FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 computationMon, 22 Dec 2008 09:42:36 -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/2008/Dec/22/t12299642344vfr1rmrwpdw6tk.htm/, Retrieved Sun, 12 May 2024 22:07:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=36139, Retrieved Sun, 12 May 2024 22:07:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact164

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Standard Deviation-Mean Plot] [] [2008-12-12 12:13:32] [fad8a251ac01c156a8ae23a83577546f]
- RMPD  [(Partial) Autocorrelation Function] [Consumptiegoederen] [2008-12-12 13:39:25] [fad8a251ac01c156a8ae23a83577546f]
-   P     [(Partial) Autocorrelation Function] [auto corr cons] [2008-12-19 10:53:37] [fad8a251ac01c156a8ae23a83577546f]
-   P       [(Partial) Autocorrelation Function] [autocorr cons D] [2008-12-21 18:04:22] [fad8a251ac01c156a8ae23a83577546f]
- RMPD        [ARIMA Backward Selection] [Arima backw sel n...] [2008-12-22 10:23:57] [fad8a251ac01c156a8ae23a83577546f]
- RMPD            [ARIMA Forecasting] [forecasting duur ...] [2008-12-22 16:42:36] [fa8b44cd657c07c6ee11bb2476ca3f8d] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
72.5
72.0
98.8
75.2
81.2
88.0
54.6
68.6
101.5
93.4
84.5
91.4
64.5
64.5
117.3
73.5
79.7
102.6
57.9
73.1
102.4
82.3
89.1
84.7
81.4
67.5
113.9
83.8
73.9
103.9
67.9
62.5
125.4
79.1
106.3
96.2
94.3
85.6
117.4
88.5
124.2
119.3
76.8
70.6
122.1
109.5
119.9
102.3
79.6
78.2
103.6
77.8
99.1
105.7
84.1
88.7
108.0
98.1
101.0
82.0

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 4 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=36139&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=36139&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=36139&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 time4 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001






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[48])
3696.2-------
3794.3-------
3885.6-------
39117.4-------
4088.5-------
41124.2-------
42119.3-------
4376.8-------
4470.6-------
45122.1-------
46109.5-------
47119.9-------
48102.3-------
4979.692.932570.1224115.74250.1260.21040.45320.2104
5078.282.826459.2933106.35950.350.60590.40870.0524
51103.6122.894997.2427148.5470.07020.99970.66270.9422
5277.886.096359.9974112.19530.26660.09430.42840.1118
5399.1109.051782.453135.65050.23170.98940.13220.6906
54105.7114.664787.8807141.44880.25590.87260.36720.8172
5584.170.884643.958397.81090.1680.00560.33340.0111
5688.772.807645.813899.80130.12430.20610.56370.0161
57108115.217288.1794142.2550.30040.97270.30890.8255
5898.199.111372.0502126.17240.47080.25990.22590.4087
59101108.024980.9497135.10020.30550.76380.1950.6607
608295.629968.5467122.7130.1620.34880.31460.3146

