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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, 25 Dec 2010 18:35:08 +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/25/t1293301963z8dgkrpdhok16z6.htm/, Retrieved Mon, 29 Apr 2024 00:48:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=115434, Retrieved Mon, 29 Apr 2024 00:48:27 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact132

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 Forecasting...] [2010-12-06 13:02:50] [f4dc4aa51d65be851b8508203d9f6001]
-   PD        [ARIMA Forecasting] [ARIMA Forecasting...] [2010-12-19 20:46:40] [f4dc4aa51d65be851b8508203d9f6001]
-    D          [ARIMA Forecasting] [ARIMA FORECAST] [2010-12-25 16:51:28] [f9eaed74daea918f73b9f505c5b1f19e]
-    D            [ARIMA Forecasting] [Arima forecasting...] [2010-12-25 18:15:26] [f9eaed74daea918f73b9f505c5b1f19e]
-   P                 [ARIMA Forecasting] [Arima forecasting...] [2010-12-25 18:35:08] [2e49bff66bb3e1f5d7fa8957e12fbb12] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
25.22
27.63
27.47
22.54
27.4
29.68
28.51
29.89
32.62
30.93
32.52
25.28
25.64
27.41
24.4
25.55
28.45
27.72
24.54
25.67
25.54
20.48
18.94
18.6
19.49
20.29
23.69
25.65
25.43
24.13
25.77
26.63
28.34
27.55
24.5
28.52
31.29
32.65
30.34
25.02
25.81
27.55
28.4
29.83
27.1
29.59
28.77
29.88
31.18
30.87
33.8
33.36
37.92
35.19
38.37
43.03
43.38
49.77
43.05
39.65
44.28
45.56
53.08
51.86
48.67
54.31
57.58
64.09
62.98
58.52
55.54
56.75
63.57
59.92
62.25
70.44
70.19
68.86
73.9
73.61
62.77
58.38
58.48
62.31
54.3
57.76
62.14
67.4
67.48
71.32
77.2
70.8
77.13
83.04
92.53
91.45
91.92
94.82
103.28
110.44
123.94
133.05
133.9
113.85
99.06
72.84
53.24
41.58
44.86
43.24
46.84
50.85
57.94
68.59
64.92
72.5
67.69
73.19
77.04
74.67

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'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115434&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115434&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115434&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'RServer@AstonUniversity' @ vre.aston.ac.uk






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[108])
9691.45-------
9791.92-------
9894.82-------
99103.28-------
100110.44-------
101123.94-------
102133.05-------
103133.9-------
104113.85-------
10599.06-------
10672.84-------
10753.24-------
10841.58-------
10944.8640.650431.865253.64090.26270.444200.4442
11043.2442.359829.339266.44930.47150.419400.5253
11146.8444.093828.292978.05350.4370.51973e-040.5577
11250.8546.671127.637495.11080.43290.49730.00490.5816
11357.9449.727227.5306115.54310.40340.48670.01360.5959
11468.5952.447527.1786140.55750.35980.45140.03650.5955
11564.9252.302225.9776154.90390.40480.37780.05950.5811
11672.547.425523.271144.73370.30680.36230.09050.5469
11767.6943.141321.0326133.80910.29780.26280.11340.5135
11873.1935.128817.6285101.47890.13040.16810.13260.4244
11977.0428.060814.61374.36930.01910.02810.14330.2836
12074.6723.412412.505358.66790.00220.00140.15620.1562

