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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_arimaforecasting.wasp
Title produced by softwareARIMA Forecasting
Date of computationFri, 17 Dec 2010 12:22:16 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/17/t1292588440t7juuqq9v68x0zm.htm/, Retrieved Tue, 07 May 2024 03:59:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=111420, Retrieved Tue, 07 May 2024 03:59:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact157

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Standard Deviation-Mean Plot] [Unemployment] [2010-11-29 10:34:47] [b98453cac15ba1066b407e146608df68]
- RMP     [ARIMA Forecasting] [Unemployment] [2010-11-29 20:46:45] [b98453cac15ba1066b407e146608df68]
-   PD      [ARIMA Forecasting] [ARIMA Model] [2010-12-07 16:48:47] [1c68a339ea090fe045c8010fcdb839f1]
-   PD          [ARIMA Forecasting] [Paper ARIMA Model] [2010-12-17 12:22:16] [61e5ee05de011f44efa37f086a4e2271] [Current]
-   PD            [ARIMA Forecasting] [paper arima forec...] [2010-12-26 16:44:13] [eeb33d252044f8583501f5ba0605ad6d]
-   PD              [ARIMA Forecasting] [paper arima forec...] [2010-12-27 03:33:53] [eeb33d252044f8583501f5ba0605ad6d]
-   PD            [ARIMA Forecasting] [paper voorspellin...] [2010-12-26 16:46:25] [eeb33d252044f8583501f5ba0605ad6d]
-    D              [ARIMA Forecasting] [paper arima forec...] [2010-12-26 17:15:25] [eeb33d252044f8583501f5ba0605ad6d]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
130
127
122
117
112
113
149
157
157
147
137
132
125
123
117
114
111
112
144
150
149
134
123
116
117
111
105
102
95
93
124
130
124
115
106
105
105
101
95
93
84
87
116
120
117
109
105
107
109
109
108
107
99
103
131
137
135
124
118
121
121

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'George Udny Yule' @ 72.249.76.132

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111420&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111420&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111420&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'George Udny Yule' @ 72.249.76.132






Univariate ARIMA Extrapolation Forecast
timeY[t]F[t]95% LB95% UBp-value(H0: Y[t] = F[t])P(F[t]>Y[t-1])P(F[t]>Y[t-s])P(F[t]>Y[49])
37105-------
38101-------
3995-------
4093-------
4184-------
4287-------
43116-------
44120-------
45117-------
46109-------
47105-------
48107-------
49109-------
50109104.802100.0925109.80360.050.050.93190.05
5110898.509592.4342105.11420.00249e-040.85129e-04
5210796.413289.2411104.3530.00450.00210.80039e-04
539986.98879.785595.05920.001800.7660
5410390.128381.755599.65090.0040.03390.74021e-04
55131120.5565107.3293135.98370.09230.98710.71870.929
56137124.7631109.9239142.31540.08590.24310.70260.9608
57135121.6079106.3377139.8560.07520.04910.68970.9122
58124113.200498.4914130.89770.11580.00790.67910.6791
5911810994.2933126.83360.16130.04960.66990.5
60121111.099995.3662130.3820.15710.24150.66160.5845
61121113.200496.4425133.94960.23060.23060.65420.6542

