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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 computationTue, 15 Dec 2009 13:24:19 -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/15/t1260908794g6k7pumc2ujxcqw.htm/, Retrieved Wed, 08 May 2024 09:55:39 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=68126, Retrieved Wed, 08 May 2024 09:55:39 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact133

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [ARIMA Forecasting] [] [2009-12-06 10:44:58] [1e83ffa964db6f7ea6ccc4e7b5acbbff]
-   PD  [ARIMA Forecasting] [ws 10 deel 2 prblm] [2009-12-09 19:29:01] [134dc66689e3d457a82860db6471d419]
-   P     [ARIMA Forecasting] [ws 10 deel 2 arim...] [2009-12-12 09:45:03] [134dc66689e3d457a82860db6471d419]
-   P       [ARIMA Forecasting] [Paper ARIMA F IGP] [2009-12-14 21:07:10] [134dc66689e3d457a82860db6471d419]
-   P           [ARIMA Forecasting] [Paper ARIMA F IGP 12] [2009-12-15 20:24:19] [4f297b039e1043ebee7ff7a83b1eaaaa] [Current]
- R PD            [ARIMA Forecasting] [Paper arima forec...] [2009-12-23 22:37:42] [62d3ced7fb1c10c35a82e9cb1d0d0e2b]
- R PD            [ARIMA Forecasting] [Paper arima forec...] [2009-12-23 23:25:41] [62d3ced7fb1c10c35a82e9cb1d0d0e2b]
Feedback Forum

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
100.01
103.84
104.48
95.43
104.80
108.64
105.65
108.42
115.35
113.64
115.24
100.33
101.29
104.48
99.26
100.11
103.52
101.18
96.39
97.56
96.39
85.10
79.77
79.13
80.84
82.75
92.55
96.60
96.92
95.32
98.52
100.22
104.91
103.10
97.13
103.42
111.72
118.11
111.62
100.22
102.03
105.76
107.68
110.77
105.44
112.26
114.07
117.90
124.72
126.42
134.73
135.79
143.36
140.37
144.74
151.98
150.92
163.38
154.43
146.66
157.95
162.10
180.42
179.57
171.58
185.43
190.64
203.00
202.36
193.41
186.17
192.24
209.60
206.41
209.82
230.37
235.80
232.07
244.64
242.19
217.48
209.39
211.73
221.00
203.11
214.71
224.19
238.04
238.36
246.24
259.87
249.97
266.48
282.98
306.31
301.73
314.62
332.62
355.51
370.32
408.13
433.58
440.51
386.29
342.84
254.97
203.42
170.09
174.03
167.85
177.01
188.19
211.20
240.91
230.26
251.25
241.66

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68126&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]1 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=68126&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68126&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 time1 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[105])
93266.48-------
94282.98-------
95306.31-------
96301.73-------
97314.62-------
98332.62-------
99355.51-------
100370.32-------
101408.13-------
102433.58-------
103440.51-------
104386.29-------
105342.84-------
106254.97338.6286281.2533419.19360.02090.45920.91210.4592
107203.42339.1741258.4194474.69390.02480.88840.68270.4789
108170.09338.8421242.681524.20380.03720.92390.65260.4831
109174.03339.4921231.2819574.71030.0840.9210.58210.4889
110167.85340.2756222.1602626.57210.11890.87250.52090.493
111177.01341.1884214.5664681.02890.17180.84130.46710.4962
112188.19341.7173207.8579737.21710.22340.79280.44360.4978
113211.2342.974202.2571800.02240.2860.74660.390.5002
114240.91343.7053197.0617865.43040.34970.69070.36780.5013
115230.26343.8762192.1673933.14440.35280.6340.37390.5014
116251.25342.2549187.0695995.24320.39240.63160.44740.4993
117241.66340.6223182.38141060.68860.39380.59610.49760.4976

