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 12:52:23 -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/t12299756157m5h3n8drwngnvn.htm/, Retrieved Sun, 12 May 2024 14:46:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=36191, Retrieved Sun, 12 May 2024 14:46:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact151

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]
-    D          [ARIMA Backward Selection] [arima backw sel d...] [2008-12-22 10:29:20] [fad8a251ac01c156a8ae23a83577546f]
- RMPD            [ARIMA Forecasting] [] [2008-12-22 19:10:36] [b98453cac15ba1066b407e146608df68]
-                     [ARIMA Forecasting] [forecasting duur ...] [2008-12-22 19:52:23] [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 time3 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 & 3 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=36191&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]3 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=36191&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=36191&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 time3 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-------
4979.694.366.7224121.87760.14810.28480.50.2848
5078.285.658.0224113.17760.29950.66510.50.1176
51103.6117.489.8224144.97760.16330.99730.50.8584
5277.888.560.9224116.07760.22350.14160.50.1633
5399.1124.296.6224151.77760.03720.99950.50.9402
54105.7119.391.7224146.87760.16690.92450.50.8865
5584.176.849.2224104.37760.30190.020.50.035
5688.770.643.022498.17760.09920.16870.50.0121
57108122.194.5224149.67760.15810.99120.50.9203
5898.1109.581.9224137.07760.20890.54250.50.6956
59101119.992.3224147.47760.08960.93940.50.8945
6082102.374.7224129.87760.07450.53680.50.5