\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[108]) \tabularnewline
96 & 91.45 & - & - & - & - & - & - & - \tabularnewline
97 & 91.92 & - & - & - & - & - & - & - \tabularnewline
98 & 94.82 & - & - & - & - & - & - & - \tabularnewline
99 & 103.28 & - & - & - & - & - & - & - \tabularnewline
100 & 110.44 & - & - & - & - & - & - & - \tabularnewline
101 & 123.94 & - & - & - & - & - & - & - \tabularnewline
102 & 133.05 & - & - & - & - & - & - & - \tabularnewline
103 & 133.9 & - & - & - & - & - & - & - \tabularnewline
104 & 113.85 & - & - & - & - & - & - & - \tabularnewline
105 & 99.06 & - & - & - & - & - & - & - \tabularnewline
106 & 72.84 & - & - & - & - & - & - & - \tabularnewline
107 & 53.24 & - & - & - & - & - & - & - \tabularnewline
108 & 41.58 & - & - & - & - & - & - & - \tabularnewline
109 & 44.86 & 40.6504 & 31.8652 & 53.6409 & 0.2627 & 0.4442 & 0 & 0.4442 \tabularnewline
110 & 43.24 & 42.3598 & 29.3392 & 66.4493 & 0.4715 & 0.4194 & 0 & 0.5253 \tabularnewline
111 & 46.84 & 44.0938 & 28.2929 & 78.0535 & 0.437 & 0.5197 & 3e-04 & 0.5577 \tabularnewline
112 & 50.85 & 46.6711 & 27.6374 & 95.1108 & 0.4329 & 0.4973 & 0.0049 & 0.5816 \tabularnewline
113 & 57.94 & 49.7272 & 27.5306 & 115.5431 & 0.4034 & 0.4867 & 0.0136 & 0.5959 \tabularnewline
114 & 68.59 & 52.4475 & 27.1786 & 140.5575 & 0.3598 & 0.4514 & 0.0365 & 0.5955 \tabularnewline
115 & 64.92 & 52.3022 & 25.9776 & 154.9039 & 0.4048 & 0.3778 & 0.0595 & 0.5811 \tabularnewline
116 & 72.5 & 47.4255 & 23.271 & 144.7337 & 0.3068 & 0.3623 & 0.0905 & 0.5469 \tabularnewline
117 & 67.69 & 43.1413 & 21.0326 & 133.8091 & 0.2978 & 0.2628 & 0.1134 & 0.5135 \tabularnewline
118 & 73.19 & 35.1288 & 17.6285 & 101.4789 & 0.1304 & 0.1681 & 0.1326 & 0.4244 \tabularnewline
119 & 77.04 & 28.0608 & 14.613 & 74.3693 & 0.0191 & 0.0281 & 0.1433 & 0.2836 \tabularnewline
120 & 74.67 & 23.4124 & 12.5053 & 58.6679 & 0.0022 & 0.0014 & 0.1562 & 0.1562 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115434&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[108])[/C][/ROW]
[ROW][C]96[/C][C]91.45[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]97[/C][C]91.92[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]98[/C][C]94.82[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]99[/C][C]103.28[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]100[/C][C]110.44[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]101[/C][C]123.94[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]102[/C][C]133.05[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]103[/C][C]133.9[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]104[/C][C]113.85[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]105[/C][C]99.06[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]106[/C][C]72.84[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]107[/C][C]53.24[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]108[/C][C]41.58[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]109[/C][C]44.86[/C][C]40.6504[/C][C]31.8652[/C][C]53.6409[/C][C]0.2627[/C][C]0.4442[/C][C]0[/C][C]0.4442[/C][/ROW]
