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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, 13 Dec 2008 07:12:52 -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/13/t1229177627016l3mu8sbetvvf.htm/, Retrieved Fri, 17 May 2024 04:42:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=33109, Retrieved Fri, 17 May 2024 04:42:13 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsIn samenwerking met Roland Feldman
Estimated Impact184

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] [Run sequence plot...] [2008-12-02 22:19:27] [ed2ba3b6182103c15c0ab511ae4e6284]
- RMPD  [Standard Deviation-Mean Plot] [SD mean plot] [2008-12-06 11:49:39] [ed2ba3b6182103c15c0ab511ae4e6284]
F RMP     [(Partial) Autocorrelation Function] [ACF d=1 en D=1 la...] [2008-12-06 13:30:27] [ed2ba3b6182103c15c0ab511ae4e6284]
- RM        [ARIMA Backward Selection] [ARIMA model met q...] [2008-12-06 17:04:18] [4242609301e759e844b9196c1994e4ef]
-   P         [ARIMA Backward Selection] [ARima backward se...] [2008-12-08 11:53:47] [ed2ba3b6182103c15c0ab511ae4e6284]
-   P           [ARIMA Backward Selection] [MA controle] [2008-12-08 11:58:59] [ed2ba3b6182103c15c0ab511ae4e6284]
F                 [ARIMA Backward Selection] [ARIMA] [2008-12-08 20:02:58] [4ad596f10399a71ad29b7d76e6ab90ac]
- RMP                 [ARIMA Forecasting] [ARIMA forecast HI...] [2008-12-13 14:12:52] [a8228479d4547a92e2d3f176a5299609] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
92.66
94.2
94.37
94.45
94.62
94.37
93.43
94.79
94.88
94.79
94.62
94.71
93.77
95.73
95.99
95.82
95.47
95.82
94.71
96.33
96.5
96.16
96.33
96.33
95.05
96.84
96.92
97.44
97.78
97.69
96.67
98.29
98.2
98.71
98.54
98.2
96.92
99.06
99.65
99.82
99.99
100.33
99.31
101.1
101.1
100.93
100.85
100.93
99.6
101.88
101.81
102.38
102.74
102.82
101.72
103.47
102.98
102.68
102.9
103.03
101.29

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'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33109&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33109&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33109&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24






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[49])
3796.92-------
3899.06-------
3999.65-------
4099.82-------
4199.99-------
42100.33-------
4399.31-------
44101.1-------
45101.1-------
46100.93-------
47100.85-------
48100.93-------
4999.6-------
50101.88101.4892101.0029101.97560.0576111
51101.81101.7138101.026102.40160.3920.317911
52102.38102.1346101.2922102.9770.2840.774911
53102.74102.4264101.4537103.39910.26370.537211
54102.82102.4583101.3708103.54580.25720.30580.99991
55101.72101.4383100.247102.62960.32150.01150.99980.9988
56103.47103.1065101.8197104.39320.28990.98270.99891
57102.98103.042101.6664104.41760.46480.2710.99721
58102.68103.3592101.9002104.81830.18080.69480.99941
59102.9103.2147101.6768104.75270.34420.75220.99871
60103.03102.9938101.3808104.60680.48250.54540.99391
61101.29101.6996100.0149103.38440.31680.06080.99270.9927

