMT

Hi!

I am trying to use Interrupted time series with ARIMA model to compare before and after at intervention=45

data outcome;
input outcome time intervention time_af_int;
datalines;
9 1 0 0
9 2 0 0
10 3 0 0
8 4 0 0
8 5 0 0
6 6 0 0
6 7 0 0
13 8 0 0
20 9 0 0
23 10 0 0
29 11 0 0
34 12 0 0
19 13 0 0
39 14 0 0
44 15 0 0
29 16 0 0
34 17 0 0
62 18 0 0
50 19 0 0
46 20 0 0
51 21 0 0
36 22 0 0
42 23 0 0
48 24 0 0
30 25 0 0
64 26 0 0
66 27 0 0
77 28 0 0
54 29 0 0
74 30 0 0
48 31 0 0
52 32 0 0
73 33 0 0
77 34 0 0
83 35 0 0
55 36 0 0
48 37 0 0
48 38 0 0
47 39 0 0
44 40 0 0
49 41 0 0
64 42 0 0
35 43 1 1
77 44 1 2
46 45 1 3
58 46 1 4
55 47 1 5
70 48 1 6
41 49 1 7
56 50 1 8
45 51 1 9
57 52 1 10
62 53 1 11
51 54 1 12
76 55 1 13
58 56 1 14
46 57 1 15
71 58 1 16
62 59 1 17
64 60 1 18
59 61 1 19
54 62 1 20
70 63 1 21
54 64 1 22
65 65 1 23
52 66 1 24

56 67 1 25

70 68 1 26

71 69 1 27

70 70 1 28

60 71 1 29
;
run;

there are the steps I went through

1 check for stationarity by using PROC ARIMA

proc arima data=sample; identify var=outcome stationarity=(adf); run;

the results showed the outcome is stationarity

2. from ACF PACF plots the AR=2

my questions are

1.how I can estimate the coefficient of the model (b0,b1,b2,b3)with AR=2 if I used the linear regression

outcome=b0+b1*time+b2*intervention+b3*time_af_int

2. can I use the nonlinear regression with ARIMA?

3. how account for the seasonality?

Re: nonlinear regression

bara — Thu, 09 Apr 2020 19:26:07 GMT

I used the Poisson model PROC GENMOD to estimate the parameters then use the NLIN.

There is an errors in the output. It is segmented regression with breaking point at t=16

Re: nonlinear regression

bara — Thu, 09 Apr 2020 17:18:10 GMT

sorry, the model

number=b0*(time**b1)*(intervention**b2)*(time_af_int**b3)

nonlinear regression

bara — Wed, 08 Apr 2020 20:24:12 GMT

Hi!

I want to apply the nonlinear regression on the data below. my question is how identify the parameters value?

data accidents;
input number time intervention time_af_int ;
datalines; 
17 1 0 0
10 2 0 0
15 3 0 0
14 4 0 0
26 5 0 0
9 6 0 0
11 7 0 0
17 8 0 0
10 9 0 0
15 10 0 0
21 11 0 0
11 12 0 0
14 13 0 0
16 14 0 0
9 15 0 0
11 16 0 0
13 17 1 1
10 18 1 2 
12 19 1 3
5 20 1 4 
11 21 1 5
7 22 1 6
10 23 1 7
7 24 1 8
9 25 1 9 
6 26 1 10
6 27 1 11
6 28 1 12 
10 29 1 13
8 30 1 14
11 31 1 15
7 32 1 16
8 33 1 17
5 34 1 18 
6 35 1 19
;
run;


 proc nlin data=accidents outest=est;
 parameters b0=
            b1=
            b2=
            b3=
            ;
  model number=b0+(b1*time**2)+(b2*(intervention**2))+(b3*(time_af_int**2));
  run;