我在R中有一个名为jj的时间序列。
> jj
Jan Feb Mar Apr May Jun Jul
1 2.5625072 2.6864995 2.7760495 2.6864995 2.6176149 2.8472302 2.8889086
2 2.4733998 2.5853644 2.3614352 2.5548286 2.2392920 2.2698278 2.3614352
3 2.5833333 1.9444444 2.0092593 2.2222222 2.2222222 2.0092593 2.5446500
4 2.0092593 1.6666667 1.4351852 2.2222222 2.5000000 2.2962963 2.6260788
5 2.8703704 2.2222222 1.7222222 1.6666667 1.6666667 1.7222222 2.2222222
6 2.5259259 1.8333333 1.8944444 1.5277778 2.7500000 2.8897045 2.8703652
7 2.8703704 1.9444444 2.0092593 1.9444444 1.9444444 2.5833333 2.7288827
8 2.0092593 1.3888889 1.1481481 1.1111111 1.1111111 1.1481481 2.2222222
9 1.3777778 1.1111111 0.9185185 0.8888889 1.1111111 1.3777778 0.6666667
10 1.1481481 1.1111111 1.4351852 1.1111111 1.3888889 1.1481481 1.3888889
11 1.7222222 1.3888889 1.1481481 1.3888889 1.3888889 1.4351852 2.2222222
12 2.0092593 1.1111111 0.8611111 1.3888889 1.3888889 1.7222222 2.8286329
13 1.7222222 2.9517940 2.9416154 2.6666667 2.9517940 2.9517940 2.9416154
14 2.9517940 2.7777778 2.2962963 2.5000000 1.9444444 1.7222222 2.2222222
15 2.2962963 1.9444444 2.0092593 2.2222222 1.9444444 2.2962963 2.9426333
16 2.5833333 1.9444444 1.7222222 1.9444444 1.9444444 2.0092593 2.5000000
17 1.7222222 1.3888889 1.4351852 1.3888889 1.1111111 1.1481481 1.9444444
18 1.7222222 1.6666667 1.7222222 2.7777778 2.7777778 2.6861325 2.7604363
19 2.5833333 1.9444444 1.7222222 2.8286329 2.5000000 2.5833333 2.9253296
20 2.8703704 2.5000000 1.7222222 1.6666667 2.2222222 2.2962963 2.7237934
21 2.9277778 2.8968296 2.9517940 2.9517940 2.7777778 2.9314368 2.9416154
22 2.5833333 1.6666667 2.0092593 1.6666667 1.6666667 2.0092593 2.9049724
23 2.0092593 1.3888889 2.8703652 2.7807935 2.7064897 2.9039546 2.9273654
24 2.8703704 1.6666667 2.2962963 2.2222222 2.5000000 2.2962963 2.9517940
25 2.5833333 1.3888889 2.0092593 2.2222222 1.9444444 2.5833333 2.8388115
26 2.8703704 1.3888889 2.0092593 1.6666667 1.6666667 2.0092593 2.9517940
27 2.2962963 1.3888889 2.6861325 1.9444444 1.9444444 2.5833333 2.9517940
28 2.8703704 1.3888889 2.0092593 2.8286329 2.9202403 2.9517940 2.9517940
29 2.8703704 1.6666667 1.7222222 1.3888889 1.6666667 1.4351852 2.9517940
30 1.4351852 1.1111111 0.8611111 1.1111111 1.1111111 1.4351852 2.9517940
31 1.4351852 1.1111111 0.8611111 1.1111111 1.3888889 1.4351852 2.7777778
32 1.4351852 1.3888889 0.8611111 2.2222222 2.2222222 2.5833333 2.8479723
33 2.2962963 1.3888889 2.0092593 2.5000000 2.2222222 2.8703704 2.8347401
34 2.1513000 2.4921000 2.5453500 2.5027500 2.5347000 2.1300000 2.2684500
35 1.7892000 2.3430000 2.3749500 2.2258500 2.5134000 1.8744000 2.1726000
36 1.4590500 2.4921000 2.4814500 2.1619500 1.8424500 2.0341500 1.7253000
37 1.8424500 1.5549000 1.0330500 0.9904500 0.9265500 0.5751000 0.7668000
38 0.7348500 0.9904500 2.3749500 2.3004000 2.8222500 2.8648500 2.9500500
39 2.3536500 2.1513000 2.2684500 1.5229500 0.8946000 0.7774500 1.1715000
40 0.7029000 1.1608500 0.8839500 0.7881000 0.9478500 2.0448000 1.9276500
41 1.0330500 1.6507500 2.0235000 2.6092500 2.4388500 2.5666500 2.8861500
42 1.7679000 2.1300000 2.0661000 2.1832500 2.4175500 2.4388500 2.8435500
43 1.3845000 2.0661000 2.8861500 2.9517940 2.6518500 2.5453500 2.7370500
44 2.0874000 2.1087000 2.0661000 2.7051000 1.8531000 1.2673500 2.1619500
45 1.7276389 2.1373656 1.9451389 1.9963710 1.9757930 1.6044444 1.3500000
46 0.9780556 1.9094086 2.4434722 2.4868280 2.3836022 2.6620833 2.6272849
47 2.2038889 2.3560484 2.1755556 2.1993280 1.9193548 2.2468056 2.5161290
48 1.6276389 1.8481183 2.0204167 2.2172043 1.9631720 1.4890278 1.6551075