[ROW][C]110[/C][C]43.24[/C][C]42.3598[/C][C]29.3392[/C][C]66.4493[/C][C]0.4715[/C][C]0.4194[/C][C]0[/C][C]0.5253[/C][/ROW]
[ROW][C]111[/C][C]46.84[/C][C]44.0938[/C][C]28.2929[/C][C]78.0535[/C][C]0.437[/C][C]0.5197[/C][C]3e-04[/C][C]0.5577[/C][/ROW]
[ROW][C]112[/C][C]50.85[/C][C]46.6711[/C][C]27.6374[/C][C]95.1108[/C][C]0.4329[/C][C]0.4973[/C][C]0.0049[/C][C]0.5816[/C][/ROW]
[ROW][C]113[/C][C]57.94[/C][C]49.7272[/C][C]27.5306[/C][C]115.5431[/C][C]0.4034[/C][C]0.4867[/C][C]0.0136[/C][C]0.5959[/C][/ROW]
[ROW][C]114[/C][C]68.59[/C][C]52.4475[/C][C]27.1786[/C][C]140.5575[/C][C]0.3598[/C][C]0.4514[/C][C]0.0365[/C][C]0.5955[/C][/ROW]
[ROW][C]115[/C][C]64.92[/C][C]52.3022[/C][C]25.9776[/C][C]154.9039[/C][C]0.4048[/C][C]0.3778[/C][C]0.0595[/C][C]0.5811[/C][/ROW]
[ROW][C]116[/C][C]72.5[/C][C]47.4255[/C][C]23.271[/C][C]144.7337[/C][C]0.3068[/C][C]0.3623[/C][C]0.0905[/C][C]0.5469[/C][/ROW]
[ROW][C]117[/C][C]67.69[/C][C]43.1413[/C][C]21.0326[/C][C]133.8091[/C][C]0.2978[/C][C]0.2628[/C][C]0.1134[/C][C]0.5135[/C][/ROW]
[ROW][C]118[/C][C]73.19[/C][C]35.1288[/C][C]17.6285[/C][C]101.4789[/C][C]0.1304[/C][C]0.1681[/C][C]0.1326[/C][C]0.4244[/C][/ROW]
[ROW][C]119[/C][C]77.04[/C][C]28.0608[/C][C]14.613[/C][C]74.3693[/C][C]0.0191[/C][C]0.0281[/C][C]0.1433[/C][C]0.2836[/C][/ROW]
[ROW][C]120[/C][C]74.67[/C][C]23.4124[/C][C]12.5053[/C][C]58.6679[/C][C]0.0022[/C][C]0.0014[/C][C]0.1562[/C][C]0.1562[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115434&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115434&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[108])
9691.45-------
9791.92-------
9894.82-------
99103.28-------
100110.44-------
101123.94-------
102133.05-------
103133.9-------
104113.85-------
10599.06-------
10672.84-------
10753.24-------
10841.58-------
10944.8640.650431.865253.64090.26270.444200.4442
11043.2442.359829.339266.44930.47150.419400.5253
11146.8444.093828.292978.05350.4370.51973e-040.5577
11250.8546.671127.637495.11080.43290.49730.00490.5816
11357.9449.727227.5306115.54310.40340.48670.01360.5959
11468.5952.447527.1786140.55750.35980.45140.03650.5955
11564.9252.302225.9776154.90390.40480.37780.05950.5811
11672.547.425523.271144.73370.30680.36230.09050.5469
11767.6943.141321.0326133.80910.29780.26280.11340.5135
11873.1935.128817.6285101.47890.13040.16810.13260.4244
11977.0428.060814.61374.36930.01910.02810.14330.2836
12074.6723.412412.505358.66790.00220.00140.15620.1562






Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
1090.1630.1036017.720500
1100.29010.02080.06220.77489.24773.041
1110.39290.06230.06227.54178.6792.946
1120.52950.08950.06917.462810.8753.2977
1130.67530.16520.088367.450622.19014.7106
1140.85710.30780.1248260.580461.92187.869
1151.00090.24120.1415159.208875.81998.7075
1161.04680.52870.1899628.7323144.93412.0389
1171.07230.5690.232602.6388195.790113.9925
1180.96371.08350.31721448.6577321.076817.9186
1190.8421.74550.4472398.9572509.975122.5826
1200.76832.18930.59222627.3453686.422626.1997

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
109 & 0.163 & 0.1036 & 0 & 17.7205 & 0 & 0 \tabularnewline
110 & 0.2901 & 0.0208 & 0.0622 & 0.7748 & 9.2477 & 3.041 \tabularnewline
111 & 0.3929 & 0.0623 & 0.0622 & 7.5417 & 8.679 & 2.946 \tabularnewline
112 & 0.5295 & 0.0895 & 0.069 & 17.4628 & 10.875 & 3.2977 \tabularnewline
113 & 0.6753 & 0.1652 & 0.0883 & 67.4506 & 22.1901 & 4.7106 \tabularnewline
114 & 0.8571 & 0.3078 & 0.1248 & 260.5804 & 61.9218 & 7.869 \tabularnewline
115 & 1.0009 & 0.2412 & 0.1415 & 159.2088 & 75.8199 & 8.7075 \tabularnewline
116 & 1.0468 & 0.5287 & 0.1899 & 628.7323 & 144.934 & 12.0389 \tabularnewline
117 & 1.0723 & 0.569 & 0.232 & 602.6388 & 195.7901 & 13.9925 \tabularnewline
118 & 0.9637 & 1.0835 & 0.3172 & 1448.6577 & 321.0768 & 17.9186 \tabularnewline
119 & 0.842 & 1.7455 & 0.447 & 2398.9572 & 509.9751 & 22.5826 \tabularnewline
120 & 0.7683 & 2.1893 & 0.5922 & 2627.3453 & 686.4226 & 26.1997 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115434&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]109[/C][C]0.163[/C][C]0.1036[/C][C]0[/C][C]17.7205[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]110[/C][C]0.2901[/C][C]0.0208[/C][C]0.0622[/C][C]0.7748[/C][C]9.2477[/C][C]3.041[/C][/ROW]
[ROW][C]111[/C][C]0.3929[/C][C]0.0623[/C][C]0.0622[/C][C]7.5417[/C][C]8.679[/C][C]2.946[/C][/ROW]
[ROW][C]112[/C][C]0.5295[/C][C]0.0895[/C][C]0.069[/C][C]17.4628[/C][C]10.875[/C][C]3.2977[/C][/ROW]
[ROW][C]113[/C][C]0.6753[/C][C]0.1652[/C][C]0.0883[/C][C]67.4506[/C][C]22.1901[/C][C]4.7106[/C][/ROW]
[ROW][C]114[/C][C]0.8571[/C][C]0.3078[/C][C]0.1248[/C][C]260.5804[/C][C]61.9218[/C][C]7.869[/C][/ROW]
[ROW][C]115[/C][C]1.0009[/C][C]0.2412[/C][C]0.1415[/C][C]159.2088[/C][C]75.8199[/C][C]8.7075[/C][/ROW]
[ROW][C]116[/C][C]1.0468[/C][C]0.5287[/C][C]0.1899[/C][C]628.7323[/C][C]144.934[/C][C]12.0389[/C][/ROW]
[ROW][C]117[/C][C]1.0723[/C][C]0.569[/C][C]0.232[/C][C]602.6388[/C][C]195.7901[/C][C]13.9925[/C][/ROW]
[ROW][C]118[/C][C]0.9637[/C][C]1.0835[/C][C]0.3172[/C][C]1448.6577[/C][C]321.0768[/C][C]17.9186[/C][/ROW]
[ROW][C]119[/C][C]0.842[/C][C]1.7455[/C][C]0.447[/C][C]2398.9572[/C][C]509.9751[/C][C]22.5826[/C][/ROW]
[ROW][C]120[/C][C]0.7683[/C][C]2.1893[/C][C]0.5922[/C][C]2627.3453[/C][C]686.4226[/C][C]26.1997[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115434&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115434&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
1090.1630.1036017.720500
1100.29010.02080.06220.77489.24773.041
1110.39290.06230.06227.54178.6792.946
1120.52950.08950.06917.462810.8753.2977
1130.67530.16520.088367.450622.19014.7106
1140.85710.30780.1248260.580461.92187.869
1151.00090.24120.1415159.208875.81998.7075
1161.04680.52870.1899628.7323144.93412.0389
1171.07230.5690.232602.6388195.790113.9925
1180.96371.08350.31721448.6577321.076817.9186
1190.8421.74550.4472398.9572509.975122.5826
1200.76832.18930.59222627.3453686.422626.1997


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = FALSE ; par2 = -0.5 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 0 ;
Parameters (R input):
par1 = 12 ; par2 = -0.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 1 ; par7 = 1 ; par8 = 0 ; 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')