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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, 16 Dec 2011 12:16:12 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/16/t1324057671o71gj99kgu5t7yl.htm/, Retrieved Sun, 05 May 2024 18:28:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=156091, Retrieved Sun, 05 May 2024 18:28:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact141

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]
-    D      [ARIMA Forecasting] [WS 9 Forecasting ...] [2010-12-03 22:01:04] [8081b8996d5947580de3eb171e82db4f]
-   PD        [ARIMA Forecasting] [Workshop 9, Forecast] [2010-12-05 20:21:31] [3635fb7041b1998c5a1332cf9de22bce]
-   P           [ARIMA Forecasting] [ARIMA Extrapolati...] [2010-12-06 22:58:10] [3635fb7041b1998c5a1332cf9de22bce]
-   P             [ARIMA Forecasting] [Verbetering WS9] [2010-12-14 19:20:19] [3635fb7041b1998c5a1332cf9de22bce]
-   PD              [ARIMA Forecasting] [Paper Forecast] [2010-12-19 18:06:55] [3635fb7041b1998c5a1332cf9de22bce]
-   PD                [ARIMA Forecasting] [Paper Forecast 2] [2010-12-19 21:47:07] [3635fb7041b1998c5a1332cf9de22bce]
- R PD                    [ARIMA Forecasting] [ARIMA forecasting] [2011-12-16 17:16:12] [274a40ad31da88f12aea425a159a1f93] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
9911.00
8915.00
9452.00
9112.00
8472.00
8230.00
8384.00
8625.00
8221.00
8649.00
8625.00
10443.00
10357.00
8586.00
8892.00
8329.00
8101.00
7922.00
8120.00
7838.00
7735.00
8406.00
8209.00
9451.00
10041.00
9411.00
10405.00
8467.00
8464.00
8102.00
7627.00
7513.00
7510.00
8291.00
8064.00
9383.00
9706.00
8579.00
9474.00
8318.00
8213.00
8059.00
9111.00
7708.00
7680.00
8014.00
8007.00
8718.00
9486.00
9113.00
9025.00
8476.00
7952.00
7759.00
7835.00
7600.00
7651.00
8319.00
8812.00
8630.00

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'AstonUniversity' @ aston.wessa.net

\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 & 2 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156091&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156091&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156091&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 time2 seconds
R Server'AstonUniversity' @ aston.wessa.net






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])
369383-------
379706-------
388579-------
399474-------
408318-------
418213-------
428059-------
439111-------
447708-------
457680-------
468014-------
478007-------
488718-------
4994869581.68228691.61610471.74840.41660.97140.39210.9714
5091138964.87588008.77349920.97820.38070.14270.78550.6936
5190259905.79148949.68910861.89380.03550.94790.8120.9926
5284768387.10527431.00289343.20760.42770.09550.55630.2488
5379528329.41217373.30979285.51450.21960.38190.59430.2128
5477598078.94317122.84079035.04550.2560.60270.51630.0951
5578358422.73117466.62879378.83350.11410.91320.07910.2725
5676007617.56036661.45798573.66280.48560.32790.42650.012
5776517601.15526645.05288557.25760.45930.50090.43580.011
5883198142.47077186.36839098.57310.35870.84320.60390.119
5988128033.43627077.33388989.53860.05520.27910.52160.0803
6086309026.42248070.329982.52480.20820.66990.73640.7364