49 0.9380556 1.2758065 2.3233333 2.4397849 2.4451613 2.4405556 2.5103495
50 1.4444444 2.2043011 1.9166667 1.9086022 1.9489247 2.1111111 1.8682796
51 2.0139072 2.2568109 2.4786743 1.9108121 2.2756334 1.4880000 1.6814920
Aug Sep Oct Nov Dec
1 2.8828206 2.9262278 2.9162662 2.9030509 2.7553841
2 2.6973290 2.9262278 2.9162662 2.9030509 2.7553841
3 2.2962963 2.9517940 2.9517940 2.9517940 2.7777778
4 2.8416667 2.9517940 2.9517940 2.9517940 2.8703652
5 2.8129630 2.9517940 2.9517940 2.9517940 2.7777778
6 2.8642580 2.9517940 2.9517940 2.9517940 2.8622223
7 2.8713831 2.9517940 2.9517940 2.9517940 2.7777778
8 2.2962963 2.9517940 2.9517940 2.9517940 1.9444444
9 1.3777778 1.3333333 2.0000000 1.9682540 1.5555556
10 2.9436511 2.9517940 2.9517940 2.9517940 2.5000000
11 2.9182046 2.9517940 2.7777778 2.7678571 1.6666667
12 2.8754545 2.9517940 2.9517940 2.9517940 1.9444444
13 2.9314368 2.9517940 2.9517940 2.9009010 2.8296508
14 2.7879185 2.9517940 2.9517940 2.9517940 2.8286329
15 2.9416154 2.9517940 2.9517940 2.9517940 2.9120975
16 2.9039546 2.9517940 2.9517940 2.9216270 2.2222222
17 2.9517940 2.9517940 2.9517940 2.9517940 2.2222222
18 2.8703704 2.9517940 2.9517940 2.9517940 2.9314368
19 2.9517940 2.9517940 2.9517940 2.9517940 2.9517940
20 2.9416154 2.9517940 2.9517940 2.9517940 2.9517940
21 2.9517940 2.9517940 2.9517940 2.9517940 2.9517940
22 2.9517940 2.9517940 2.9517940 2.9517940 2.7777778
23 2.9517940 2.9517940 2.9517940 2.9517940 2.9517940
24 2.9517940 2.9517940 2.9517940 2.9517940 2.9517940
25 2.9467047 2.9517940 2.9517940 2.9517940 2.9131153
26 2.9517940 2.9517940 2.9517940 2.9517940 2.9517940
27 2.9517940 2.9517940 2.9517940 2.9517940 2.9517940
28 2.9517940 2.9517940 2.9517940 2.9517940 2.9517940
29 2.9517940 2.9517940 2.9517940 2.9517940 2.5000000
30 2.9517940 2.9517940 2.9517940 2.9517940 2.5000000
31 2.9517940 2.9517940 2.9517940 2.9517940 2.5000000
32 2.9517940 2.9517940 2.9517940 2.9517940 2.7838471
33 2.9517940 2.9517940 2.9500500 2.9500500 2.6518500
34 2.8755000 2.9500500 2.9500500 2.7690000 2.2791000
35 2.8009500 2.9500500 2.9500500 2.7477000 2.0022000
36 2.9181000 2.9500500 2.4814500 2.0874000 1.7146500
37 0.9372000 1.1608500 1.6827000 1.4590500 1.0543500
38 2.8968000 2.8542000 2.8755000 2.8861500 2.5773000
39 1.6294500 2.1406500 2.1406500 1.9170000 1.2034500
40 2.7157500 2.9074500 2.9517940 2.0874000 1.1928000
41 2.7903000 2.9074500 2.9074500 2.9074500 2.7370500
42 2.9517940 2.9517940 2.9517940 2.9517940 2.3110500
43 2.9517940 2.9517940 2.9517940 2.9517940 2.9517940
44 2.9517940 2.9517940 2.9517940 2.7690000 1.8105000
45 1.9430556 2.5471774 2.4963710 1.8916667 1.4965054
46 2.6363889 2.6645161 2.6932796 2.6964286 2.6544355
47 2.6268056 2.6650403 2.5970430 2.5544643 2.1424731
48 2.2509722 2.2399194 2.2094086 1.8453869 1.3264785
49 2.6687500 2.8084677 2.8376344 2.7081845 1.9912634
50 2.4583333 2.8360215 2.8763441 2.6636905 2.0833333
51 2.0464437 2.0434435 1.8343226 1.7039880 1.3593448
我已经确定最好的SARIMA适合SARIMA（1,0,0）x（0,1,2）12。所以我使用（来自astsa包）sarima（jj，1,0,0,0,1,2,12）来拟合模型。我得到以下结果：
Coefficients:
ar1 sma1 sma2 constant
0.7456 -0.7469 -0.1032 -6e-04
s.e. 0.0272 0.0405 0.0429 8e-04
sigma^2 estimated as 0.1201: log likelihood = -223.17, aic = 456.33
$AIC
[1] -1.106286
$AICc
[1] -1.102856
$BIC
[1] -2.077418
现在我想生成一个新的SARIMA（1,0,0）x（0,1,2）12模型，给我刚刚找到的方差和参数，加上我的时间序列的前几个值，然后模拟500个值，以便在平均值，方差，偏度等方面将模拟数据与实际数据进行比较.R中是否有一个函数可以做到这一点？或者我是以错误的方式处理事情的？我考虑过使用预测包中的Arima来创建模型，然后为它提供新的起始值（比如我原始时间序列数据的前24个值），然后让它预测未来值。但我担心这不会保留我的方差，我想这样做，好像我没有所有的数据，只有24个起始值和模型的参数。谢谢！