\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[105]) \tabularnewline
93 & 266.48 & - & - & - & - & - & - & - \tabularnewline
94 & 282.98 & - & - & - & - & - & - & - \tabularnewline
95 & 306.31 & - & - & - & - & - & - & - \tabularnewline
96 & 301.73 & - & - & - & - & - & - & - \tabularnewline
97 & 314.62 & - & - & - & - & - & - & - \tabularnewline
98 & 332.62 & - & - & - & - & - & - & - \tabularnewline
99 & 355.51 & - & - & - & - & - & - & - \tabularnewline
100 & 370.32 & - & - & - & - & - & - & - \tabularnewline
101 & 408.13 & - & - & - & - & - & - & - \tabularnewline
102 & 433.58 & - & - & - & - & - & - & - \tabularnewline
103 & 440.51 & - & - & - & - & - & - & - \tabularnewline
104 & 386.29 & - & - & - & - & - & - & - \tabularnewline
105 & 342.84 & - & - & - & - & - & - & - \tabularnewline
106 & 254.97 & 338.6286 & 281.2533 & 419.1936 & 0.0209 & 0.4592 & 0.9121 & 0.4592 \tabularnewline
107 & 203.42 & 339.1741 & 258.4194 & 474.6939 & 0.0248 & 0.8884 & 0.6827 & 0.4789 \tabularnewline
108 & 170.09 & 338.8421 & 242.681 & 524.2038 & 0.0372 & 0.9239 & 0.6526 & 0.4831 \tabularnewline
109 & 174.03 & 339.4921 & 231.2819 & 574.7103 & 0.084 & 0.921 & 0.5821 & 0.4889 \tabularnewline
110 & 167.85 & 340.2756 & 222.1602 & 626.5721 & 0.1189 & 0.8725 & 0.5209 & 0.493 \tabularnewline
111 & 177.01 & 341.1884 & 214.5664 & 681.0289 & 0.1718 & 0.8413 & 0.4671 & 0.4962 \tabularnewline
112 & 188.19 & 341.7173 & 207.8579 & 737.2171 & 0.2234 & 0.7928 & 0.4436 & 0.4978 \tabularnewline
113 & 211.2 & 342.974 & 202.2571 & 800.0224 & 0.286 & 0.7466 & 0.39 & 0.5002 \tabularnewline
114 & 240.91 & 343.7053 & 197.0617 & 865.4304 & 0.3497 & 0.6907 & 0.3678 & 0.5013 \tabularnewline
115 & 230.26 & 343.8762 & 192.1673 & 933.1444 & 0.3528 & 0.634 & 0.3739 & 0.5014 \tabularnewline
116 & 251.25 & 342.2549 & 187.0695 & 995.2432 & 0.3924 & 0.6316 & 0.4474 & 0.4993 \tabularnewline
117 & 241.66 & 340.6223 & 182.3814 & 1060.6886 & 0.3938 & 0.5961 & 0.4976 & 0.4976 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68126&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[105])[/C][/ROW]
[ROW][C]93[/C][C]266.48[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]94[/C][C]282.98[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]95[/C][C]306.31[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]96[/C][C]301.73[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]97[/C][C]314.62[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]98[/C][C]332.62[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]99[/C][C]355.51[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]100[/C][C]370.32[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]101[/C][C]408.13[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]102[/C][C]433.58[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]103[/C][C]440.51[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]104[/C][C]386.29[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]105[/C][C]342.84[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]106[/C][C]254.97[/C][C]338.6286[/C][C]281.2533[/C][C]419.1936[/C][C]0.0209[/C][C]0.4592[/C][C]0.9121[/C][C]0.4592[/C][/ROW]