\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 & 9383 & - & - & - & - & - & - & - \tabularnewline
37 & 9706 & - & - & - & - & - & - & - \tabularnewline
38 & 8579 & - & - & - & - & - & - & - \tabularnewline
39 & 9474 & - & - & - & - & - & - & - \tabularnewline
40 & 8318 & - & - & - & - & - & - & - \tabularnewline
41 & 8213 & - & - & - & - & - & - & - \tabularnewline
42 & 8059 & - & - & - & - & - & - & - \tabularnewline
43 & 9111 & - & - & - & - & - & - & - \tabularnewline
44 & 7708 & - & - & - & - & - & - & - \tabularnewline
45 & 7680 & - & - & - & - & - & - & - \tabularnewline
46 & 8014 & - & - & - & - & - & - & - \tabularnewline
47 & 8007 & - & - & - & - & - & - & - \tabularnewline
48 & 8718 & - & - & - & - & - & - & - \tabularnewline
49 & 9486 & 9581.6822 & 8691.616 & 10471.7484 & 0.4166 & 0.9714 & 0.3921 & 0.9714 \tabularnewline
50 & 9113 & 8964.8758 & 8008.7734 & 9920.9782 & 0.3807 & 0.1427 & 0.7855 & 0.6936 \tabularnewline
51 & 9025 & 9905.7914 & 8949.689 & 10861.8938 & 0.0355 & 0.9479 & 0.812 & 0.9926 \tabularnewline
52 & 8476 & 8387.1052 & 7431.0028 & 9343.2076 & 0.4277 & 0.0955 & 0.5563 & 0.2488 \tabularnewline
53 & 7952 & 8329.4121 & 7373.3097 & 9285.5145 & 0.2196 & 0.3819 & 0.5943 & 0.2128 \tabularnewline
54 & 7759 & 8078.9431 & 7122.8407 & 9035.0455 & 0.256 & 0.6027 & 0.5163 & 0.0951 \tabularnewline
55 & 7835 & 8422.7311 & 7466.6287 & 9378.8335 & 0.1141 & 0.9132 & 0.0791 & 0.2725 \tabularnewline
56 & 7600 & 7617.5603 & 6661.4579 & 8573.6628 & 0.4856 & 0.3279 & 0.4265 & 0.012 \tabularnewline
57 & 7651 & 7601.1552 & 6645.0528 & 8557.2576 & 0.4593 & 0.5009 & 0.4358 & 0.011 \tabularnewline
58 & 8319 & 8142.4707 & 7186.3683 & 9098.5731 & 0.3587 & 0.8432 & 0.6039 & 0.119 \tabularnewline
59 & 8812 & 8033.4362 & 7077.3338 & 8989.5386 & 0.0552 & 0.2791 & 0.5216 & 0.0803 \tabularnewline
60 & 8630 & 9026.4224 & 8070.32 & 9982.5248 & 0.2082 & 0.6699 & 0.7364 & 0.7364 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156091&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]9383[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]37[/C][C]9706[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]38[/C][C]8579[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]39[/C][C]9474[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]40[/C][C]8318[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]41[/C][C]8213[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]42[/C][C]8059[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]43[/C][C]9111[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]44[/C][C]7708[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]45[/C][C]7680[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]46[/C][C]8014[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]47[/C][C]8007[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]48[/C][C]8718[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]49[/C][C]9486[/C][C]9581.6822[/C][C]8691.616[/C][C]10471.7484[/C][C]0.4166[/C][C]0.9714[/C][C]0.3921[/C][C]0.9714[/C][/ROW]