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast \tabularnewline
time & Y[t] & F[t] & 95% LB & 95% UB & p-value(H0: Y[t] = F[t]) & P(F[t]>Y[t-1]) & P(F[t]>Y[t-s]) & P(F[t]>Y[49]) \tabularnewline
37 & 105 & - & - & - & - & - & - & - \tabularnewline
38 & 101 & - & - & - & - & - & - & - \tabularnewline
39 & 95 & - & - & - & - & - & - & - \tabularnewline
40 & 93 & - & - & - & - & - & - & - \tabularnewline
41 & 84 & - & - & - & - & - & - & - \tabularnewline
42 & 87 & - & - & - & - & - & - & - \tabularnewline
43 & 116 & - & - & - & - & - & - & - \tabularnewline
44 & 120 & - & - & - & - & - & - & - \tabularnewline
45 & 117 & - & - & - & - & - & - & - \tabularnewline
46 & 109 & - & - & - & - & - & - & - \tabularnewline
47 & 105 & - & - & - & - & - & - & - \tabularnewline
48 & 107 & - & - & - & - & - & - & - \tabularnewline
49 & 109 & - & - & - & - & - & - & - \tabularnewline
50 & 109 & 104.802 & 100.0925 & 109.8036 & 0.05 & 0.05 & 0.9319 & 0.05 \tabularnewline
51 & 108 & 98.5095 & 92.4342 & 105.1142 & 0.0024 & 9e-04 & 0.8512 & 9e-04 \tabularnewline
52 & 107 & 96.4132 & 89.2411 & 104.353 & 0.0045 & 0.0021 & 0.8003 & 9e-04 \tabularnewline
53 & 99 & 86.988 & 79.7855 & 95.0592 & 0.0018 & 0 & 0.766 & 0 \tabularnewline
54 & 103 & 90.1283 & 81.7555 & 99.6509 & 0.004 & 0.0339 & 0.7402 & 1e-04 \tabularnewline
55 & 131 & 120.5565 & 107.3293 & 135.9837 & 0.0923 & 0.9871 & 0.7187 & 0.929 \tabularnewline
56 & 137 & 124.7631 & 109.9239 & 142.3154 & 0.0859 & 0.2431 & 0.7026 & 0.9608 \tabularnewline
57 & 135 & 121.6079 & 106.3377 & 139.856 & 0.0752 & 0.0491 & 0.6897 & 0.9122 \tabularnewline
58 & 124 & 113.2004 & 98.4914 & 130.8977 & 0.1158 & 0.0079 & 0.6791 & 0.6791 \tabularnewline
59 & 118 & 109 & 94.2933 & 126.8336 & 0.1613 & 0.0496 & 0.6699 & 0.5 \tabularnewline
60 & 121 & 111.0999 & 95.3662 & 130.382 & 0.1571 & 0.2415 & 0.6616 & 0.5845 \tabularnewline
61 & 121 & 113.2004 & 96.4425 & 133.9496 & 0.2306 & 0.2306 & 0.6542 & 0.6542 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111420&T=1

