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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 computationSat, 19 Dec 2009 09:24:22 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/19/t1261240751h11ym307n83wp95.htm/, Retrieved Sat, 04 May 2024 00:34:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=69690, Retrieved Sat, 04 May 2024 00:34:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact147

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [ARIMA Forecasting] [] [2009-12-06 10:44:58] [1e83ffa964db6f7ea6ccc4e7b5acbbff]
-   PD  [ARIMA Forecasting] [ws 10 deel 2 prblm] [2009-12-09 19:29:01] [134dc66689e3d457a82860db6471d419]
-   P     [ARIMA Forecasting] [ws 10 deel 2 arim...] [2009-12-12 09:45:03] [134dc66689e3d457a82860db6471d419]
-    D      [ARIMA Forecasting] [WS 10 ] [2009-12-12 11:53:20] [3425351e86519d261a643e224a0c8ee1]
-   PD          [ARIMA Forecasting] [] [2009-12-19 16:24:22] [17416e80e7873ecccac25c455c5f767e] [Current]
-   P             [ARIMA Forecasting] [] [2009-12-20 10:24:37] [3425351e86519d261a643e224a0c8ee1]
-   PD              [ARIMA Forecasting] [ARIMA forecasting] [2009-12-21 15:59:35] [76ab39dc7a55316678260825bd5ad46c]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
91.98
91.72
90.27
91.89
92.07
92.92
93.34
93.6
92.41
93.6
93.77
93.6
93.6
93.51
92.66
94.2
94.37
94.45
94.62
94.37
93.43
94.79
94.88
94.79
94.62
94.71
93.77
95.73
95.99
95.82
95.47
95.82
94.71
96.33
96.5
96.16
96.33
96.33
95.05
96.84
96.92
97.44
97.78
97.69
96.67
98.29
98.2
98.71
98.54
98.2
96.92
99.06
99.65
99.82
99.99
100.33
99.31
101.1
101.1
100.93
100.85
100.93
99.6
101.88
101.81
102.38
102.74
102.82
101.72
103.47
102.98
102.68
102.9
103.03
101.29
103.69
103.68
104.2
104.08
104.16
103.05
104.66
104.46
104.95
105.85
106.23
104.86
107.44
108.23
108.45
109.39
110.15
109.13
110.28
110.17
109.99
109.26
109.11
107.06
109.53
108.92
109.24
109.12
109
107.23
109.49
109.04
109.02
109.23

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69690&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]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69690&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69690&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 time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






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[97])
85105.85-------
86106.23-------
87104.86-------
88107.44-------
89108.23-------
90108.45-------
91109.39-------
92110.15-------
93109.13-------
94110.28-------
95110.17-------
96109.99-------
97109.26-------
98109.11109.2354108.5771109.89360.35450.470810.4708
99107.06107.8142106.8367108.79180.06520.004710.0019
100109.53109.9922108.754111.23040.2322110.8768
101108.92110.2785108.8144111.74270.03450.84180.99690.9136
102109.24110.6173108.9519112.28280.05250.97710.99460.9449
103109.12110.9435109.0954112.79150.02660.96460.95030.9629
104109111.2197109.2039113.23560.01550.97940.85090.9716
105107.23110.1565107.9849112.3280.00410.85170.82290.7908
106109.49111.6485109.3313113.96580.03390.99990.87650.9783
107109.04111.5266109.072113.98130.02350.9480.86060.9648
108109.02111.5158108.931114.10070.02920.96980.87640.9564
109109.23111.5103108.8015114.21910.04950.96420.94830.9483

