时间序列实验报告实验六Word格式.docx
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时间序列实验报告实验六Word格式.docx
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t4.51580.372612.120.000
S=24.1962R-Sq=80.8%R-Sq(调整)=80.2%
方差分析
来源自由度SSMSFP
回归18601486014146.920.000
残差误差3520491585
合计36106505
异常观测值
拟合值标准化
观测值tTt拟合值标准误残差残差
3737.0279.40233.237.8046.172.02R
R表示此观测值含有大的标准化残差
④回归分析:
xt与xt-1
xt=18.2+0.927xt-1
常量18.1548.2102.210.034
xt-10.926580.0531617.430.000
S=17.7290R-Sq=89.7%R-Sq(调整)=89.4%
回归19550495504303.850.000
残差误差3511001314
观测值xt-1xt拟合值标准误残差残差
10100.0018.158.2181.855.21RX
37273279.40271.297.448.110.50X
X表示受X值影响很大的观测值。
⑤回归分析:
xt=1.04xt-1
无常数项
xt-11.036450.0198652.180.000
S=18.6620
回归19482389482382722.710.000
残差误差3612538348
合计37960776
10100.000.000.00100.005.36R
37273279.40283.165.43-3.76-0.21X
⑥自相关函数:
残差1
滞后ACFTLBQ
10.8580735.2229.51
20.6497702.5146.92
30.4330361.4554.88
40.2056150.6556.73
50.0018440.0156.73
6-0.132743-0.4257.55
7-0.216002-0.6759.79
8-0.206314-0.6461.91
9-0.161411-0.4963.25
10-0.151776-0.4664.48
11-0.126886-0.3865.38
12-0.102208-0.3165.98
13-0.102679-0.3166.61
14-0.118259-0.3567.49
15-0.129545-0.3868.59
16-0.125660-0.3769.68
17-0.097002-0.2970.36
18-0.099145-0.2971.10
19-0.138576-0.4172.64
20-0.177660-0.5275.32
21-0.228044-0.6680.01
22-0.311090-0.8989.32
23-0.378048-1.06104.05
24-0.401812-1.10121.97
残差1的自相关
⑦偏自相关函数:
滞后PACFT
10.8580735.22
2-0.328081-2.00
3-0.110138-0.67
4-0.198267-1.21
5-0.071217-0.43
60.0627050.38
7-0.039807-0.24
80.2051631.25
9-0.083272-0.51
10-0.231817-1.41
110.0715380.44
12-0.062013-0.38
13-0.006191-0.04
14-0.052089-0.32
150.0001830.00
160.0625630.38
17-0.042816-0.26
18-0.172466-1.05
19-0.145155-0.88
20-0.078179-0.48
21-0.104789-0.64
22-0.188685-1.15
23-0.022760-0.14
240.0010300.01
残差1的偏自相关
⑧综合自回归移动平均(ARIMA)模型:
每次迭代中的估计值
迭代SSE参数
014926.60.1000.1000.080
112179.30.2500.0320.133
29834.90.400-0.0400.174
37836.30.550-0.1160.208
46153.30.700-0.1940.236
54765.50.850-0.2740.259
63658.41.000-0.3550.279
72820.81.150-0.4370.297
82244.31.300-0.5190.314
91924.31.450-0.6000.333
101857.91.530-0.6370.398
111854.01.533-0.6290.506
121853.31.530-0.6230.547
131853.21.529-0.6210.562
141853.21.529-0.6200.566
151853.21.529-0.6190.569
161853.21.529-0.6190.570
171853.21.529-0.6190.571
181853.21.529-0.6190.571
每个估计值的相对变化不到0.0010
参数的最终估计值
类型系数系数标准误TP
AR11.52870.138611.030.000
AR2-0.61890.1434-4.320.000
常量0.5711.2660.450.655
平均值6.3314.03
观测值个数:
37
残差:
SS=1818.08(不包括向后预测)
MS=53.47DF=34
修正Box-Pierce(Ljung-Box)卡方统计量
滞后12243648
卡方12.923.833.5*
自由度92133*
P值0.1670.3050.444*
⑨综合自回归移动平均(ARIMA)
模型:
每次迭代中的估计值(模型2)
014929.90.1000.100
112196.10.2500.032
29859.90.400-0.041
37866.10.550-0.117
46185.50.700-0.196
54798.40.850-0.276
63690.51.000-0.358
72850.51.150-0.440
82270.21.300-0.524
91944.01.450-0.606
101870.41.539-0.649
111867.31.544-0.644
121866.91.543-0.640
131866.81.543-0.638
141866.81.543-0.637
