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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 computationMon, 21 Dec 2009 01:49:19 -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/21/t12613854024qf0tcu6f24u0p8.htm/, Retrieved Sun, 05 May 2024 16:35:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=70071, Retrieved Sun, 05 May 2024 16:35:24 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact150

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [ARIMA Forecasting] [hfdst 21 arima fo...] [2008-12-15 08:32:33] [11edab5c4db3615abbf782b1c6e7cacf]
-   PD  [ARIMA Forecasting] [Gilliam Schoorel] [2008-12-15 20:58:10] [74be16979710d4c4e7c6647856088456]
-   PD    [ARIMA Forecasting] [Gilliam Schoorel ...] [2008-12-16 11:51:33] [74be16979710d4c4e7c6647856088456]
-   P       [ARIMA Forecasting] [Gilliam Schoorel] [2008-12-18 18:40:57] [74be16979710d4c4e7c6647856088456]
-             [ARIMA Forecasting] [Toon Wouters] [2008-12-19 07:57:41] [74be16979710d4c4e7c6647856088456]
- RM              [ARIMA Forecasting] [Sören Van Donink ...] [2009-12-21 08:49:19] [56eb6eb137e5652a8f2309d1e9c805c5] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
101.0
98.7
105.1
98.4
101.7
102.9
92.2
94.9
92.8
98.5
94.3
87.4
103.4
101.2
109.6
111.9
108.9
105.6
107.8
97.5
102.4
105.6
99.8
96.2
113.1
107.4
116.8
112.9
105.3
109.3
107.9
101.1
114.7
116.2
108.4
113.4
108.7
112.6
124.2
114.9
110.5
121.5
118.1
111.7
132.7
119.0
116.7
120.1
113.4
106.6
116.3
112.6
111.6
125.1
110.7
109.6
114.2
113.4
116.0
109.6
117.8
115.8
125.3
113.0
120.5
116.6
111.8
115.2
118.6
122.4
116.4
114.5
119.8
115.8
127.8
118.8
119.7
118.6
120.8
115.9
109.7
114.8
116.2
112.2

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'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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70071&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70071&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70071&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'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[72])
60109.6-------
61117.8-------
62115.8-------
63125.3-------
64113-------
65120.5-------
66116.6-------
67111.8-------
68115.2-------
69118.6-------
70122.4-------
71116.4-------
72114.5-------
73119.8121.6254111.1939132.0570.36580.90970.76390.9097
74115.8118.44107.5576129.32240.31720.40320.68280.761
75127.8127.6941116.0314139.35690.49290.97720.65630.9867
76118.8122.29109.08135.49990.30230.20680.9160.8761
77119.7121.2555107.3383135.17260.41330.63530.54240.8293
78118.6125.0433110.2949139.79160.19590.76120.86910.9194
79120.8119.6651104.0144135.31590.44350.55310.83770.7411
80115.9116.551100.1824132.91960.46890.30550.56430.597
81109.7124.1042107.0016141.20670.04940.82640.73590.8645
82114.8124.0848106.2572141.91250.15370.94310.57350.854
83116.2120.1878101.698138.67760.33620.7160.6560.7267
84112.2118.339799.2052137.47420.26470.58670.6530.653