[TABLE]
[ROW][C]Univariate ARIMA Extrapolation Forecast[/C][/ROW]
[ROW][C]time[/C][C]Y[t][/C][C]F[t][/C][C]95% LB[/C][C]95% UB[/C][C]p-value(H0: Y[t] = F[t])[/C][C]P(F[t]>Y[t-1])[/C][C]P(F[t]>Y[t-s])[/C][C]P(F[t]>Y[49])[/C][/ROW]
[ROW][C]37[/C][C]105[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]38[/C][C]101[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]39[/C][C]95[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]40[/C][C]93[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]41[/C][C]84[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]42[/C][C]87[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]43[/C][C]116[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]44[/C][C]120[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]45[/C][C]117[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]46[/C][C]109[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]47[/C][C]105[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]48[/C][C]107[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]49[/C][C]109[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]50[/C][C]109[/C][C]104.802[/C][C]100.0925[/C][C]109.8036[/C][C]0.05[/C][C]0.05[/C][C]0.9319[/C][C]0.05[/C][/ROW]
[ROW][C]51[/C][C]108[/C][C]98.5095[/C][C]92.4342[/C][C]105.1142[/C][C]0.0024[/C][C]9e-04[/C][C]0.8512[/C][C]9e-04[/C][/ROW]
[ROW][C]52[/C][C]107[/C][C]96.4132[/C][C]89.2411[/C][C]104.353[/C][C]0.0045[/C][C]0.0021[/C][C]0.8003[/C][C]9e-04[/C][/ROW]
[ROW][C]53[/C][C]99[/C][C]86.988[/C][C]79.7855[/C][C]95.0592[/C][C]0.0018[/C][C]0[/C][C]0.766[/C][C]0[/C][/ROW]
[ROW][C]54[/C][C]103[/C][C]90.1283[/C][C]81.7555[/C][C]99.6509[/C][C]0.004[/C][C]0.0339[/C][C]0.7402[/C][C]1e-04[/C][/ROW]
[ROW][C]55[/C][C]131[/C][C]120.5565[/C][C]107.3293[/C][C]135.9837[/C][C]0.0923[/C][C]0.9871[/C][C]0.7187[/C][C]0.929[/C][/ROW]
[ROW][C]56[/C][C]137[/C][C]124.7631[/C][C]109.9239[/C][C]142.3154[/C][C]0.0859[/C][C]0.2431[/C][C]0.7026[/C][C]0.9608[/C][/ROW]
[ROW][C]57[/C][C]135[/C][C]121.6079[/C][C]106.3377[/C][C]139.856[/C][C]0.0752[/C][C]0.0491[/C][C]0.6897[/C][C]0.9122[/C][/ROW]
[ROW][C]58[/C][C]124[/C][C]113.2004[/C][C]98.4914[/C][C]130.8977[/C][C]0.1158[/C][C]0.0079[/C][C]0.6791[/C][C]0.6791[/C][/ROW]
[ROW][C]59[/C][C]118[/C][C]109[/C][C]94.2933[/C][C]126.8336[/C][C]0.1613[/C][C]0.0496[/C][C]0.6699[/C][C]0.5[/C][/ROW]
[ROW][C]60[/C][C]121[/C][C]111.0999[/C][C]95.3662[/C][C]130.382[/C][C]0.1571[/C][C]0.2415[/C][C]0.6616[/C][C]0.5845[/C][/ROW]
[ROW][C]61[/C][C]121[/C][C]113.2004[/C][C]96.4425[/C][C]133.9496[/C][C]0.2306[/C][C]0.2306[/C][C]0.6542[/C][C]0.6542[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111420&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111420&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Univariate ARIMA Extrapolation Forecast
timeY[t]F[t]95% LB95% UBp-value(H0: Y[t] = F[t])P(F[t]>Y[t-1])P(F[t]>Y[t-s])P(F[t]>Y[49])
37105-------
38101-------
3995-------
4093-------
4184-------
4287-------
43116-------
44120-------
45117-------
46109-------
47105-------
48107-------
49109-------
50109104.802100.0925109.80360.050.050.93190.05
5110898.509592.4342105.11420.00249e-040.85129e-04
5210796.413289.2411104.3530.00450.00210.80039e-04
539986.98879.785595.05920.001800.7660
5410390.128381.755599.65090.0040.03390.74021e-04
55131120.5565107.3293135.98370.09230.98710.71870.929
56137124.7631109.9239142.31540.08590.24310.70260.9608
57135121.6079106.3377139.8560.07520.04910.68970.9122
58124113.200498.4914130.89770.11580.00790.67910.6791
5911810994.2933126.83360.16130.04960.66990.5
60121111.099995.3662130.3820.15710.24150.66160.5845
61121113.200496.4425133.94960.23060.23060.65420.6542






Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
500.02430.0401017.623500
510.03420.09630.068290.070553.8477.3381
520.0420.10980.0821112.080573.25828.5591
530.04730.13810.0961144.287191.01549.5402
540.05390.14280.1054165.6814105.948610.2931
550.06530.08660.1023109.0664106.468210.3183
560.07180.09810.1017149.7429112.650310.6137
570.07660.11010.1027179.3471120.987410.9994
580.07980.09540.1019116.6318120.503510.9774
590.08350.08260.181116.553110.796
600.08850.08910.09998.012114.867610.7176
610.09350.06890.096560.8341110.364810.5055