\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[97]) \tabularnewline
85 & 105.85 & - & - & - & - & - & - & - \tabularnewline
86 & 106.23 & - & - & - & - & - & - & - \tabularnewline
87 & 104.86 & - & - & - & - & - & - & - \tabularnewline
88 & 107.44 & - & - & - & - & - & - & - \tabularnewline
89 & 108.23 & - & - & - & - & - & - & - \tabularnewline
90 & 108.45 & - & - & - & - & - & - & - \tabularnewline
91 & 109.39 & - & - & - & - & - & - & - \tabularnewline
92 & 110.15 & - & - & - & - & - & - & - \tabularnewline
93 & 109.13 & - & - & - & - & - & - & - \tabularnewline
94 & 110.28 & - & - & - & - & - & - & - \tabularnewline
95 & 110.17 & - & - & - & - & - & - & - \tabularnewline
96 & 109.99 & - & - & - & - & - & - & - \tabularnewline
97 & 109.26 & - & - & - & - & - & - & - \tabularnewline
98 & 109.11 & 109.2354 & 108.5771 & 109.8936 & 0.3545 & 0.4708 & 1 & 0.4708 \tabularnewline
99 & 107.06 & 107.8142 & 106.8367 & 108.7918 & 0.0652 & 0.0047 & 1 & 0.0019 \tabularnewline
100 & 109.53 & 109.9922 & 108.754 & 111.2304 & 0.2322 & 1 & 1 & 0.8768 \tabularnewline
101 & 108.92 & 110.2785 & 108.8144 & 111.7427 & 0.0345 & 0.8418 & 0.9969 & 0.9136 \tabularnewline
102 & 109.24 & 110.6173 & 108.9519 & 112.2828 & 0.0525 & 0.9771 & 0.9946 & 0.9449 \tabularnewline
103 & 109.12 & 110.9435 & 109.0954 & 112.7915 & 0.0266 & 0.9646 & 0.9503 & 0.9629 \tabularnewline
104 & 109 & 111.2197 & 109.2039 & 113.2356 & 0.0155 & 0.9794 & 0.8509 & 0.9716 \tabularnewline
105 & 107.23 & 110.1565 & 107.9849 & 112.328 & 0.0041 & 0.8517 & 0.8229 & 0.7908 \tabularnewline
106 & 109.49 & 111.6485 & 109.3313 & 113.9658 & 0.0339 & 0.9999 & 0.8765 & 0.9783 \tabularnewline
107 & 109.04 & 111.5266 & 109.072 & 113.9813 & 0.0235 & 0.948 & 0.8606 & 0.9648 \tabularnewline
108 & 109.02 & 111.5158 & 108.931 & 114.1007 & 0.0292 & 0.9698 & 0.8764 & 0.9564 \tabularnewline
109 & 109.23 & 111.5103 & 108.8015 & 114.2191 & 0.0495 & 0.9642 & 0.9483 & 0.9483 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69690&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[97])[/C][/ROW]
[ROW][C]85[/C][C]105.85[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]86[/C][C]106.23[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]87[/C][C]104.86[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]88[/C][C]107.44[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]89[/C][C]108.23[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]90[/C][C]108.45[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]91[/C][C]109.39[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]92[/C][C]110.15[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]93[/C][C]109.13[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]94[/C][C]110.28[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]95[/C][C]110.17[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]96[/C][C]109.99[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]97[/C][C]109.26[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]98[/C][C]109.11[/C][C]109.2354[/C][C]108.5771[/C][C]109.8936[/C][C]0.3545[/C][C]0.4708[/C][C]1[/C][C]0.4708[/C][/ROW]