\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[72]) \tabularnewline
60 & 109.6 & - & - & - & - & - & - & - \tabularnewline
61 & 117.8 & - & - & - & - & - & - & - \tabularnewline
62 & 115.8 & - & - & - & - & - & - & - \tabularnewline
63 & 125.3 & - & - & - & - & - & - & - \tabularnewline
64 & 113 & - & - & - & - & - & - & - \tabularnewline
65 & 120.5 & - & - & - & - & - & - & - \tabularnewline
66 & 116.6 & - & - & - & - & - & - & - \tabularnewline
67 & 111.8 & - & - & - & - & - & - & - \tabularnewline
68 & 115.2 & - & - & - & - & - & - & - \tabularnewline
69 & 118.6 & - & - & - & - & - & - & - \tabularnewline
70 & 122.4 & - & - & - & - & - & - & - \tabularnewline
71 & 116.4 & - & - & - & - & - & - & - \tabularnewline
72 & 114.5 & - & - & - & - & - & - & - \tabularnewline
73 & 119.8 & 121.6254 & 111.1939 & 132.057 & 0.3658 & 0.9097 & 0.7639 & 0.9097 \tabularnewline
74 & 115.8 & 118.44 & 107.5576 & 129.3224 & 0.3172 & 0.4032 & 0.6828 & 0.761 \tabularnewline
75 & 127.8 & 127.6941 & 116.0314 & 139.3569 & 0.4929 & 0.9772 & 0.6563 & 0.9867 \tabularnewline
76 & 118.8 & 122.29 & 109.08 & 135.4999 & 0.3023 & 0.2068 & 0.916 & 0.8761 \tabularnewline
77 & 119.7 & 121.2555 & 107.3383 & 135.1726 & 0.4133 & 0.6353 & 0.5424 & 0.8293 \tabularnewline
78 & 118.6 & 125.0433 & 110.2949 & 139.7916 & 0.1959 & 0.7612 & 0.8691 & 0.9194 \tabularnewline
79 & 120.8 & 119.6651 & 104.0144 & 135.3159 & 0.4435 & 0.5531 & 0.8377 & 0.7411 \tabularnewline
80 & 115.9 & 116.551 & 100.1824 & 132.9196 & 0.4689 & 0.3055 & 0.5643 & 0.597 \tabularnewline
81 & 109.7 & 124.1042 & 107.0016 & 141.2067 & 0.0494 & 0.8264 & 0.7359 & 0.8645 \tabularnewline
82 & 114.8 & 124.0848 & 106.2572 & 141.9125 & 0.1537 & 0.9431 & 0.5735 & 0.854 \tabularnewline
83 & 116.2 & 120.1878 & 101.698 & 138.6776 & 0.3362 & 0.716 & 0.656 & 0.7267 \tabularnewline
84 & 112.2 & 118.3397 & 99.2052 & 137.4742 & 0.2647 & 0.5867 & 0.653 & 0.653 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70071&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[72])[/C][/ROW]
[ROW][C]60[/C][C]109.6[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]61[/C][C]117.8[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]62[/C][C]115.8[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]63[/C][C]125.3[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]64[/C][C]113[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]65[/C][C]120.5[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]66[/C][C]116.6[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]67[/C][C]111.8[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]68[/C][C]115.2[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]69[/C][C]118.6[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]70[/C][C]122.4[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]71[/C][C]116.4[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]72[/C][C]114.5[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]73[/C][C]119.8[/C][C]121.6254[/C][C]111.1939[/C][C]132.057[/C][C]0.3658[/C][C]0.9097[/C][C]0.7639[/C][C]0.9097[/C][/ROW]