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
50 & 0.0243 & 0.0401 & 0 & 17.6235 & 0 & 0 \tabularnewline
51 & 0.0342 & 0.0963 & 0.0682 & 90.0705 & 53.847 & 7.3381 \tabularnewline
52 & 0.042 & 0.1098 & 0.0821 & 112.0805 & 73.2582 & 8.5591 \tabularnewline
53 & 0.0473 & 0.1381 & 0.0961 & 144.2871 & 91.0154 & 9.5402 \tabularnewline
54 & 0.0539 & 0.1428 & 0.1054 & 165.6814 & 105.9486 & 10.2931 \tabularnewline
55 & 0.0653 & 0.0866 & 0.1023 & 109.0664 & 106.4682 & 10.3183 \tabularnewline
56 & 0.0718 & 0.0981 & 0.1017 & 149.7429 & 112.6503 & 10.6137 \tabularnewline
57 & 0.0766 & 0.1101 & 0.1027 & 179.3471 & 120.9874 & 10.9994 \tabularnewline
58 & 0.0798 & 0.0954 & 0.1019 & 116.6318 & 120.5035 & 10.9774 \tabularnewline
59 & 0.0835 & 0.0826 & 0.1 & 81 & 116.5531 & 10.796 \tabularnewline
60 & 0.0885 & 0.0891 & 0.099 & 98.012 & 114.8676 & 10.7176 \tabularnewline
61 & 0.0935 & 0.0689 & 0.0965 & 60.8341 & 110.3648 & 10.5055 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111420&T=2

[TABLE]
[ROW][C]Univariate ARIMA Extrapolation Forecast Performance[/C][/ROW]
[ROW][C]time[/C][C]% S.E.[/C][C]PE[/C][C]MAPE[/C][C]Sq.E[/C][C]MSE[/C][C]RMSE[/C][/ROW]
[ROW][C]50[/C][C]0.0243[/C][C]0.0401[/C][C]0[/C][C]17.6235[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]51[/C][C]0.0342[/C][C]0.0963[/C][C]0.0682[/C][C]90.0705[/C][C]53.847[/C][C]7.3381[/C][/ROW]
[ROW][C]52[/C][C]0.042[/C][C]0.1098[/C][C]0.0821[/C][C]112.0805[/C][C]73.2582[/C][C]8.5591[/C][/ROW]
[ROW][C]53[/C][C]0.0473[/C][C]0.1381[/C][C]0.0961[/C][C]144.2871[/C][C]91.0154[/C][C]9.5402[/C][/ROW]
[ROW][C]54[/C][C]0.0539[/C][C]0.1428[/C][C]0.1054[/C][C]165.6814[/C][C]105.9486[/C][C]10.2931[/C][/ROW]
[ROW][C]55[/C][C]0.0653[/C][C]0.0866[/C][C]0.1023[/C][C]109.0664[/C][C]106.4682[/C][C]10.3183[/C][/ROW]
[ROW][C]56[/C][C]0.0718[/C][C]0.0981[/C][C]0.1017[/C][C]149.7429[/C][C]112.6503[/C][C]10.6137[/C][/ROW]
[ROW][C]57[/C][C]0.0766[/C][C]0.1101[/C][C]0.1027[/C][C]179.3471[/C][C]120.9874[/C][C]10.9994[/C][/ROW]
[ROW][C]58[/C][C]0.0798[/C][C]0.0954[/C][C]0.1019[/C][C]116.6318[/C][C]120.5035[/C][C]10.9774[/C][/ROW]
[ROW][C]59[/C][C]0.0835[/C][C]0.0826[/C][C]0.1[/C][C]81[/C][C]116.5531[/C][C]10.796[/C][/ROW]
[ROW][C]60[/C][C]0.0885[/C][C]0.0891[/C][C]0.099[/C][C]98.012[/C][C]114.8676[/C][C]10.7176[/C][/ROW]
[ROW][C]61[/C][C]0.0935[/C][C]0.0689[/C][C]0.0965[/C][C]60.8341[/C][C]110.3648[/C][C]10.5055[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111420&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111420&T=2