\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 & 94.3 & 66.7224 & 121.8776 & 0.1481 & 0.2848 & 0.5 & 0.2848 \tabularnewline
50 & 78.2 & 85.6 & 58.0224 & 113.1776 & 0.2995 & 0.6651 & 0.5 & 0.1176 \tabularnewline
51 & 103.6 & 117.4 & 89.8224 & 144.9776 & 0.1633 & 0.9973 & 0.5 & 0.8584 \tabularnewline
52 & 77.8 & 88.5 & 60.9224 & 116.0776 & 0.2235 & 0.1416 & 0.5 & 0.1633 \tabularnewline
53 & 99.1 & 124.2 & 96.6224 & 151.7776 & 0.0372 & 0.9995 & 0.5 & 0.9402 \tabularnewline
54 & 105.7 & 119.3 & 91.7224 & 146.8776 & 0.1669 & 0.9245 & 0.5 & 0.8865 \tabularnewline
55 & 84.1 & 76.8 & 49.2224 & 104.3776 & 0.3019 & 0.02 & 0.5 & 0.035 \tabularnewline
56 & 88.7 & 70.6 & 43.0224 & 98.1776 & 0.0992 & 0.1687 & 0.5 & 0.0121 \tabularnewline
57 & 108 & 122.1 & 94.5224 & 149.6776 & 0.1581 & 0.9912 & 0.5 & 0.9203 \tabularnewline
58 & 98.1 & 109.5 & 81.9224 & 137.0776 & 0.2089 & 0.5425 & 0.5 & 0.6956 \tabularnewline
59 & 101 & 119.9 & 92.3224 & 147.4776 & 0.0896 & 0.9394 & 0.5 & 0.8945 \tabularnewline
60 & 82 & 102.3 & 74.7224 & 129.8776 & 0.0745 & 0.5368 & 0.5 & 0.5 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=36191&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]94.3[/C][C]66.7224[/C][C]121.8776[/C][C]0.1481[/C][C]0.2848[/C][C]0.5[/C][C]0.2848[/C][/ROW]
[ROW][C]50[/C][C]78.2[/C][C]85.6[/C][C]58.0224[/C][C]113.1776[/C][C]0.2995[/C][C]0.6651[/C][C]0.5[/C][C]0.1176[/C][/ROW]
[ROW][C]51[/C][C]103.6[/C][C]117.4[/C][C]89.8224[/C][C]144.9776[/C][C]0.1633[/C][C]0.9973[/C][C]0.5[/C][C]0.8584[/C][/ROW]
[ROW][C]52[/C][C]77.8[/C][C]88.5[/C][C]60.9224[/C][C]116.0776[/C][C]0.2235[/C][C]0.1416[/C][C]0.5[/C][C]0.1633[/C][/ROW]
[ROW][C]53[/C][C]99.1[/C][C]124.2[/C][C]96.6224[/C][C]151.7776[/C][C]0.0372[/C][C]0.9995[/C][C]0.5[/C][C]0.9402[/C][/ROW]
[ROW][C]54[/C][C]105.7[/C][C]119.3[/C][C]91.7224[/C][C]146.8776[/C][C]0.1669[/C][C]0.9245[/C][C]0.5[/C][C]0.8865[/C][/ROW]
[ROW][C]55[/C][C]84.1[/C][C]76.8[/C][C]49.2224[/C][C]104.3776[/C][C]0.3019[/C][C]0.02[/C][C]0.5[/C][C]0.035[/C][/ROW]
[ROW][C]56[/C][C]88.7[/C][C]70.6[/C][C]43.0224[/C][C]98.1776[/C][C]0.0992[/C][C]0.1687[/C][C]0.5[/C][C]0.0121[/C][/ROW]
[ROW][C]57[/C][C]108[/C][C]122.1[/C][C]94.5224[/C][C]149.6776[/C][C]0.1581[/C][C]0.9912[/C][C]0.5[/C][C]0.9203[/C][/ROW]
[ROW][C]58[/C][C]98.1[/C][C]109.5[/C][C]81.9224[/C][C]137.0776[/C][C]0.2089[/C][C]0.5425[/C][C]0.5[/C][C]0.6956[/C][/ROW]
[ROW][C]59[/C][C]101[/C][C]119.9[/C][C]92.3224[/C][C]147.4776[/C][C]0.0896[/C][C]0.9394[/C][C]0.5[/C][C]0.8945[/C][/ROW]
[ROW][C]60[/C][C]82[/C][C]102.3[/C][C]74.7224[/C][C]129.8776[/C][C]0.0745[/C][C]0.5368[/C][C]0.5[/C][C]0.5[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=36191&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=36191&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-------
4979.694.366.7224121.87760.14810.28480.50.2848
5078.285.658.0224113.17760.29950.66510.50.1176
51103.6117.489.8224144.97760.16330.99730.50.8584
5277.888.560.9224116.07760.22350.14160.50.1633
5399.1124.296.6224151.77760.03720.99950.50.9402
54105.7119.391.7224146.87760.16690.92450.50.8865
5584.176.849.2224104.37760.30190.020.50.035
5688.770.643.022498.17760.09920.16870.50.0121
57108122.194.5224149.67760.15810.99120.50.9203
5898.1109.581.9224137.07760.20890.54250.50.6956
59101119.992.3224147.47760.08960.93940.50.8945
6082102.374.7224129.87760.07450.53680.50.5






Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
490.1492-0.15590.013216.0918.00754.2435
500.1644-0.08640.007254.764.56332.1362
510.1198-0.11750.0098190.4415.873.9837
520.159-0.12090.0101114.499.54083.0888
530.1133-0.20210.0168630.0152.50087.2457
540.1179-0.1140.0095184.9615.41333.926
550.18320.09510.007953.294.44082.1073
560.19930.25640.0214327.6127.30085.225
570.1152-0.11550.0096198.8116.56754.0703
580.1285-0.10410.0087129.9610.833.2909
590.1173-0.15760.0131357.2129.76755.456
600.1375-0.19840.0165412.0934.34085.8601