[ROW][C]74[/C][C]115.8[/C][C]118.44[/C][C]107.5576[/C][C]129.3224[/C][C]0.3172[/C][C]0.4032[/C][C]0.6828[/C][C]0.761[/C][/ROW]
[ROW][C]75[/C][C]127.8[/C][C]127.6941[/C][C]116.0314[/C][C]139.3569[/C][C]0.4929[/C][C]0.9772[/C][C]0.6563[/C][C]0.9867[/C][/ROW]
[ROW][C]76[/C][C]118.8[/C][C]122.29[/C][C]109.08[/C][C]135.4999[/C][C]0.3023[/C][C]0.2068[/C][C]0.916[/C][C]0.8761[/C][/ROW]
[ROW][C]77[/C][C]119.7[/C][C]121.2555[/C][C]107.3383[/C][C]135.1726[/C][C]0.4133[/C][C]0.6353[/C][C]0.5424[/C][C]0.8293[/C][/ROW]
[ROW][C]78[/C][C]118.6[/C][C]125.0433[/C][C]110.2949[/C][C]139.7916[/C][C]0.1959[/C][C]0.7612[/C][C]0.8691[/C][C]0.9194[/C][/ROW]
[ROW][C]79[/C][C]120.8[/C][C]119.6651[/C][C]104.0144[/C][C]135.3159[/C][C]0.4435[/C][C]0.5531[/C][C]0.8377[/C][C]0.7411[/C][/ROW]
[ROW][C]80[/C][C]115.9[/C][C]116.551[/C][C]100.1824[/C][C]132.9196[/C][C]0.4689[/C][C]0.3055[/C][C]0.5643[/C][C]0.597[/C][/ROW]
[ROW][C]81[/C][C]109.7[/C][C]124.1042[/C][C]107.0016[/C][C]141.2067[/C][C]0.0494[/C][C]0.8264[/C][C]0.7359[/C][C]0.8645[/C][/ROW]
[ROW][C]82[/C][C]114.8[/C][C]124.0848[/C][C]106.2572[/C][C]141.9125[/C][C]0.1537[/C][C]0.9431[/C][C]0.5735[/C][C]0.854[/C][/ROW]
[ROW][C]83[/C][C]116.2[/C][C]120.1878[/C][C]101.698[/C][C]138.6776[/C][C]0.3362[/C][C]0.716[/C][C]0.656[/C][C]0.7267[/C][/ROW]
[ROW][C]84[/C][C]112.2[/C][C]118.3397[/C][C]99.2052[/C][C]137.4742[/C][C]0.2647[/C][C]0.5867[/C][C]0.653[/C][C]0.653[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70071&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70071&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[72])
60109.6-------
61117.8-------
62115.8-------
63125.3-------
64113-------
65120.5-------
66116.6-------
67111.8-------
68115.2-------
69118.6-------
70122.4-------
71116.4-------
72114.5-------
73119.8121.6254111.1939132.0570.36580.90970.76390.9097
74115.8118.44107.5576129.32240.31720.40320.68280.761
75127.8127.6941116.0314139.35690.49290.97720.65630.9867
76118.8122.29109.08135.49990.30230.20680.9160.8761
77119.7121.2555107.3383135.17260.41330.63530.54240.8293
78118.6125.0433110.2949139.79160.19590.76120.86910.9194
79120.8119.6651104.0144135.31590.44350.55310.83770.7411
80115.9116.551100.1824132.91960.46890.30550.56430.597
81109.7124.1042107.0016141.20670.04940.82640.73590.8645
82114.8124.0848106.2572141.91250.15370.94310.57350.854
83116.2120.1878101.698138.67760.33620.7160.6560.7267
84112.2118.339799.2052137.47420.26470.58670.6530.653






Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
730.0438-0.0150.00133.33220.27770.527
740.0469-0.02230.00196.96950.58080.7621
750.04668e-041e-040.01129e-040.0306
760.0551-0.02850.002412.17981.0151.0075
770.0586-0.01280.00112.41950.20160.449
780.0602-0.05150.004341.51583.45971.86
790.06670.00958e-041.28790.10730.3276
800.0717-0.00565e-040.42380.03530.1879
810.0703-0.11610.0097207.480117.294.1581
820.0733-0.07480.006286.20797.1842.6803
830.0785-0.03320.002815.90281.32521.1512
840.0825-0.05190.004337.69563.14131.7724

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
73 & 0.0438 & -0.015 & 0.0013 & 3.3322 & 0.2777 & 0.527 \tabularnewline
74 & 0.0469 & -0.0223 & 0.0019 & 6.9695 & 0.5808 & 0.7621 \tabularnewline
75 & 0.0466 & 8e-04 & 1e-04 & 0.0112 & 9e-04 & 0.0306 \tabularnewline
76 & 0.0551 & -0.0285 & 0.0024 & 12.1798 & 1.015 & 1.0075 \tabularnewline
77 & 0.0586 & -0.0128 & 0.0011 & 2.4195 & 0.2016 & 0.449 \tabularnewline
78 & 0.0602 & -0.0515 & 0.0043 & 41.5158 & 3.4597 & 1.86 \tabularnewline
79 & 0.0667 & 0.0095 & 8e-04 & 1.2879 & 0.1073 & 0.3276 \tabularnewline
80 & 0.0717 & -0.0056 & 5e-04 & 0.4238 & 0.0353 & 0.1879 \tabularnewline
81 & 0.0703 & -0.1161 & 0.0097 & 207.4801 & 17.29 & 4.1581 \tabularnewline
82 & 0.0733 & -0.0748 & 0.0062 & 86.2079 & 7.184 & 2.6803 \tabularnewline
83 & 0.0785 & -0.0332 & 0.0028 & 15.9028 & 1.3252 & 1.1512 \tabularnewline
84 & 0.0825 & -0.0519 & 0.0043 & 37.6956 & 3.1413 & 1.7724 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70071&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]73[/C][C]0.0438[/C][C]-0.015[/C][C]0.0013[/C][C]3.3322[/C][C]0.2777[/C][C]0.527[/C][/ROW]
[ROW][C]74[/C][C]0.0469[/C][C]-0.0223[/C][C]0.0019[/C][C]6.9695[/C][C]0.5808[/C][C]0.7621[/C][/ROW]
[ROW][C]75[/C][C]0.0466[/C][C]8e-04[/C][C]1e-04[/C][C]0.0112[/C][C]9e-04[/C][C]0.0306[/C][/ROW]
[ROW][C]76[/C][C]0.0551[/C][C]-0.0285[/C][C]0.0024[/C][C]12.1798[/C][C]1.015[/C][C]1.0075[/C][/ROW]
[ROW][C]77[/C][C]0.0586[/C][C]-0.0128[/C][C]0.0011[/C][C]2.4195[/C][C]0.2016[/C][C]0.449[/C][/ROW]
[ROW][C]78[/C][C]0.0602[/C][C]-0.0515[/C][C]0.0043[/C][C]41.5158[/C][C]3.4597[/C][C]1.86[/C][/ROW]
[ROW][C]79[/C][C]0.0667[/C][C]0.0095[/C][C]8e-04[/C][C]1.2879[/C][C]0.1073[/C][C]0.3276[/C][/ROW]
[ROW][C]80[/C][C]0.0717[/C][C]-0.0056[/C][C]5e-04[/C][C]0.4238[/C][C]0.0353[/C][C]0.1879[/C][/ROW]
[ROW][C]81[/C][C]0.0703[/C][C]-0.1161[/C][C]0.0097[/C][C]207.4801[/C][C]17.29[/C][C]4.1581[/C][/ROW]
[ROW][C]82[/C][C]0.0733[/C][C]-0.0748[/C][C]0.0062[/C][C]86.2079[/C][C]7.184[/C][C]2.6803[/C][/ROW]
[ROW][C]83[/C][C]0.0785[/C][C]-0.0332[/C][C]0.0028[/C][C]15.9028[/C][C]1.3252[/C][C]1.1512[/C][/ROW]
[ROW][C]84[/C][C]0.0825[/C][C]-0.0519[/C][C]0.0043[/C][C]37.6956[/C][C]3.1413[/C][C]1.7724[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70071&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70071&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
730.0438-0.0150.00133.33220.27770.527
740.0469-0.02230.00196.96950.58080.7621
750.04668e-041e-040.01129e-040.0306
760.0551-0.02850.002412.17981.0151.0075
770.0586-0.01280.00112.41950.20160.449
780.0602-0.05150.004341.51583.45971.86
790.06670.00958e-041.28790.10730.3276
800.0717-0.00565e-040.42380.03530.1879
810.0703-0.11610.0097207.480117.294.1581
820.0733-0.07480.006286.20797.1842.6803
830.0785-0.03320.002815.90281.32521.1512
840.0825-0.05190.004337.69563.14131.7724


Figures (Output of Computation)

Input Parameters & R Code

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