[ROW][C]99[/C][C]107.06[/C][C]107.8142[/C][C]106.8367[/C][C]108.7918[/C][C]0.0652[/C][C]0.0047[/C][C]1[/C][C]0.0019[/C][/ROW]
[ROW][C]100[/C][C]109.53[/C][C]109.9922[/C][C]108.754[/C][C]111.2304[/C][C]0.2322[/C][C]1[/C][C]1[/C][C]0.8768[/C][/ROW]
[ROW][C]101[/C][C]108.92[/C][C]110.2785[/C][C]108.8144[/C][C]111.7427[/C][C]0.0345[/C][C]0.8418[/C][C]0.9969[/C][C]0.9136[/C][/ROW]
[ROW][C]102[/C][C]109.24[/C][C]110.6173[/C][C]108.9519[/C][C]112.2828[/C][C]0.0525[/C][C]0.9771[/C][C]0.9946[/C][C]0.9449[/C][/ROW]
[ROW][C]103[/C][C]109.12[/C][C]110.9435[/C][C]109.0954[/C][C]112.7915[/C][C]0.0266[/C][C]0.9646[/C][C]0.9503[/C][C]0.9629[/C][/ROW]
[ROW][C]104[/C][C]109[/C][C]111.2197[/C][C]109.2039[/C][C]113.2356[/C][C]0.0155[/C][C]0.9794[/C][C]0.8509[/C][C]0.9716[/C][/ROW]
[ROW][C]105[/C][C]107.23[/C][C]110.1565[/C][C]107.9849[/C][C]112.328[/C][C]0.0041[/C][C]0.8517[/C][C]0.8229[/C][C]0.7908[/C][/ROW]
[ROW][C]106[/C][C]109.49[/C][C]111.6485[/C][C]109.3313[/C][C]113.9658[/C][C]0.0339[/C][C]0.9999[/C][C]0.8765[/C][C]0.9783[/C][/ROW]
[ROW][C]107[/C][C]109.04[/C][C]111.5266[/C][C]109.072[/C][C]113.9813[/C][C]0.0235[/C][C]0.948[/C][C]0.8606[/C][C]0.9648[/C][/ROW]
[ROW][C]108[/C][C]109.02[/C][C]111.5158[/C][C]108.931[/C][C]114.1007[/C][C]0.0292[/C][C]0.9698[/C][C]0.8764[/C][C]0.9564[/C][/ROW]
[ROW][C]109[/C][C]109.23[/C][C]111.5103[/C][C]108.8015[/C][C]114.2191[/C][C]0.0495[/C][C]0.9642[/C][C]0.9483[/C][C]0.9483[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69690&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69690&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[97])
85105.85-------
86106.23-------
87104.86-------
88107.44-------
89108.23-------
90108.45-------
91109.39-------
92110.15-------
93109.13-------
94110.28-------
95110.17-------
96109.99-------
97109.26-------
98109.11109.2354108.5771109.89360.35450.470810.4708
99107.06107.8142106.8367108.79180.06520.004710.0019
100109.53109.9922108.754111.23040.2322110.8768
101108.92110.2785108.8144111.74270.03450.84180.99690.9136
102109.24110.6173108.9519112.28280.05250.97710.99460.9449
103109.12110.9435109.0954112.79150.02660.96460.95030.9629
104109111.2197109.2039113.23560.01550.97940.85090.9716
105107.23110.1565107.9849112.3280.00410.85170.82290.7908
106109.49111.6485109.3313113.96580.03390.99990.87650.9783
107109.04111.5266109.072113.98130.02350.9480.86060.9648
108109.02111.5158108.931114.10070.02920.96980.87640.9564
109109.23111.5103108.8015114.21910.04950.96420.94830.9483






Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
980.0031-0.00111e-040.01570.00130.0362
990.0046-0.0076e-040.56890.04740.2177
1000.0057-0.00424e-040.21360.01780.1334
1010.0068-0.01230.0011.84570.15380.3922
1020.0077-0.01250.0011.89710.15810.3976
1030.0085-0.01640.00143.3250.27710.5264
1040.0092-0.020.00174.92720.41060.6408
1050.0101-0.02660.00228.56420.71370.8448
1060.0106-0.01930.00164.65930.38830.6231
1070.0112-0.02230.00196.18320.51530.7178
1080.0118-0.02240.00196.22920.51910.7205
1090.0124-0.02040.00175.19990.43330.6583

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
98 & 0.0031 & -0.0011 & 1e-04 & 0.0157 & 0.0013 & 0.0362 \tabularnewline
99 & 0.0046 & -0.007 & 6e-04 & 0.5689 & 0.0474 & 0.2177 \tabularnewline
100 & 0.0057 & -0.0042 & 4e-04 & 0.2136 & 0.0178 & 0.1334 \tabularnewline
101 & 0.0068 & -0.0123 & 0.001 & 1.8457 & 0.1538 & 0.3922 \tabularnewline
102 & 0.0077 & -0.0125 & 0.001 & 1.8971 & 0.1581 & 0.3976 \tabularnewline
103 & 0.0085 & -0.0164 & 0.0014 & 3.325 & 0.2771 & 0.5264 \tabularnewline
104 & 0.0092 & -0.02 & 0.0017 & 4.9272 & 0.4106 & 0.6408 \tabularnewline
105 & 0.0101 & -0.0266 & 0.0022 & 8.5642 & 0.7137 & 0.8448 \tabularnewline
106 & 0.0106 & -0.0193 & 0.0016 & 4.6593 & 0.3883 & 0.6231 \tabularnewline
107 & 0.0112 & -0.0223 & 0.0019 & 6.1832 & 0.5153 & 0.7178 \tabularnewline
108 & 0.0118 & -0.0224 & 0.0019 & 6.2292 & 0.5191 & 0.7205 \tabularnewline
109 & 0.0124 & -0.0204 & 0.0017 & 5.1999 & 0.4333 & 0.6583 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69690&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]98[/C][C]0.0031[/C][C]-0.0011[/C][C]1e-04[/C][C]0.0157[/C][C]0.0013[/C][C]0.0362[/C][/ROW]
[ROW][C]99[/C][C]0.0046[/C][C]-0.007[/C][C]6e-04[/C][C]0.5689[/C][C]0.0474[/C][C]0.2177[/C][/ROW]
[ROW][C]100[/C][C]0.0057[/C][C]-0.0042[/C][C]4e-04[/C][C]0.2136[/C][C]0.0178[/C][C]0.1334[/C][/ROW]
[ROW][C]101[/C][C]0.0068[/C][C]-0.0123[/C][C]0.001[/C][C]1.8457[/C][C]0.1538[/C][C]0.3922[/C][/ROW]
[ROW][C]102[/C][C]0.0077[/C][C]-0.0125[/C][C]0.001[/C][C]1.8971[/C][C]0.1581[/C][C]0.3976[/C][/ROW]
[ROW][C]103[/C][C]0.0085[/C][C]-0.0164[/C][C]0.0014[/C][C]3.325[/C][C]0.2771[/C][C]0.5264[/C][/ROW]
[ROW][C]104[/C][C]0.0092[/C][C]-0.02[/C][C]0.0017[/C][C]4.9272[/C][C]0.4106[/C][C]0.6408[/C][/ROW]
[ROW][C]105[/C][C]0.0101[/C][C]-0.0266[/C][C]0.0022[/C][C]8.5642[/C][C]0.7137[/C][C]0.8448[/C][/ROW]
[ROW][C]106[/C][C]0.0106[/C][C]-0.0193[/C][C]0.0016[/C][C]4.6593[/C][C]0.3883[/C][C]0.6231[/C][/ROW]
[ROW][C]107[/C][C]0.0112[/C][C]-0.0223[/C][C]0.0019[/C][C]6.1832[/C][C]0.5153[/C][C]0.7178[/C][/ROW]
[ROW][C]108[/C][C]0.0118[/C][C]-0.0224[/C][C]0.0019[/C][C]6.2292[/C][C]0.5191[/C][C]0.7205[/C][/ROW]
[ROW][C]109[/C][C]0.0124[/C][C]-0.0204[/C][C]0.0017[/C][C]5.1999[/C][C]0.4333[/C][C]0.6583[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69690&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69690&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
980.0031-0.00111e-040.01570.00130.0362
990.0046-0.0076e-040.56890.04740.2177
1000.0057-0.00424e-040.21360.01780.1334
1010.0068-0.01230.0011.84570.15380.3922
1020.0077-0.01250.0011.89710.15810.3976
1030.0085-0.01640.00143.3250.27710.5264
1040.0092-0.020.00174.92720.41060.6408
1050.0101-0.02660.00228.56420.71370.8448
1060.0106-0.01930.00164.65930.38830.6231
1070.0112-0.02230.00196.18320.51530.7178
1080.0118-0.02240.00196.22920.51910.7205
1090.0124-0.02040.00175.19990.43330.6583


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 12 ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 1 ; par7 = 1 ; par8 = 0 ; par9 = 1 ; par10 = FALSE ;
Parameters (R input):
par1 = 12 ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 1 ; par7 = 1 ; par8 = 0 ; par9 = 1 ; 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,fx))
(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.se <- array(0, dim=fx)
perf.mse <- 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$pred[i]
perf.mape[i] = perf.mape[i] + abs(perf.pe[i])
perf.se[i] = (x[nx+i] - forecast$pred[i])^2
perf.mse[i] = perf.mse[i] + perf.se[i]
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 = perf.mape / fx
perf.mse = perf.mse / fx
perf.rmse = sqrt(perf.mse)
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[1:12] <- 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.mape[i],4))
a<-table.element(a,round(perf.se[i],4))
a<-table.element(a,round(perf.mse[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')