[ROW][C]107[/C][C]203.42[/C][C]339.1741[/C][C]258.4194[/C][C]474.6939[/C][C]0.0248[/C][C]0.8884[/C][C]0.6827[/C][C]0.4789[/C][/ROW]
[ROW][C]108[/C][C]170.09[/C][C]338.8421[/C][C]242.681[/C][C]524.2038[/C][C]0.0372[/C][C]0.9239[/C][C]0.6526[/C][C]0.4831[/C][/ROW]
[ROW][C]109[/C][C]174.03[/C][C]339.4921[/C][C]231.2819[/C][C]574.7103[/C][C]0.084[/C][C]0.921[/C][C]0.5821[/C][C]0.4889[/C][/ROW]
[ROW][C]110[/C][C]167.85[/C][C]340.2756[/C][C]222.1602[/C][C]626.5721[/C][C]0.1189[/C][C]0.8725[/C][C]0.5209[/C][C]0.493[/C][/ROW]
[ROW][C]111[/C][C]177.01[/C][C]341.1884[/C][C]214.5664[/C][C]681.0289[/C][C]0.1718[/C][C]0.8413[/C][C]0.4671[/C][C]0.4962[/C][/ROW]
[ROW][C]112[/C][C]188.19[/C][C]341.7173[/C][C]207.8579[/C][C]737.2171[/C][C]0.2234[/C][C]0.7928[/C][C]0.4436[/C][C]0.4978[/C][/ROW]
[ROW][C]113[/C][C]211.2[/C][C]342.974[/C][C]202.2571[/C][C]800.0224[/C][C]0.286[/C][C]0.7466[/C][C]0.39[/C][C]0.5002[/C][/ROW]
[ROW][C]114[/C][C]240.91[/C][C]343.7053[/C][C]197.0617[/C][C]865.4304[/C][C]0.3497[/C][C]0.6907[/C][C]0.3678[/C][C]0.5013[/C][/ROW]
[ROW][C]115[/C][C]230.26[/C][C]343.8762[/C][C]192.1673[/C][C]933.1444[/C][C]0.3528[/C][C]0.634[/C][C]0.3739[/C][C]0.5014[/C][/ROW]
[ROW][C]116[/C][C]251.25[/C][C]342.2549[/C][C]187.0695[/C][C]995.2432[/C][C]0.3924[/C][C]0.6316[/C][C]0.4474[/C][C]0.4993[/C][/ROW]
[ROW][C]117[/C][C]241.66[/C][C]340.6223[/C][C]182.3814[/C][C]1060.6886[/C][C]0.3938[/C][C]0.5961[/C][C]0.4976[/C][C]0.4976[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68126&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68126&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[105])
93266.48-------
94282.98-------
95306.31-------
96301.73-------
97314.62-------
98332.62-------
99355.51-------
100370.32-------
101408.13-------
102433.58-------
103440.51-------
104386.29-------
105342.84-------
106254.97338.6286281.2533419.19360.02090.45920.91210.4592
107203.42339.1741258.4194474.69390.02480.88840.68270.4789
108170.09338.8421242.681524.20380.03720.92390.65260.4831
109174.03339.4921231.2819574.71030.0840.9210.58210.4889
110167.85340.2756222.1602626.57210.11890.87250.52090.493
111177.01341.1884214.5664681.02890.17180.84130.46710.4962
112188.19341.7173207.8579737.21710.22340.79280.44360.4978
113211.2342.974202.2571800.02240.2860.74660.390.5002
114240.91343.7053197.0617865.43040.34970.69070.36780.5013
115230.26343.8762192.1673933.14440.35280.6340.37390.5014
116251.25342.2549187.0695995.24320.39240.63160.44740.4993
117241.66340.6223182.38141060.68860.39380.59610.49760.4976






Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
1060.1214-0.24710.02066998.7604583.2324.1502
1070.2039-0.40020.033418429.1861535.765539.1888
1080.2791-0.4980.041528477.26042373.10548.7145
1090.3535-0.48740.040627377.69872281.474947.7648
1100.4293-0.50670.042229730.59682477.549749.775
1110.5082-0.48120.040126954.54932246.212447.3942
1120.5905-0.44930.037423570.62961964.219144.3195
1130.6799-0.38420.03217364.39281447.032738.0399
1140.7745-0.29910.024910566.8766880.57329.6745
1150.8743-0.33040.027512908.63521075.719632.7982
1160.9734-0.26590.02228281.8851690.157126.2708
1171.0786-0.29050.02429793.5421816.128528.568

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
106 & 0.1214 & -0.2471 & 0.0206 & 6998.7604 & 583.23 & 24.1502 \tabularnewline
107 & 0.2039 & -0.4002 & 0.0334 & 18429.186 & 1535.7655 & 39.1888 \tabularnewline
108 & 0.2791 & -0.498 & 0.0415 & 28477.2604 & 2373.105 & 48.7145 \tabularnewline
109 & 0.3535 & -0.4874 & 0.0406 & 27377.6987 & 2281.4749 & 47.7648 \tabularnewline
110 & 0.4293 & -0.5067 & 0.0422 & 29730.5968 & 2477.5497 & 49.775 \tabularnewline
111 & 0.5082 & -0.4812 & 0.0401 & 26954.5493 & 2246.2124 & 47.3942 \tabularnewline
112 & 0.5905 & -0.4493 & 0.0374 & 23570.6296 & 1964.2191 & 44.3195 \tabularnewline
113 & 0.6799 & -0.3842 & 0.032 & 17364.3928 & 1447.0327 & 38.0399 \tabularnewline
114 & 0.7745 & -0.2991 & 0.0249 & 10566.8766 & 880.573 & 29.6745 \tabularnewline
115 & 0.8743 & -0.3304 & 0.0275 & 12908.6352 & 1075.7196 & 32.7982 \tabularnewline
116 & 0.9734 & -0.2659 & 0.0222 & 8281.8851 & 690.1571 & 26.2708 \tabularnewline
117 & 1.0786 & -0.2905 & 0.0242 & 9793.5421 & 816.1285 & 28.568 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68126&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]106[/C][C]0.1214[/C][C]-0.2471[/C][C]0.0206[/C][C]6998.7604[/C][C]583.23[/C][C]24.1502[/C][/ROW]
[ROW][C]107[/C][C]0.2039[/C][C]-0.4002[/C][C]0.0334[/C][C]18429.186[/C][C]1535.7655[/C][C]39.1888[/C][/ROW]
[ROW][C]108[/C][C]0.2791[/C][C]-0.498[/C][C]0.0415[/C][C]28477.2604[/C][C]2373.105[/C][C]48.7145[/C][/ROW]
[ROW][C]109[/C][C]0.3535[/C][C]-0.4874[/C][C]0.0406[/C][C]27377.6987[/C][C]2281.4749[/C][C]47.7648[/C][/ROW]
[ROW][C]110[/C][C]0.4293[/C][C]-0.5067[/C][C]0.0422[/C][C]29730.5968[/C][C]2477.5497[/C][C]49.775[/C][/ROW]
[ROW][C]111[/C][C]0.5082[/C][C]-0.4812[/C][C]0.0401[/C][C]26954.5493[/C][C]2246.2124[/C][C]47.3942[/C][/ROW]
[ROW][C]112[/C][C]0.5905[/C][C]-0.4493[/C][C]0.0374[/C][C]23570.6296[/C][C]1964.2191[/C][C]44.3195[/C][/ROW]
[ROW][C]113[/C][C]0.6799[/C][C]-0.3842[/C][C]0.032[/C][C]17364.3928[/C][C]1447.0327[/C][C]38.0399[/C][/ROW]
[ROW][C]114[/C][C]0.7745[/C][C]-0.2991[/C][C]0.0249[/C][C]10566.8766[/C][C]880.573[/C][C]29.6745[/C][/ROW]
[ROW][C]115[/C][C]0.8743[/C][C]-0.3304[/C][C]0.0275[/C][C]12908.6352[/C][C]1075.7196[/C][C]32.7982[/C][/ROW]
[ROW][C]116[/C][C]0.9734[/C][C]-0.2659[/C][C]0.0222[/C][C]8281.8851[/C][C]690.1571[/C][C]26.2708[/C][/ROW]
[ROW][C]117[/C][C]1.0786[/C][C]-0.2905[/C][C]0.0242[/C][C]9793.5421[/C][C]816.1285[/C][C]28.568[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68126&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68126&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
1060.1214-0.24710.02066998.7604583.2324.1502
1070.2039-0.40020.033418429.1861535.765539.1888
1080.2791-0.4980.041528477.26042373.10548.7145
1090.3535-0.48740.040627377.69872281.474947.7648
1100.4293-0.50670.042229730.59682477.549749.775
1110.5082-0.48120.040126954.54932246.212447.3942
1120.5905-0.44930.037423570.62961964.219144.3195
1130.6799-0.38420.03217364.39281447.032738.0399
1140.7745-0.29910.024910566.8766880.57329.6745
1150.8743-0.33040.027512908.63521075.719632.7982
1160.9734-0.26590.02228281.8851690.157126.2708
1171.0786-0.29050.02429793.5421816.128528.568


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 12 ; par2 = -0.7 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 1 ; par7 = 0 ; par8 = 1 ; par9 = 1 ; par10 = FALSE ;
Parameters (R input):
par1 = 12 ; par2 = -0.7 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 1 ; par7 = 0 ; par8 = 1 ; 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')