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
500.02430.0401017.623500
510.03420.09630.068290.070553.8477.3381
520.0420.10980.0821112.080573.25828.5591
530.04730.13810.0961144.287191.01549.5402
540.05390.14280.1054165.6814105.948610.2931
550.06530.08660.1023109.0664106.468210.3183
560.07180.09810.1017149.7429112.650310.6137
570.07660.11010.1027179.3471120.987410.9994
580.07980.09540.1019116.6318120.503510.9774
590.08350.08260.181116.553110.796
600.08850.08910.09998.012114.867610.7176
610.09350.06890.096560.8341110.364810.5055


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 12 ; par2 = -0.3 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 0 ; par7 = 0 ; par8 = 1 ; par9 = 0 ; par10 = FALSE ;
Parameters (R input):
par1 = 12 ; par2 = -0.3 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 0 ; par7 = 0 ; par8 = 1 ; par9 = 0 ; par10 = FALSE ;
R code (references can be found in the software module):
par1 <- as.numeric(par1) #cut off periods
par2 <- as.numeric(par2) #lambda
par3 <- as.numeric(par3) #degree of non-seasonal differencing
par4 <- as.numeric(par4) #degree of seasonal differencing
par5 <- as.numeric(par5) #seasonal period
par6 <- as.numeric(par6) #p
par7 <- as.numeric(par7) #q
par8 <- as.numeric(par8) #P
par9 <- as.numeric(par9) #Q
if (par10 == 'TRUE') par10 <- TRUE
if (par10 == 'FALSE') par10 <- FALSE
if (par2 == 0) x <- log(x)
if (par2 != 0) x <- x^par2
lx <- length(x)
first <- lx - 2*par1
nx <- lx - par1
nx1 <- nx + 1
fx <- lx - nx
if (fx < 1) {
fx <- par5
nx1 <- lx + fx - 1
first <- lx - 2*fx
}
first <- 1
if (fx < 3) fx <- round(lx/10,0)
(arima.out <- arima(x[1:nx], order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5), include.mean=par10, method='ML'))
(forecast <- predict(arima.out,par1))
(lb <- forecast$pred - 1.96 * forecast$se)
(ub <- forecast$pred + 1.96 * forecast$se)
if (par2 == 0) {
x <- exp(x)
forecast$pred <- exp(forecast$pred)
lb <- exp(lb)
ub <- exp(ub)
}
if (par2 != 0) {
x <- x^(1/par2)
forecast$pred <- forecast$pred^(1/par2)
lb <- lb^(1/par2)
ub <- ub^(1/par2)
}
if (par2 < 0) {
olb <- lb
lb <- ub
ub <- olb
}
(actandfor <- c(x[1:nx], forecast$pred))
(perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
bitmap(file='test1.png')
opar <- par(mar=c(4,4,2,2),las=1)
ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
plot(x,ylim=ylim,type='n',xlim=c(first,lx))
usr <- par('usr')
rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
abline(h= (-3:3)*2 , col ='gray', lty =3)
polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
lines(nx1:lx, lb , lty=2)
lines(nx1:lx, ub , lty=2)
lines(x, lwd=2)
lines(nx1:lx, forecast$pred , lwd=2 , col ='white')
box()
par(opar)
dev.off()
prob.dec <- array(NA, dim=fx)
prob.sdec <- array(NA, dim=fx)
prob.ldec <- array(NA, dim=fx)
prob.pval <- array(NA, dim=fx)
perf.pe <- array(0, dim=fx)
perf.mape <- array(0, dim=fx)
perf.mape1 <- array(0, dim=fx)
perf.se <- array(0, dim=fx)
perf.mse <- array(0, dim=fx)