\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[48]) \tabularnewline
36 & 96.2 & - & - & - & - & - & - & - \tabularnewline
37 & 94.3 & - & - & - & - & - & - & - \tabularnewline
38 & 85.6 & - & - & - & - & - & - & - \tabularnewline
39 & 117.4 & - & - & - & - & - & - & - \tabularnewline
40 & 88.5 & - & - & - & - & - & - & - \tabularnewline
41 & 124.2 & - & - & - & - & - & - & - \tabularnewline
42 & 119.3 & - & - & - & - & - & - & - \tabularnewline
43 & 76.8 & - & - & - & - & - & - & - \tabularnewline
44 & 70.6 & - & - & - & - & - & - & - \tabularnewline
45 & 122.1 & - & - & - & - & - & - & - \tabularnewline
46 & 109.5 & - & - & - & - & - & - & - \tabularnewline
47 & 119.9 & - & - & - & - & - & - & - \tabularnewline
48 & 102.3 & - & - & - & - & - & - & - \tabularnewline
49 & 79.6 & 92.9325 & 70.1224 & 115.7425 & 0.126 & 0.2104 & 0.4532 & 0.2104 \tabularnewline
50 & 78.2 & 82.8264 & 59.2933 & 106.3595 & 0.35 & 0.6059 & 0.4087 & 0.0524 \tabularnewline
51 & 103.6 & 122.8949 & 97.2427 & 148.547 & 0.0702 & 0.9997 & 0.6627 & 0.9422 \tabularnewline
52 & 77.8 & 86.0963 & 59.9974 & 112.1953 & 0.2666 & 0.0943 & 0.4284 & 0.1118 \tabularnewline
53 & 99.1 & 109.0517 & 82.453 & 135.6505 & 0.2317 & 0.9894 & 0.1322 & 0.6906 \tabularnewline
54 & 105.7 & 114.6647 & 87.8807 & 141.4488 & 0.2559 & 0.8726 & 0.3672 & 0.8172 \tabularnewline
55 & 84.1 & 70.8846 & 43.9583 & 97.8109 & 0.168 & 0.0056 & 0.3334 & 0.0111 \tabularnewline
56 & 88.7 & 72.8076 & 45.8138 & 99.8013 & 0.1243 & 0.2061 & 0.5637 & 0.0161 \tabularnewline
57 & 108 & 115.2172 & 88.1794 & 142.255 & 0.3004 & 0.9727 & 0.3089 & 0.8255 \tabularnewline
58 & 98.1 & 99.1113 & 72.0502 & 126.1724 & 0.4708 & 0.2599 & 0.2259 & 0.4087 \tabularnewline
59 & 101 & 108.0249 & 80.9497 & 135.1002 & 0.3055 & 0.7638 & 0.195 & 0.6607 \tabularnewline
60 & 82 & 95.6299 & 68.5467 & 122.713 & 0.162 & 0.3488 & 0.3146 & 0.3146 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=36139&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[48])[/C][/ROW]
[ROW][C]36[/C][C]96.2[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]37[/C][C]94.3[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]38[/C][C]85.6[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]39[/C][C]117.4[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]40[/C][C]88.5[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]41[/C][C]124.2[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]42[/C][C]119.3[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]43[/C][C]76.8[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]44[/C][C]70.6[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]45[/C][C]122.1[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]46[/C][C]109.5[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]47[/C][C]119.9[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]48[/C][C]102.3[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]49[/C][C]79.6[/C][C]92.9325[/C][C]70.1224[/C][C]115.7425[/C][C]0.126[/C][C]0.2104[/C][C]0.4532[/C][C]0.2104[/C][/ROW]
