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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 computationSun, 19 Dec 2010 13:22:59 +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/19/t1292764838s9v0nffjjwi1ixv.htm/, Retrieved Sat, 04 May 2024 20:15:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112361, Retrieved Sat, 04 May 2024 20:15:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact148

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] [WS9 - ARIMA Forec...] [2010-12-04 16:25:58] [8ef49741e164ec6343c90c7935194465]
-   P         [ARIMA Forecasting] [WS9 - ARIMA Forec...] [2010-12-04 16:58:46] [8ef49741e164ec6343c90c7935194465]
-   PD          [ARIMA Forecasting] [ARIMA Forecasting...] [2010-12-14 11:24:37] [8ef49741e164ec6343c90c7935194465]
-   P               [ARIMA Forecasting] [ARIMA Forecasting...] [2010-12-19 13:22:59] [934c3727858e074bf543f25f5906ed72] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
104.37
104.89
105.15
105.72
106.38
106.40
106.47
106.59
106.76
107.35
107.81
108.03
109.08
109.86
110.29
110.34
110.59
110.64
110.83
111.51
113.32
115.89
116.51
117.44
118.25
118.65
118.52
119.07
119.12
119.28
119.30
119.44
119.57
119.93
120.03
119.66
119.46
119.48
119.56
119.43
119.57
119.59
119.50
119.54
119.56
119.61
119.64
119.60
119.71
119.72
119.66
119.76
119.80
119.88
119.78
120.08
120.22

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=112361&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=112361&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112361&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[45])
33119.57-------
34119.93-------
35120.03-------
36119.66-------
37119.46-------
38119.48-------
39119.56-------
40119.43-------
41119.57-------
42119.59-------
43119.5-------
44119.54-------
45119.56-------
46119.61119.6711118.8261120.51610.44370.60170.27410.6017
47119.64119.8349118.2212121.44860.40640.60760.40630.6308
48119.6120.0293117.6696122.38910.36070.62680.62050.6517
49119.71120.2414117.172123.31080.36720.65890.69110.6683
50119.72120.4638116.7206124.2070.34850.65350.69680.682
51119.66120.6921116.3057125.07840.32230.6680.69350.6935
52119.76120.9238115.919125.92860.32430.68970.72070.7034
53119.8121.1575115.5535126.76160.31750.68750.71060.7118
54119.88121.3924115.2037127.58110.3160.6930.71590.7192
55119.78121.628114.8655128.39040.29610.69380.73130.7255
56120.08121.8639114.5356129.19230.31660.71140.73290.7311
57120.22122.1001114.2113129.98890.32020.69210.7360.736