\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[49]) \tabularnewline
37 & 96.92 & - & - & - & - & - & - & - \tabularnewline
38 & 99.06 & - & - & - & - & - & - & - \tabularnewline
39 & 99.65 & - & - & - & - & - & - & - \tabularnewline
40 & 99.82 & - & - & - & - & - & - & - \tabularnewline
41 & 99.99 & - & - & - & - & - & - & - \tabularnewline
42 & 100.33 & - & - & - & - & - & - & - \tabularnewline
43 & 99.31 & - & - & - & - & - & - & - \tabularnewline
44 & 101.1 & - & - & - & - & - & - & - \tabularnewline
45 & 101.1 & - & - & - & - & - & - & - \tabularnewline
46 & 100.93 & - & - & - & - & - & - & - \tabularnewline
47 & 100.85 & - & - & - & - & - & - & - \tabularnewline
48 & 100.93 & - & - & - & - & - & - & - \tabularnewline
49 & 99.6 & - & - & - & - & - & - & - \tabularnewline
50 & 101.88 & 101.4892 & 101.0029 & 101.9756 & 0.0576 & 1 & 1 & 1 \tabularnewline
51 & 101.81 & 101.7138 & 101.026 & 102.4016 & 0.392 & 0.3179 & 1 & 1 \tabularnewline
52 & 102.38 & 102.1346 & 101.2922 & 102.977 & 0.284 & 0.7749 & 1 & 1 \tabularnewline
53 & 102.74 & 102.4264 & 101.4537 & 103.3991 & 0.2637 & 0.5372 & 1 & 1 \tabularnewline
54 & 102.82 & 102.4583 & 101.3708 & 103.5458 & 0.2572 & 0.3058 & 0.9999 & 1 \tabularnewline
55 & 101.72 & 101.4383 & 100.247 & 102.6296 & 0.3215 & 0.0115 & 0.9998 & 0.9988 \tabularnewline
56 & 103.47 & 103.1065 & 101.8197 & 104.3932 & 0.2899 & 0.9827 & 0.9989 & 1 \tabularnewline
57 & 102.98 & 103.042 & 101.6664 & 104.4176 & 0.4648 & 0.271 & 0.9972 & 1 \tabularnewline
58 & 102.68 & 103.3592 & 101.9002 & 104.8183 & 0.1808 & 0.6948 & 0.9994 & 1 \tabularnewline
59 & 102.9 & 103.2147 & 101.6768 & 104.7527 & 0.3442 & 0.7522 & 0.9987 & 1 \tabularnewline
60 & 103.03 & 102.9938 & 101.3808 & 104.6068 & 0.4825 & 0.5454 & 0.9939 & 1 \tabularnewline
61 & 101.29 & 101.6996 & 100.0149 & 103.3844 & 0.3168 & 0.0608 & 0.9927 & 0.9927 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33109&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[49])[/C][/ROW]
[ROW][C]37[/C][C]96.92[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]38[/C][C]99.06[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]39[/C][C]99.65[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]40[/C][C]99.82[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]41[/C][C]99.99[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]42[/C][C]100.33[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]43[/C][C]99.31[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]44[/C][C]101.1[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]45[/C][C]101.1[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]46[/C][C]100.93[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]47[/C][C]100.85[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]48[/C][C]100.93[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]49[/C][C]99.6[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]50[/C][C]101.88[/C][C]101.4892[/C][C]101.0029[/C][C]101.9756[/C][C]0.0576[/C][C]1[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]51[/C][C]101.81[/C][C]101.7138[/C][C]101.026[/C][C]102.4016[/C][C]0.392[/C][C]0.3179[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]52[/C][C]102.38[/C][C]102.1346[/C][C]101.2922[/C][C]102.977[/C][C]0.284[/C][C]0.7749[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]53[/C][C]102.74[/C][C]102.4264[/C][C]101.4537[/C][C]103.3991[/C][C]0.2637[/C][C]0.5372[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]54[/C][C]102.82[/C][C]102.4583[/C][C]101.3708[/C][C]103.5458[/C][C]0.2572[/C][C]0.3058[/C][C]0.9999[/C][C]1[/C][/ROW]
[ROW][C]55[/C][C]101.72[/C][C]101.4383[/C][C]100.247[/C][C]102.6296[/C][C]0.3215[/C][C]0.0115[/C][C]0.9998[/C][C]0.9988[/C][/ROW]
[ROW][C]56[/C][C]103.47[/C][C]103.1065[/C][C]101.8197[/C][C]104.3932[/C][C]0.2899[/C][C]0.9827[/C][C]0.9989[/C][C]1[/C][/ROW]
[ROW][C]57[/C][C]102.98[/C][C]103.042[/C][C]101.6664[/C][C]104.4176[/C][C]0.4648[/C][C]0.271[/C][C]0.9972[/C][C]1[/C][/ROW]
[ROW][C]58[/C][C]102.68[/C][C]103.3592[/C][C]101.9002[/C][C]104.8183[/C][C]0.1808[/C][C]0.6948[/C][C]0.9994[/C][C]1[/C][/ROW]
[ROW][C]59[/C][C]102.9[/C][C]103.2147[/C][C]101.6768[/C][C]104.7527[/C][C]0.3442[/C][C]0.7522[/C][C]0.9987[/C][C]1[/C][/ROW]
[ROW][C]60[/C][C]103.03[/C][C]102.9938[/C][C]101.3808[/C][C]104.6068[/C][C]0.4825[/C][C]0.5454[/C][C]0.9939[/C][C]1[/C][/ROW]
[ROW][C]61[/C][C]101.29[/C][C]101.6996[/C][C]100.0149[/C][C]103.3844[/C][C]0.3168[/C][C]0.0608[/C][C]0.9927[/C][C]0.9927[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33109&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33109&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[49])
3796.92-------
3899.06-------
3999.65-------
4099.82-------
4199.99-------
42100.33-------
4399.31-------
44101.1-------
45101.1-------
46100.93-------
47100.85-------
48100.93-------
4999.6-------
50101.88101.4892101.0029101.97560.0576111
51101.81101.7138101.026102.40160.3920.317911
52102.38102.1346101.2922102.9770.2840.774911
53102.74102.4264101.4537103.39910.26370.537211
54102.82102.4583101.3708103.54580.25720.30580.99991
55101.72101.4383100.247102.62960.32150.01150.99980.9988
56103.47103.1065101.8197104.39320.28990.98270.99891
57102.98103.042101.6664104.41760.46480.2710.99721
58102.68103.3592101.9002104.81830.18080.69480.99941
59102.9103.2147101.6768104.75270.34420.75220.99871
60103.03102.9938101.3808104.60680.48250.54540.99391
61101.29101.6996100.0149103.38440.31680.06080.99270.9927






Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
500.00240.00393e-040.15270.01270.1128
510.00359e-041e-040.00938e-040.0278
520.00420.00242e-040.06020.0050.0708
530.00480.00313e-040.09840.00820.0905
540.00540.00353e-040.13080.01090.1044
550.0060.00282e-040.07940.00660.0813
560.00640.00353e-040.13220.0110.1049
570.0068-6e-041e-040.00383e-040.0179
580.0072-0.00665e-040.46130.03840.1961
590.0076-0.0033e-040.09910.00830.0909
600.0084e-0400.00131e-040.0105
610.0085-0.0043e-040.16780.0140.1182

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
50 & 0.0024 & 0.0039 & 3e-04 & 0.1527 & 0.0127 & 0.1128 \tabularnewline
51 & 0.0035 & 9e-04 & 1e-04 & 0.0093 & 8e-04 & 0.0278 \tabularnewline
52 & 0.0042 & 0.0024 & 2e-04 & 0.0602 & 0.005 & 0.0708 \tabularnewline
53 & 0.0048 & 0.0031 & 3e-04 & 0.0984 & 0.0082 & 0.0905 \tabularnewline
54 & 0.0054 & 0.0035 & 3e-04 & 0.1308 & 0.0109 & 0.1044 \tabularnewline
55 & 0.006 & 0.0028 & 2e-04 & 0.0794 & 0.0066 & 0.0813 \tabularnewline
56 & 0.0064 & 0.0035 & 3e-04 & 0.1322 & 0.011 & 0.1049 \tabularnewline
57 & 0.0068 & -6e-04 & 1e-04 & 0.0038 & 3e-04 & 0.0179 \tabularnewline
58 & 0.0072 & -0.0066 & 5e-04 & 0.4613 & 0.0384 & 0.1961 \tabularnewline
59 & 0.0076 & -0.003 & 3e-04 & 0.0991 & 0.0083 & 0.0909 \tabularnewline
60 & 0.008 & 4e-04 & 0 & 0.0013 & 1e-04 & 0.0105 \tabularnewline
61 & 0.0085 & -0.004 & 3e-04 & 0.1678 & 0.014 & 0.1182 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33109&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]50[/C][C]0.0024[/C][C]0.0039[/C][C]3e-04[/C][C]0.1527[/C][C]0.0127[/C][C]0.1128[/C][/ROW]
[ROW][C]51[/C][C]0.0035[/C][C]9e-04[/C][C]1e-04[/C][C]0.0093[/C][C]8e-04[/C][C]0.0278[/C][/ROW]
[ROW][C]52[/C][C]0.0042[/C][C]0.0024[/C][C]2e-04[/C][C]0.0602[/C][C]0.005[/C][C]0.0708[/C][/ROW]
[ROW][C]53[/C][C]0.0048[/C][C]0.0031[/C][C]3e-04[/C][C]0.0984[/C][C]0.0082[/C][C]0.0905[/C][/ROW]
[ROW][C]54[/C][C]0.0054[/C][C]0.0035[/C][C]3e-04[/C][C]0.1308[/C][C]0.0109[/C][C]0.1044[/C][/ROW]
[ROW][C]55[/C][C]0.006[/C][C]0.0028[/C][C]2e-04[/C][C]0.0794[/C][C]0.0066[/C][C]0.0813[/C][/ROW]
[ROW][C]56[/C][C]0.0064[/C][C]0.0035[/C][C]3e-04[/C][C]0.1322[/C][C]0.011[/C][C]0.1049[/C][/ROW]
[ROW][C]57[/C][C]0.0068[/C][C]-6e-04[/C][C]1e-04[/C][C]0.0038[/C][C]3e-04[/C][C]0.0179[/C][/ROW]
[ROW][C]58[/C][C]0.0072[/C][C]-0.0066[/C][C]5e-04[/C][C]0.4613[/C][C]0.0384[/C][C]0.1961[/C][/ROW]
[ROW][C]59[/C][C]0.0076[/C][C]-0.003[/C][C]3e-04[/C][C]0.0991[/C][C]0.0083[/C][C]0.0909[/C][/ROW]
[ROW][C]60[/C][C]0.008[/C][C]4e-04[/C][C]0[/C][C]0.0013[/C][C]1e-04[/C][C]0.0105[/C][/ROW]
[ROW][C]61[/C][C]0.0085[/C][C]-0.004[/C][C]3e-04[/C][C]0.1678[/C][C]0.014[/C][C]0.1182[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33109&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33109&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
500.00240.00393e-040.15270.01270.1128
510.00359e-041e-040.00938e-040.0278
520.00420.00242e-040.06020.0050.0708
530.00480.00313e-040.09840.00820.0905
540.00540.00353e-040.13080.01090.1044
550.0060.00282e-040.07940.00660.0813
560.00640.00353e-040.13220.0110.1049
570.0068-6e-041e-040.00383e-040.0179
580.0072-0.00665e-040.46130.03840.1961
590.0076-0.0033e-040.09910.00830.0909
600.0084e-0400.00131e-040.0105
610.0085-0.0043e-040.16780.0140.1182


Figures (Output of Computation)

Input Parameters & R Code

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