[ROW][C]50[/C][C]78.2[/C][C]82.8264[/C][C]59.2933[/C][C]106.3595[/C][C]0.35[/C][C]0.6059[/C][C]0.4087[/C][C]0.0524[/C][/ROW]
[ROW][C]51[/C][C]103.6[/C][C]122.8949[/C][C]97.2427[/C][C]148.547[/C][C]0.0702[/C][C]0.9997[/C][C]0.6627[/C][C]0.9422[/C][/ROW]
[ROW][C]52[/C][C]77.8[/C][C]86.0963[/C][C]59.9974[/C][C]112.1953[/C][C]0.2666[/C][C]0.0943[/C][C]0.4284[/C][C]0.1118[/C][/ROW]
[ROW][C]53[/C][C]99.1[/C][C]109.0517[/C][C]82.453[/C][C]135.6505[/C][C]0.2317[/C][C]0.9894[/C][C]0.1322[/C][C]0.6906[/C][/ROW]
[ROW][C]54[/C][C]105.7[/C][C]114.6647[/C][C]87.8807[/C][C]141.4488[/C][C]0.2559[/C][C]0.8726[/C][C]0.3672[/C][C]0.8172[/C][/ROW]
[ROW][C]55[/C][C]84.1[/C][C]70.8846[/C][C]43.9583[/C][C]97.8109[/C][C]0.168[/C][C]0.0056[/C][C]0.3334[/C][C]0.0111[/C][/ROW]
[ROW][C]56[/C][C]88.7[/C][C]72.8076[/C][C]45.8138[/C][C]99.8013[/C][C]0.1243[/C][C]0.2061[/C][C]0.5637[/C][C]0.0161[/C][/ROW]
[ROW][C]57[/C][C]108[/C][C]115.2172[/C][C]88.1794[/C][C]142.255[/C][C]0.3004[/C][C]0.9727[/C][C]0.3089[/C][C]0.8255[/C][/ROW]
[ROW][C]58[/C][C]98.1[/C][C]99.1113[/C][C]72.0502[/C][C]126.1724[/C][C]0.4708[/C][C]0.2599[/C][C]0.2259[/C][C]0.4087[/C][/ROW]
[ROW][C]59[/C][C]101[/C][C]108.0249[/C][C]80.9497[/C][C]135.1002[/C][C]0.3055[/C][C]0.7638[/C][C]0.195[/C][C]0.6607[/C][/ROW]
[ROW][C]60[/C][C]82[/C][C]95.6299[/C][C]68.5467[/C][C]122.713[/C][C]0.162[/C][C]0.3488[/C][C]0.3146[/C][C]0.3146[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=36139&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=36139&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[48])
3696.2-------
3794.3-------
3885.6-------
39117.4-------
4088.5-------
41124.2-------
42119.3-------
4376.8-------
4470.6-------
45122.1-------
46109.5-------
47119.9-------
48102.3-------
4979.692.932570.1224115.74250.1260.21040.45320.2104
5078.282.826459.2933106.35950.350.60590.40870.0524
51103.6122.894997.2427148.5470.07020.99970.66270.9422
5277.886.096359.9974112.19530.26660.09430.42840.1118
5399.1109.051782.453135.65050.23170.98940.13220.6906
54105.7114.664787.8807141.44880.25590.87260.36720.8172
5584.170.884643.958397.81090.1680.00560.33340.0111
5688.772.807645.813899.80130.12430.20610.56370.0161
57108115.217288.1794142.2550.30040.97270.30890.8255
5898.199.111372.0502126.17240.47080.25990.22590.4087
59101108.024980.9497135.10020.30550.76380.1950.6607
608295.629968.5467122.7130.1620.34880.31460.3146






Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
490.1252-0.14350.012177.754814.81293.8488
500.145-0.05590.004721.40331.78361.3355
510.1065-0.1570.0131372.291531.02435.5699
520.1547-0.09640.00868.82925.73582.3949
530.1244-0.09130.007699.03698.25312.8728
540.1192-0.07820.006580.36676.69722.5879
550.19380.18640.0155174.64714.55393.815
560.18920.21830.0182252.569221.04744.5877
570.1197-0.06260.005252.08814.34072.0834
580.1393-0.01029e-041.02280.08520.2919
590.1279-0.0650.005449.34974.11252.0279
600.1445-0.14250.0119185.77315.48113.9346

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
49 & 0.1252 & -0.1435 & 0.012 & 177.7548 & 14.8129 & 3.8488 \tabularnewline
50 & 0.145 & -0.0559 & 0.0047 & 21.4033 & 1.7836 & 1.3355 \tabularnewline
51 & 0.1065 & -0.157 & 0.0131 & 372.2915 & 31.0243 & 5.5699 \tabularnewline
52 & 0.1547 & -0.0964 & 0.008 & 68.8292 & 5.7358 & 2.3949 \tabularnewline
53 & 0.1244 & -0.0913 & 0.0076 & 99.0369 & 8.2531 & 2.8728 \tabularnewline
54 & 0.1192 & -0.0782 & 0.0065 & 80.3667 & 6.6972 & 2.5879 \tabularnewline
55 & 0.1938 & 0.1864 & 0.0155 & 174.647 & 14.5539 & 3.815 \tabularnewline
56 & 0.1892 & 0.2183 & 0.0182 & 252.5692 & 21.0474 & 4.5877 \tabularnewline
57 & 0.1197 & -0.0626 & 0.0052 & 52.0881 & 4.3407 & 2.0834 \tabularnewline
58 & 0.1393 & -0.0102 & 9e-04 & 1.0228 & 0.0852 & 0.2919 \tabularnewline
59 & 0.1279 & -0.065 & 0.0054 & 49.3497 & 4.1125 & 2.0279 \tabularnewline
60 & 0.1445 & -0.1425 & 0.0119 & 185.773 & 15.4811 & 3.9346 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=36139&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]49[/C][C]0.1252[/C][C]-0.1435[/C][C]0.012[/C][C]177.7548[/C][C]14.8129[/C][C]3.8488[/C][/ROW]
[ROW][C]50[/C][C]0.145[/C][C]-0.0559[/C][C]0.0047[/C][C]21.4033[/C][C]1.7836[/C][C]1.3355[/C][/ROW]
[ROW][C]51[/C][C]0.1065[/C][C]-0.157[/C][C]0.0131[/C][C]372.2915[/C][C]31.0243[/C][C]5.5699[/C][/ROW]
[ROW][C]52[/C][C]0.1547[/C][C]-0.0964[/C][C]0.008[/C][C]68.8292[/C][C]5.7358[/C][C]2.3949[/C][/ROW]
[ROW][C]53[/C][C]0.1244[/C][C]-0.0913[/C][C]0.0076[/C][C]99.0369[/C][C]8.2531[/C][C]2.8728[/C][/ROW]
[ROW][C]54[/C][C]0.1192[/C][C]-0.0782[/C][C]0.0065[/C][C]80.3667[/C][C]6.6972[/C][C]2.5879[/C][/ROW]
[ROW][C]55[/C][C]0.1938[/C][C]0.1864[/C][C]0.0155[/C][C]174.647[/C][C]14.5539[/C][C]3.815[/C][/ROW]
[ROW][C]56[/C][C]0.1892[/C][C]0.2183[/C][C]0.0182[/C][C]252.5692[/C][C]21.0474[/C][C]4.5877[/C][/ROW]
[ROW][C]57[/C][C]0.1197[/C][C]-0.0626[/C][C]0.0052[/C][C]52.0881[/C][C]4.3407[/C][C]2.0834[/C][/ROW]
[ROW][C]58[/C][C]0.1393[/C][C]-0.0102[/C][C]9e-04[/C][C]1.0228[/C][C]0.0852[/C][C]0.2919[/C][/ROW]
[ROW][C]59[/C][C]0.1279[/C][C]-0.065[/C][C]0.0054[/C][C]49.3497[/C][C]4.1125[/C][C]2.0279[/C][/ROW]
[ROW][C]60[/C][C]0.1445[/C][C]-0.1425[/C][C]0.0119[/C][C]185.773[/C][C]15.4811[/C][C]3.9346[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=36139&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=36139&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
490.1252-0.14350.012177.754814.81293.8488
500.145-0.05590.004721.40331.78361.3355
510.1065-0.1570.0131372.291531.02435.5699
520.1547-0.09640.00868.82925.73582.3949
530.1244-0.09130.007699.03698.25312.8728
540.1192-0.07820.006580.36676.69722.5879
550.19380.18640.0155174.64714.55393.815
560.18920.21830.0182252.569221.04744.5877
570.1197-0.06260.005252.08814.34072.0834
580.1393-0.01029e-041.02280.08520.2919
590.1279-0.0650.005449.34974.11252.0279
600.1445-0.14250.0119185.77315.48113.9346


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 12 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 2 ; par7 = 0 ; par8 = 2 ; par9 = 0 ; par10 = FALSE ;
Parameters (R input):
par1 = 12 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 2 ; par7 = 0 ; par8 = 2 ; 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,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')