\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[45]) \tabularnewline
33 & 119.57 & - & - & - & - & - & - & - \tabularnewline
34 & 119.93 & - & - & - & - & - & - & - \tabularnewline
35 & 120.03 & - & - & - & - & - & - & - \tabularnewline
36 & 119.66 & - & - & - & - & - & - & - \tabularnewline
37 & 119.46 & - & - & - & - & - & - & - \tabularnewline
38 & 119.48 & - & - & - & - & - & - & - \tabularnewline
39 & 119.56 & - & - & - & - & - & - & - \tabularnewline
40 & 119.43 & - & - & - & - & - & - & - \tabularnewline
41 & 119.57 & - & - & - & - & - & - & - \tabularnewline
42 & 119.59 & - & - & - & - & - & - & - \tabularnewline
43 & 119.5 & - & - & - & - & - & - & - \tabularnewline
44 & 119.54 & - & - & - & - & - & - & - \tabularnewline
45 & 119.56 & - & - & - & - & - & - & - \tabularnewline
46 & 119.61 & 119.6711 & 118.8261 & 120.5161 & 0.4437 & 0.6017 & 0.2741 & 0.6017 \tabularnewline
47 & 119.64 & 119.8349 & 118.2212 & 121.4486 & 0.4064 & 0.6076 & 0.4063 & 0.6308 \tabularnewline
48 & 119.6 & 120.0293 & 117.6696 & 122.3891 & 0.3607 & 0.6268 & 0.6205 & 0.6517 \tabularnewline
49 & 119.71 & 120.2414 & 117.172 & 123.3108 & 0.3672 & 0.6589 & 0.6911 & 0.6683 \tabularnewline
50 & 119.72 & 120.4638 & 116.7206 & 124.207 & 0.3485 & 0.6535 & 0.6968 & 0.682 \tabularnewline
51 & 119.66 & 120.6921 & 116.3057 & 125.0784 & 0.3223 & 0.668 & 0.6935 & 0.6935 \tabularnewline
52 & 119.76 & 120.9238 & 115.919 & 125.9286 & 0.3243 & 0.6897 & 0.7207 & 0.7034 \tabularnewline
53 & 119.8 & 121.1575 & 115.5535 & 126.7616 & 0.3175 & 0.6875 & 0.7106 & 0.7118 \tabularnewline
54 & 119.88 & 121.3924 & 115.2037 & 127.5811 & 0.316 & 0.693 & 0.7159 & 0.7192 \tabularnewline
55 & 119.78 & 121.628 & 114.8655 & 128.3904 & 0.2961 & 0.6938 & 0.7313 & 0.7255 \tabularnewline
56 & 120.08 & 121.8639 & 114.5356 & 129.1923 & 0.3166 & 0.7114 & 0.7329 & 0.7311 \tabularnewline
57 & 120.22 & 122.1001 & 114.2113 & 129.9889 & 0.3202 & 0.6921 & 0.736 & 0.736 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112361&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[45])[/C][/ROW]
[ROW][C]33[/C][C]119.57[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]34[/C][C]119.93[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]35[/C][C]120.03[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]36[/C][C]119.66[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]37[/C][C]119.46[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]38[/C][C]119.48[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]39[/C][C]119.56[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]40[/C][C]119.43[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]41[/C][C]119.57[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]42[/C][C]119.59[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]43[/C][C]119.5[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]44[/C][C]119.54[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]45[/C][C]119.56[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]46[/C][C]119.61[/C][C]119.6711[/C][C]118.8261[/C][C]120.5161[/C][C]0.4437[/C][C]0.6017[/C][C]0.2741[/C][C]0.6017[/C][/ROW]
[ROW][C]47[/C][C]119.64[/C][C]119.8349[/C][C]118.2212[/C][C]121.4486[/C][C]0.4064[/C][C]0.6076[/C][C]0.4063[/C][C]0.6308[/C][/ROW]
[ROW][C]48[/C][C]119.6[/C][C]120.0293[/C][C]117.6696[/C][C]122.3891[/C][C]0.3607[/C][C]0.6268[/C][C]0.6205[/C][C]0.6517[/C][/ROW]
[ROW][C]49[/C][C]119.71[/C][C]120.2414[/C][C]117.172[/C][C]123.3108[/C][C]0.3672[/C][C]0.6589[/C][C]0.6911[/C][C]0.6683[/C][/ROW]
[ROW][C]50[/C][C]119.72[/C][C]120.4638[/C][C]116.7206[/C][C]124.207[/C][C]0.3485[/C][C]0.6535[/C][C]0.6968[/C][C]0.682[/C][/ROW]
[ROW][C]51[/C][C]119.66[/C][C]120.6921[/C][C]116.3057[/C][C]125.0784[/C][C]0.3223[/C][C]0.668[/C][C]0.6935[/C][C]0.6935[/C][/ROW]
[ROW][C]52[/C][C]119.76[/C][C]120.9238[/C][C]115.919[/C][C]125.9286[/C][C]0.3243[/C][C]0.6897[/C][C]0.7207[/C][C]0.7034[/C][/ROW]
[ROW][C]53[/C][C]119.8[/C][C]121.1575[/C][C]115.5535[/C][C]126.7616[/C][C]0.3175[/C][C]0.6875[/C][C]0.7106[/C][C]0.7118[/C][/ROW]
[ROW][C]54[/C][C]119.88[/C][C]121.3924[/C][C]115.2037[/C][C]127.5811[/C][C]0.316[/C][C]0.693[/C][C]0.7159[/C][C]0.7192[/C][/ROW]
[ROW][C]55[/C][C]119.78[/C][C]121.628[/C][C]114.8655[/C][C]128.3904[/C][C]0.2961[/C][C]0.6938[/C][C]0.7313[/C][C]0.7255[/C][/ROW]
[ROW][C]56[/C][C]120.08[/C][C]121.8639[/C][C]114.5356[/C][C]129.1923[/C][C]0.3166[/C][C]0.7114[/C][C]0.7329[/C][C]0.7311[/C][/ROW]
[ROW][C]57[/C][C]120.22[/C][C]122.1001[/C][C]114.2113[/C][C]129.9889[/C][C]0.3202[/C][C]0.6921[/C][C]0.736[/C][C]0.736[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112361&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112361&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[45])
33119.57-------
34119.93-------
35120.03-------
36119.66-------
37119.46-------
38119.48-------
39119.56-------
40119.43-------
41119.57-------
42119.59-------
43119.5-------
44119.54-------
45119.56-------
46119.61119.6711118.8261120.51610.44370.60170.27410.6017
47119.64119.8349118.2212121.44860.40640.60760.40630.6308
48119.6120.0293117.6696122.38910.36070.62680.62050.6517
49119.71120.2414117.172123.31080.36720.65890.69110.6683
50119.72120.4638116.7206124.2070.34850.65350.69680.682
51119.66120.6921116.3057125.07840.32230.6680.69350.6935
52119.76120.9238115.919125.92860.32430.68970.72070.7034
53119.8121.1575115.5535126.76160.31750.68750.71060.7118
54119.88121.3924115.2037127.58110.3160.6930.71590.7192
55119.78121.628114.8655128.39040.29610.69380.73130.7255
56120.08121.8639114.5356129.19230.31660.71140.73290.7311
57120.22122.1001114.2113129.98890.32020.69210.7360.736






Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
460.0036-5e-0400.003700
470.0069-0.00160.00110.0380.02090.1444
480.01-0.00360.00190.18430.07530.2745
490.013-0.00440.00250.28240.12710.3565
500.0159-0.00620.00330.55320.21230.4608
510.0185-0.00860.00411.06520.35450.5954
520.0211-0.00960.00491.35450.49730.7052
530.0236-0.01120.00571.84290.66550.8158
540.026-0.01250.00652.28750.84570.9196
550.0284-0.01520.00733.41511.10271.0501
560.0307-0.01460.0083.18241.29171.1365
570.033-0.01540.00863.53481.47871.216

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
46 & 0.0036 & -5e-04 & 0 & 0.0037 & 0 & 0 \tabularnewline
47 & 0.0069 & -0.0016 & 0.0011 & 0.038 & 0.0209 & 0.1444 \tabularnewline
48 & 0.01 & -0.0036 & 0.0019 & 0.1843 & 0.0753 & 0.2745 \tabularnewline
49 & 0.013 & -0.0044 & 0.0025 & 0.2824 & 0.1271 & 0.3565 \tabularnewline
50 & 0.0159 & -0.0062 & 0.0033 & 0.5532 & 0.2123 & 0.4608 \tabularnewline
51 & 0.0185 & -0.0086 & 0.0041 & 1.0652 & 0.3545 & 0.5954 \tabularnewline
52 & 0.0211 & -0.0096 & 0.0049 & 1.3545 & 0.4973 & 0.7052 \tabularnewline
53 & 0.0236 & -0.0112 & 0.0057 & 1.8429 & 0.6655 & 0.8158 \tabularnewline
54 & 0.026 & -0.0125 & 0.0065 & 2.2875 & 0.8457 & 0.9196 \tabularnewline
55 & 0.0284 & -0.0152 & 0.0073 & 3.4151 & 1.1027 & 1.0501 \tabularnewline
56 & 0.0307 & -0.0146 & 0.008 & 3.1824 & 1.2917 & 1.1365 \tabularnewline
57 & 0.033 & -0.0154 & 0.0086 & 3.5348 & 1.4787 & 1.216 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112361&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]46[/C][C]0.0036[/C][C]-5e-04[/C][C]0[/C][C]0.0037[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]47[/C][C]0.0069[/C][C]-0.0016[/C][C]0.0011[/C][C]0.038[/C][C]0.0209[/C][C]0.1444[/C][/ROW]
[ROW][C]48[/C][C]0.01[/C][C]-0.0036[/C][C]0.0019[/C][C]0.1843[/C][C]0.0753[/C][C]0.2745[/C][/ROW]
[ROW][C]49[/C][C]0.013[/C][C]-0.0044[/C][C]0.0025[/C][C]0.2824[/C][C]0.1271[/C][C]0.3565[/C][/ROW]
[ROW][C]50[/C][C]0.0159[/C][C]-0.0062[/C][C]0.0033[/C][C]0.5532[/C][C]0.2123[/C][C]0.4608[/C][/ROW]
[ROW][C]51[/C][C]0.0185[/C][C]-0.0086[/C][C]0.0041[/C][C]1.0652[/C][C]0.3545[/C][C]0.5954[/C][/ROW]
[ROW][C]52[/C][C]0.0211[/C][C]-0.0096[/C][C]0.0049[/C][C]1.3545[/C][C]0.4973[/C][C]0.7052[/C][/ROW]
[ROW][C]53[/C][C]0.0236[/C][C]-0.0112[/C][C]0.0057[/C][C]1.8429[/C][C]0.6655[/C][C]0.8158[/C][/ROW]
[ROW][C]54[/C][C]0.026[/C][C]-0.0125[/C][C]0.0065[/C][C]2.2875[/C][C]0.8457[/C][C]0.9196[/C][/ROW]
[ROW][C]55[/C][C]0.0284[/C][C]-0.0152[/C][C]0.0073[/C][C]3.4151[/C][C]1.1027[/C][C]1.0501[/C][/ROW]
[ROW][C]56[/C][C]0.0307[/C][C]-0.0146[/C][C]0.008[/C][C]3.1824[/C][C]1.2917[/C][C]1.1365[/C][/ROW]
[ROW][C]57[/C][C]0.033[/C][C]-0.0154[/C][C]0.0086[/C][C]3.5348[/C][C]1.4787[/C][C]1.216[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112361&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112361&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
460.0036-5e-0400.003700
470.0069-0.00160.00110.0380.02090.1444
480.01-0.00360.00190.18430.07530.2745
490.013-0.00440.00250.28240.12710.3565
500.0159-0.00620.00330.55320.21230.4608
510.0185-0.00860.00411.06520.35450.5954
520.0211-0.00960.00491.35450.49730.7052
530.0236-0.01120.00571.84290.66550.8158
540.026-0.01250.00652.28750.84570.9196
550.0284-0.01520.00733.41511.10271.0501
560.0307-0.01460.0083.18241.29171.1365
570.033-0.01540.00863.53481.47871.216


Figures (Output of Computation)

Input Parameters & R Code

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