151866.81.543-0.637
AR11.54260.135911.350.000
AR2-0.63720.1409-4.520.000
SS=1811.38(不包括向后预测)
MS=51.75DF=35
卡方13.324.034.0*
自由度102234*
P值0.2090.3460.466*
⑩xt,
xt的拟合值的时间序列图
自相关函数:
at
1-0.034928-0.210.05
2-0.129209-0.780.74
30.1704391.021.97
4-0.205781-1.203.82
50.0825330.464.13
60.0231970.134.15
7-0.267176-1.497.59
80.1417990.758.59
90.2433331.2611.64
10-0.169548-0.8413.18
11-0.038495-0.1913.26
12-0.008613-0.0413.26
13-0.045873-0.2213.39
14-0.072364-0.3513.72
15-0.054325-0.2613.91
16-0.072976-0.3514.28
170.0823060.4014.77
180.1474610.7116.42
19-0.158618-0.7518.43
200.0213690.1018.47
210.1351490.6320.12
22-0.171065-0.7922.94
23-0.011875-0.0522.95
24-0.098278-0.4524.02
at的自相关
①自相关函数:
残差2
10.0810820.490.26
20.0049660.030.26
30.0278600.170.30
4-0.055917-0.340.43
5-0.046201-0.280.53
6-0.071956-0.430.77
7-0.228393-1.373.28
8-0.141247-0.804.27
90.0205580.124.30
100.0055330.034.30
110.0510500.294.44
120.0631930.354.67
130.0303420.174.73
140.0069470.044.73
15-0.038367-0.214.83
16-0.052168-0.295.01
170.0032470.025.01
180.0660200.375.35
19-0.007259-0.045.35
20-0.037869-0.215.47
210.0603620.335.80
22-0.044789-0.255.99
23-0.080607-0.446.66
24-0.142030-0.788.90
残差2的自相关
②自相关函数:
残差2_1
10.4661362.808.49
20.0950260.488.86
3-0.042956-0.218.93
4-0.312987-1.5613.12
5-0.223632-1.0415.33
6-0.216898-0.9817.47
7-0.199177-0.8819.34
80.1117420.4819.95
90.1922990.8321.83
100.0048970.0221.83
11-0.012791-0.0521.84
12-0.044881-0.1921.95
13-0.105238-0.4422.61
14-0.126599-0.5323.61
15-0.072151-0.3023.95
160.0549500.2324.15
170.2116690.8827.38
180.2505371.0232.15
190.1144410.4533.20
200.1247470.4934.53
210.0593930.2334.86
22-0.180633-0.7038.04
23-0.230868-0.8943.65
24-0.278502-1.0552.50
残差2_1的自相关
③自相关函数:
10.4661362.80
2-0.156196-0.94
3-0.027973-0.17
4-0.345435-2.07
50.1170180.70
6-0.247196-1.48
70.0026370.02
80.1657220.99
90.0307270.18
10-0.280607-1.68
110.0343210.21
120.0222090.13
13-0.078825-0.47
14-0.187468-1.12
150.2115761.27
160.0099920.06
170.0535710.32
180.0664720.40
190.0576360.35
200.0789160.47
21-0.028220-0.17
22-0.120558-0.72
230.0004630.00
24-0.170539-1.02
残差2_1的偏自相关
④综合自回归移动平均(ARIMA)模型:
每次迭代中的估计值(模型3)
02326.070.100
12103.100.250
21993.510.400
31981.200.466
41981.160.470
51981.160.470
AR10.47000.14973.140.003
36
SS=1980.44(不包括向后预测)
MS=56.58DF=35
卡方13.728.8**
自由度1123**
P值0.2490.187**
⑤综合自回归移动平均(ARIMA)模型:
xt
每次迭代中的估计值(模型1)
03163.530.1005.083
12675.00-0.0505.084
22346.13-0.2005.071
32128.51-0.3505.043
41992.59-0.5005.004
51927.64-0.6444.974
61921.90-0.6804.995
71920.82-0.6955.003
81920.58-0.7025.006
91920.52-0.7055.008
101920.50-0.7075.009
111920.50-0.7085.009
121920.50-0.7085.009
移动平均1-0.70820.1264-5.600.000
常量5.0092.1202.360.024
差值:
1正规差值
原始序列37,36差值之后
SS=1920.46(不包括向后预测)
MS=56.48DF=34
卡方7.918.7**
自由度1022**
P值0.6420.662**
法一结束
法二
是白噪声
不是
成绩:
教师签名:
月日
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