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
49 & 0.1492 & -0.1559 & 0.013 & 216.09 & 18.0075 & 4.2435 \tabularnewline
50 & 0.1644 & -0.0864 & 0.0072 & 54.76 & 4.5633 & 2.1362 \tabularnewline
51 & 0.1198 & -0.1175 & 0.0098 & 190.44 & 15.87 & 3.9837 \tabularnewline
52 & 0.159 & -0.1209 & 0.0101 & 114.49 & 9.5408 & 3.0888 \tabularnewline
53 & 0.1133 & -0.2021 & 0.0168 & 630.01 & 52.5008 & 7.2457 \tabularnewline
54 & 0.1179 & -0.114 & 0.0095 & 184.96 & 15.4133 & 3.926 \tabularnewline
55 & 0.1832 & 0.0951 & 0.0079 & 53.29 & 4.4408 & 2.1073 \tabularnewline
56 & 0.1993 & 0.2564 & 0.0214 & 327.61 & 27.3008 & 5.225 \tabularnewline
57 & 0.1152 & -0.1155 & 0.0096 & 198.81 & 16.5675 & 4.0703 \tabularnewline
58 & 0.1285 & -0.1041 & 0.0087 & 129.96 & 10.83 & 3.2909 \tabularnewline
59 & 0.1173 & -0.1576 & 0.0131 & 357.21 & 29.7675 & 5.456 \tabularnewline
60 & 0.1375 & -0.1984 & 0.0165 & 412.09 & 34.3408 & 5.8601 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=36191&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]0.1492[/C][C]-0.1559[/C][C]0.013[/C][C]216.09[/C][C]18.0075[/C][C]4.2435[/C][/ROW]
[ROW][C]50[/C][C]0.1644[/C][C]-0.0864[/C][C]0.0072[/C][C]54.76[/C][C]4.5633[/C][C]2.1362[/C][/ROW]
[ROW][C]51[/C][C]0.1198[/C][C]-0.1175[/C][C]0.0098[/C][C]190.44[/C][C]15.87[/C][C]3.9837[/C][/ROW]
[ROW][C]52[/C][C]0.159[/C][C]-0.1209[/C][C]0.0101[/C][C]114.49[/C][C]9.5408[/C][C]3.0888[/C][/ROW]
[ROW][C]53[/C][C]0.1133[/C][C]-0.2021[/C][C]0.0168[/C][C]630.01[/C][C]52.5008[/C][C]7.2457[/C][/ROW]
[ROW][C]54[/C][C]0.1179[/C][C]-0.114[/C][C]0.0095[/C][C]184.96[/C][C]15.4133[/C][C]3.926[/C][/ROW]
[ROW][C]55[/C][C]0.1832[/C][C]0.0951[/C][C]0.0079[/C][C]53.29[/C][C]4.4408[/C][C]2.1073[/C][/ROW]
[ROW][C]56[/C][C]0.1993[/C][C]0.2564[/C][C]0.0214[/C][C]327.61[/C][C]27.3008[/C][C]5.225[/C][/ROW]
[ROW][C]57[/C][C]0.1152[/C][C]-0.1155[/C][C]0.0096[/C][C]198.81[/C][C]16.5675[/C][C]4.0703[/C][/ROW]
[ROW][C]58[/C][C]0.1285[/C][C]-0.1041[/C][C]0.0087[/C][C]129.96[/C][C]10.83[/C][C]3.2909[/C][/ROW]
[ROW][C]59[/C][C]0.1173[/C][C]-0.1576[/C][C]0.0131[/C][C]357.21[/C][C]29.7675[/C][C]5.456[/C][/ROW]
[ROW][C]60[/C][C]0.1375[/C][C]-0.1984[/C][C]0.0165[/C][C]412.09[/C][C]34.3408[/C][C]5.8601[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=36191&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=36191&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
490.1492-0.15590.013216.0918.00754.2435
500.1644-0.08640.007254.764.56332.1362
510.1198-0.11750.0098190.4415.873.9837
520.159-0.12090.0101114.499.54083.0888
530.1133-0.20210.0168630.0152.50087.2457
540.1179-0.1140.0095184.9615.41333.926
550.18320.09510.007953.294.44082.1073
560.19930.25640.0214327.6127.30085.225
570.1152-0.11550.0096198.8116.56754.0703
580.1285-0.10410.0087129.9610.833.2909
590.1173-0.15760.0131357.2129.76755.456
600.1375-0.19840.0165412.0934.34085.8601


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 12 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 0 ; par7 = 0 ; par8 = 0 ; par9 = 0 ; par10 = FALSE ;
Parameters (R input):
par1 = 12 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 0 ; par7 = 0 ; par8 = 0 ; par9 = 0 ; par10 = FALSE ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')