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Author

*Unverified author*

R Software Module

rwasp_arimaforecasting.wasp

Title produced by software

ARIMA Forecasting

Date of computation

Fri, 04 Dec 2009 07:46:05 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/04/t1259938116mf0m093jbofczpn.htm/, Retrieved Sun, 28 Apr 2024 11:08:58 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63659, Retrieved Sun, 28 Apr 2024 11:08:58 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

150

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [ARIMA Forecasting] [estimation of ARM...] [2007-12-06 10:08:23] [dc28704e2f48edede7e5c93fa6811a5e]
- RMPD    [ARIMA Forecasting] [Workshop 10 - ARI...] [2009-12-04 14:46:05] [e3e44d0dc7798eea8d2bf548abff3df8] [Current]
-   P       [ARIMA Forecasting] [Workshop 10 - JUI...] [2009-12-06 21:21:45] [74be16979710d4c4e7c6647856088456] 
-             [ARIMA Forecasting] [] [2009-12-17 13:53:52] [68cb6e9d2b1cb3475e83bcdfaf88b501] 
-   P         [ARIMA Forecasting] [Verbeterde foreca...] [2009-12-18 17:14:04] [9717cb857c153ca3061376906953b329] 
- R P         [ARIMA Forecasting] [FINALE PAPER - Ar...] [2009-12-28 21:52:59] [74be16979710d4c4e7c6647856088456] 

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Dataseries X:

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 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=63659&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=63659&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63659&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Univariate ARIMA Extrapolation Forecast
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])
60	248333	-	-	-	-	-	-	-
61	246969	-	-	-	-	-	-	-
62	245098	-	-	-	-	-	-	-
63	246263	-	-	-	-	-	-	-
64	255765	-	-	-	-	-	-	-
65	264319	-	-	-	-	-	-	-
66	268347	-	-	-	-	-	-	-
67	273046	-	-	-	-	-	-	-
68	273963	-	-	-	-	-	-	-
69	267430	-	-	-	-	-	-	-
70	271993	-	-	-	-	-	-	-
71	292710	-	-	-	-	-	-	-
72	295881	-	-	-	-	-	-	-
73	NA	296198.3174	288626.1757	303770.459	NA	0.5327	1	0.5327
74	NA	292149.3308	280991.0134	303307.6482	NA	NA	1	0.2561
75	NA	289481.4949	274450.1082	304512.8815	NA	NA	1	0.202
76	NA	295232.8238	275597.785	314867.8627	NA	NA	1	0.4742
77	NA	300200.6239	276381.6298	324019.618	NA	NA	0.9984	0.6389
78	NA	300993.2379	272987.6066	328998.8692	NA	NA	0.9888	0.6397
79	NA	299789.8714	267634.0587	331945.6841	NA	NA	0.9485	0.5942
80	NA	299651.5526	263535.2725	335767.8326	NA	NA	0.9184	0.5811
81	NA	292732.9475	252761.9171	332703.9779	NA	NA	0.8926	0.4387
82	NA	293493.9342	249796.9889	337190.8794	NA	NA	0.8326	0.4574
83	NA	314777.7764	267497.6222	362057.9305	NA	NA	0.8199	0.7833
84	NA	317323.5868	266582.4724	368064.7012	NA	NA	0.7962	0.7962