perf.mse1 <- array(0, dim=fx)
perf.rmse <- array(0, dim=fx)
for (i in 1:fx) {
locSD <- (ub[i] - forecast$pred[i]) / 1.96
perf.pe[i] = (x[nx+i] - forecast$pred[i]) / forecast$pred[i]
perf.se[i] = (x[nx+i] - forecast$pred[i])^2
prob.dec[i] = pnorm((x[nx+i-1] - forecast$pred[i]) / locSD)
prob.sdec[i] = pnorm((x[nx+i-par5] - forecast$pred[i]) / locSD)
prob.ldec[i] = pnorm((x[nx] - forecast$pred[i]) / locSD)
prob.pval[i] = pnorm(abs(x[nx+i] - forecast$pred[i]) / locSD)
}
perf.mape[1] = abs(perf.pe[1])
perf.mse[1] = abs(perf.se[1])
for (i in 2:fx) {
perf.mape[i] = perf.mape[i-1] + abs(perf.pe[i])
perf.mape1[i] = perf.mape[i] / i
perf.mse[i] = perf.mse[i-1] + perf.se[i]
perf.mse1[i] = perf.mse[i] / i
}
perf.rmse = sqrt(perf.mse1)
bitmap(file='test2.png')
plot(forecast$pred, pch=19, type='b',main='ARIMA Extrapolation Forecast', ylab='Forecast and 95% CI', xlab='time',ylim=c(min(lb),max(ub)))
dum <- forecast$pred
dum[1:par1] <- x[(nx+1):lx]
lines(dum, lty=1)
lines(ub,lty=3)
lines(lb,lty=3)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Univariate ARIMA Extrapolation Forecast',9,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'time',1,header=TRUE)
a<-table.element(a,'Y[t]',1,header=TRUE)
a<-table.element(a,'F[t]',1,header=TRUE)
a<-table.element(a,'95% LB',1,header=TRUE)
a<-table.element(a,'95% UB',1,header=TRUE)
a<-table.element(a,'p-value
(H0: Y[t] = F[t])',1,header=TRUE)
a<-table.element(a,'P(F[t]>Y[t-1])',1,header=TRUE)
a<-table.element(a,'P(F[t]>Y[t-s])',1,header=TRUE)
mylab <- paste('P(F[t]>Y[',nx,sep='')
mylab <- paste(mylab,'])',sep='')
a<-table.element(a,mylab,1,header=TRUE)
a<-table.row.end(a)
for (i in (nx-par5):nx) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.row.end(a)
}
for (i in 1:fx) {
a<-table.row.start(a)
a<-table.element(a,nx+i,header=TRUE)
a<-table.element(a,round(x[nx+i],4))
a<-table.element(a,round(forecast$pred[i],4))
a<-table.element(a,round(lb[i],4))
a<-table.element(a,round(ub[i],4))
a<-table.element(a,round((1-prob.pval[i]),4))
a<-table.element(a,round((1-prob.dec[i]),4))
a<-table.element(a,round((1-prob.sdec[i]),4))
a<-table.element(a,round((1-prob.ldec[i]),4))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Univariate ARIMA Extrapolation Forecast Performance',7,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'time',1,header=TRUE)
a<-table.element(a,'% S.E.',1,header=TRUE)
a<-table.element(a,'PE',1,header=TRUE)
a<-table.element(a,'MAPE',1,header=TRUE)
a<-table.element(a,'Sq.E',1,header=TRUE)
a<-table.element(a,'MSE',1,header=TRUE)
a<-table.element(a,'RMSE',1,header=TRUE)
a<-table.row.end(a)
for (i in 1:fx) {
a<-table.row.start(a)
a<-table.element(a,nx+i,header=TRUE)
a<-table.element(a,round(perc.se[i],4))
a<-table.element(a,round(perf.pe[i],4))
a<-table.element(a,round(perf.mape1[i],4))
a<-table.element(a,round(perf.se[i],4))
a<-table.element(a,round(perf.mse1[i],4))
a<-table.element(a,round(perf.rmse[i],4))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')