[ROW][C]50[/C][C]9113[/C][C]8964.8758[/C][C]8008.7734[/C][C]9920.9782[/C][C]0.3807[/C][C]0.1427[/C][C]0.7855[/C][C]0.6936[/C][/ROW]
[ROW][C]51[/C][C]9025[/C][C]9905.7914[/C][C]8949.689[/C][C]10861.8938[/C][C]0.0355[/C][C]0.9479[/C][C]0.812[/C][C]0.9926[/C][/ROW]
[ROW][C]52[/C][C]8476[/C][C]8387.1052[/C][C]7431.0028[/C][C]9343.2076[/C][C]0.4277[/C][C]0.0955[/C][C]0.5563[/C][C]0.2488[/C][/ROW]
[ROW][C]53[/C][C]7952[/C][C]8329.4121[/C][C]7373.3097[/C][C]9285.5145[/C][C]0.2196[/C][C]0.3819[/C][C]0.5943[/C][C]0.2128[/C][/ROW]
[ROW][C]54[/C][C]7759[/C][C]8078.9431[/C][C]7122.8407[/C][C]9035.0455[/C][C]0.256[/C][C]0.6027[/C][C]0.5163[/C][C]0.0951[/C][/ROW]
[ROW][C]55[/C][C]7835[/C][C]8422.7311[/C][C]7466.6287[/C][C]9378.8335[/C][C]0.1141[/C][C]0.9132[/C][C]0.0791[/C][C]0.2725[/C][/ROW]
[ROW][C]56[/C][C]7600[/C][C]7617.5603[/C][C]6661.4579[/C][C]8573.6628[/C][C]0.4856[/C][C]0.3279[/C][C]0.4265[/C][C]0.012[/C][/ROW]
[ROW][C]57[/C][C]7651[/C][C]7601.1552[/C][C]6645.0528[/C][C]8557.2576[/C][C]0.4593[/C][C]0.5009[/C][C]0.4358[/C][C]0.011[/C][/ROW]
[ROW][C]58[/C][C]8319[/C][C]8142.4707[/C][C]7186.3683[/C][C]9098.5731[/C][C]0.3587[/C][C]0.8432[/C][C]0.6039[/C][C]0.119[/C][/ROW]
[ROW][C]59[/C][C]8812[/C][C]8033.4362[/C][C]7077.3338[/C][C]8989.5386[/C][C]0.0552[/C][C]0.2791[/C][C]0.5216[/C][C]0.0803[/C][/ROW]
[ROW][C]60[/C][C]8630[/C][C]9026.4224[/C][C]8070.32[/C][C]9982.5248[/C][C]0.2082[/C][C]0.6699[/C][C]0.7364[/C][C]0.7364[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156091&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156091&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])
369383-------
379706-------
388579-------
399474-------
408318-------
418213-------
428059-------
439111-------
447708-------
457680-------
468014-------
478007-------
488718-------
4994869581.68228691.61610471.74840.41660.97140.39210.9714
5091138964.87588008.77349920.97820.38070.14270.78550.6936
5190259905.79148949.68910861.89380.03550.94790.8120.9926
5284768387.10527431.00289343.20760.42770.09550.55630.2488
5379528329.41217373.30979285.51450.21960.38190.59430.2128
5477598078.94317122.84079035.04550.2560.60270.51630.0951
5578358422.73117466.62879378.83350.11410.91320.07910.2725
5676007617.56036661.45798573.66280.48560.32790.42650.012
5776517601.15526645.05288557.25760.45930.50090.43580.011
5883198142.47077186.36839098.57310.35870.84320.60390.119
5988128033.43627077.33388989.53860.05520.27910.52160.0803
6086309026.42248070.329982.52480.20820.66990.73640.7364






Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
490.0474-0.0109155.081100
500.05440.01650.013321940.765815547.9235124.6913
510.0492-0.08890.0385775793.4167268963.0879518.6165
520.05820.01060.03157902.291203697.8886451.329
530.0586-0.04530.0343142439.8655191446.284437.5458
540.0604-0.03960.0352102363.5888176599.1682420.237
550.0579-0.06980.0401345427.8084200717.5453448.0151
560.064-0.00230.0354308.3659175666.3979419.1258
570.06420.00660.03222484.5065156423.9655395.5047
580.05990.02170.031131162.5996143897.8289379.3387
590.06070.09690.0371606161.5819185921.8065431.1865
600.054-0.04390.0377157150.7182183524.2158428.3973

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
49 & 0.0474 & -0.01 & 0 & 9155.0811 & 0 & 0 \tabularnewline
50 & 0.0544 & 0.0165 & 0.0133 & 21940.7658 & 15547.9235 & 124.6913 \tabularnewline
51 & 0.0492 & -0.0889 & 0.0385 & 775793.4167 & 268963.0879 & 518.6165 \tabularnewline
52 & 0.0582 & 0.0106 & 0.0315 & 7902.291 & 203697.8886 & 451.329 \tabularnewline
53 & 0.0586 & -0.0453 & 0.0343 & 142439.8655 & 191446.284 & 437.5458 \tabularnewline
54 & 0.0604 & -0.0396 & 0.0352 & 102363.5888 & 176599.1682 & 420.237 \tabularnewline
55 & 0.0579 & -0.0698 & 0.0401 & 345427.8084 & 200717.5453 & 448.0151 \tabularnewline
56 & 0.064 & -0.0023 & 0.0354 & 308.3659 & 175666.3979 & 419.1258 \tabularnewline
57 & 0.0642 & 0.0066 & 0.0322 & 2484.5065 & 156423.9655 & 395.5047 \tabularnewline
58 & 0.0599 & 0.0217 & 0.0311 & 31162.5996 & 143897.8289 & 379.3387 \tabularnewline
59 & 0.0607 & 0.0969 & 0.0371 & 606161.5819 & 185921.8065 & 431.1865 \tabularnewline
60 & 0.054 & -0.0439 & 0.0377 & 157150.7182 & 183524.2158 & 428.3973 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156091&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]0.0474[/C][C]-0.01[/C][C]0[/C][C]9155.0811[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]50[/C][C]0.0544[/C][C]0.0165[/C][C]0.0133[/C][C]21940.7658[/C][C]15547.9235[/C][C]124.6913[/C][/ROW]
[ROW][C]51[/C][C]0.0492[/C][C]-0.0889[/C][C]0.0385[/C][C]775793.4167[/C][C]268963.0879[/C][C]518.6165[/C][/ROW]
[ROW][C]52[/C][C]0.0582[/C][C]0.0106[/C][C]0.0315[/C][C]7902.291[/C][C]203697.8886[/C][C]451.329[/C][/ROW]
[ROW][C]53[/C][C]0.0586[/C][C]-0.0453[/C][C]0.0343[/C][C]142439.8655[/C][C]191446.284[/C][C]437.5458[/C][/ROW]
[ROW][C]54[/C][C]0.0604[/C][C]-0.0396[/C][C]0.0352[/C][C]102363.5888[/C][C]176599.1682[/C][C]420.237[/C][/ROW]
[ROW][C]55[/C][C]0.0579[/C][C]-0.0698[/C][C]0.0401[/C][C]345427.8084[/C][C]200717.5453[/C][C]448.0151[/C][/ROW]
[ROW][C]56[/C][C]0.064[/C][C]-0.0023[/C][C]0.0354[/C][C]308.3659[/C][C]175666.3979[/C][C]419.1258[/C][/ROW]
[ROW][C]57[/C][C]0.0642[/C][C]0.0066[/C][C]0.0322[/C][C]2484.5065[/C][C]156423.9655[/C][C]395.5047[/C][/ROW]
[ROW][C]58[/C][C]0.0599[/C][C]0.0217[/C][C]0.0311[/C][C]31162.5996[/C][C]143897.8289[/C][C]379.3387[/C][/ROW]
[ROW][C]59[/C][C]0.0607[/C][C]0.0969[/C][C]0.0371[/C][C]606161.5819[/C][C]185921.8065[/C][C]431.1865[/C][/ROW]
[ROW][C]60[/C][C]0.054[/C][C]-0.0439[/C][C]0.0377[/C][C]157150.7182[/C][C]183524.2158[/C][C]428.3973[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156091&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156091&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
490.0474-0.0109155.081100
500.05440.01650.013321940.765815547.9235124.6913
510.0492-0.08890.0385775793.4167268963.0879518.6165
520.05820.01060.03157902.291203697.8886451.329
530.0586-0.04530.0343142439.8655191446.284437.5458
540.0604-0.03960.0352102363.5888176599.1682420.237
550.0579-0.06980.0401345427.8084200717.5453448.0151
560.064-0.00230.0354308.3659175666.3979419.1258
570.06420.00660.03222484.5065156423.9655395.5047
580.05990.02170.031131162.5996143897.8289379.3387
590.06070.09690.0371606161.5819185921.8065431.1865
600.054-0.04390.0377157150.7182183524.2158428.3973


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = 12 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 0 ; par7 = 1 ; 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')