\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 & 248333 & - & - & - & - & - & - & - \tabularnewline
61 & 246969 & - & - & - & - & - & - & - \tabularnewline
62 & 245098 & - & - & - & - & - & - & - \tabularnewline
63 & 246263 & - & - & - & - & - & - & - \tabularnewline
64 & 255765 & - & - & - & - & - & - & - \tabularnewline
65 & 264319 & - & - & - & - & - & - & - \tabularnewline
66 & 268347 & - & - & - & - & - & - & - \tabularnewline
67 & 273046 & - & - & - & - & - & - & - \tabularnewline
68 & 273963 & - & - & - & - & - & - & - \tabularnewline
69 & 267430 & - & - & - & - & - & - & - \tabularnewline
70 & 271993 & - & - & - & - & - & - & - \tabularnewline
71 & 292710 & - & - & - & - & - & - & - \tabularnewline
72 & 295881 & - & - & - & - & - & - & - \tabularnewline
73 & NA & 296198.3174 & 288626.1757 & 303770.459 & NA & 0.5327 & 1 & 0.5327 \tabularnewline
74 & NA & 292149.3308 & 280991.0134 & 303307.6482 & NA & NA & 1 & 0.2561 \tabularnewline
75 & NA & 289481.4949 & 274450.1082 & 304512.8815 & NA & NA & 1 & 0.202 \tabularnewline
76 & NA & 295232.8238 & 275597.785 & 314867.8627 & NA & NA & 1 & 0.4742 \tabularnewline
77 & NA & 300200.6239 & 276381.6298 & 324019.618 & NA & NA & 0.9984 & 0.6389 \tabularnewline
78 & NA & 300993.2379 & 272987.6066 & 328998.8692 & NA & NA & 0.9888 & 0.6397 \tabularnewline
79 & NA & 299789.8714 & 267634.0587 & 331945.6841 & NA & NA & 0.9485 & 0.5942 \tabularnewline
80 & NA & 299651.5526 & 263535.2725 & 335767.8326 & NA & NA & 0.9184 & 0.5811 \tabularnewline
81 & NA & 292732.9475 & 252761.9171 & 332703.9779 & NA & NA & 0.8926 & 0.4387 \tabularnewline
82 & NA & 293493.9342 & 249796.9889 & 337190.8794 & NA & NA & 0.8326 & 0.4574 \tabularnewline
83 & NA & 314777.7764 & 267497.6222 & 362057.9305 & NA & NA & 0.8199 & 0.7833 \tabularnewline
84 & NA & 317323.5868 & 266582.4724 & 368064.7012 & NA & NA & 0.7962 & 0.7962 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63659&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]248333[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]61[/C][C]246969[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]62[/C][C]245098[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]63[/C][C]246263[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]64[/C][C]255765[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]65[/C][C]264319[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]66[/C][C]268347[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]67[/C][C]273046[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]68[/C][C]273963[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]69[/C][C]267430[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]70[/C][C]271993[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]71[/C][C]292710[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]72[/C][C]295881[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]73[/C][C]NA[/C][C]296198.3174[/C][C]288626.1757[/C][C]303770.459[/C][C]NA[/C][C]0.5327[/C][C]1[/C][C]0.5327[/C][/ROW]
[ROW][C]74[/C][C]NA[/C][C]292149.3308[/C][C]280991.0134[/C][C]303307.6482[/C][C]NA[/C][C]NA[/C][C]1[/C][C]0.2561[/C][/ROW]
[ROW][C]75[/C][C]NA[/C][C]289481.4949[/C][C]274450.1082[/C][C]304512.8815[/C][C]NA[/C][C]NA[/C][C]1[/C][C]0.202[/C][/ROW]
[ROW][C]76[/C][C]NA[/C][C]295232.8238[/C][C]275597.785[/C][C]314867.8627[/C][C]NA[/C][C]NA[/C][C]1[/C][C]0.4742[/C][/ROW]
[ROW][C]77[/C][C]NA[/C][C]300200.6239[/C][C]276381.6298[/C][C]324019.618[/C][C]NA[/C][C]NA[/C][C]0.9984[/C][C]0.6389[/C][/ROW]
[ROW][C]78[/C][C]NA[/C][C]300993.2379[/C][C]272987.6066[/C][C]328998.8692[/C][C]NA[/C][C]NA[/C][C]0.9888[/C][C]0.6397[/C][/ROW]
[ROW][C]79[/C][C]NA[/C][C]299789.8714[/C][C]267634.0587[/C][C]331945.6841[/C][C]NA[/C][C]NA[/C][C]0.9485[/C][C]0.5942[/C][/ROW]
[ROW][C]80[/C][C]NA[/C][C]299651.5526[/C][C]263535.2725[/C][C]335767.8326[/C][C]NA[/C][C]NA[/C][C]0.9184[/C][C]0.5811[/C][/ROW]
[ROW][C]81[/C][C]NA[/C][C]292732.9475[/C][C]252761.9171[/C][C]332703.9779[/C][C]NA[/C][C]NA[/C][C]0.8926[/C][C]0.4387[/C][/ROW]
[ROW][C]82[/C][C]NA[/C][C]293493.9342[/C][C]249796.9889[/C][C]337190.8794[/C][C]NA[/C][C]NA[/C][C]0.8326[/C][C]0.4574[/C][/ROW]
[ROW][C]83[/C][C]NA[/C][C]314777.7764[/C][C]267497.6222[/C][C]362057.9305[/C][C]NA[/C][C]NA[/C][C]0.8199[/C][C]0.7833[/C][/ROW]
[ROW][C]84[/C][C]NA[/C][C]317323.5868[/C][C]266582.4724[/C][C]368064.7012[/C][C]NA[/C][C]NA[/C][C]0.7962[/C][C]0.7962[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63659&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63659&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Univariate ARIMA Extrapolation Forecast
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])
60	248333	-	-	-	-	-	-	-
61	246969	-	-	-	-	-	-	-
62	245098	-	-	-	-	-	-	-
63	246263	-	-	-	-	-	-	-
64	255765	-	-	-	-	-	-	-
65	264319	-	-	-	-	-	-	-
66	268347	-	-	-	-	-	-	-
67	273046	-	-	-	-	-	-	-
68	273963	-	-	-	-	-	-	-
69	267430	-	-	-	-	-	-	-
70	271993	-	-	-	-	-	-	-
71	292710	-	-	-	-	-	-	-
72	295881	-	-	-	-	-	-	-
73	NA	296198.3174	288626.1757	303770.459	NA	0.5327	1	0.5327
74	NA	292149.3308	280991.0134	303307.6482	NA	NA	1	0.2561
75	NA	289481.4949	274450.1082	304512.8815	NA	NA	1	0.202
76	NA	295232.8238	275597.785	314867.8627	NA	NA	1	0.4742
77	NA	300200.6239	276381.6298	324019.618	NA	NA	0.9984	0.6389
78	NA	300993.2379	272987.6066	328998.8692	NA	NA	0.9888	0.6397
79	NA	299789.8714	267634.0587	331945.6841	NA	NA	0.9485	0.5942
80	NA	299651.5526	263535.2725	335767.8326	NA	NA	0.9184	0.5811
81	NA	292732.9475	252761.9171	332703.9779	NA	NA	0.8926	0.4387
82	NA	293493.9342	249796.9889	337190.8794	NA	NA	0.8326	0.4574
83	NA	314777.7764	267497.6222	362057.9305	NA	NA	0.8199	0.7833
84	NA	317323.5868	266582.4724	368064.7012	NA	NA	0.7962	0.7962

Univariate ARIMA Extrapolation Forecast Performance
time	% S.E.	PE	MAPE	Sq.E	MSE	RMSE
73	0.013	NA	NA	NA	NA	NA
74	0.0195	NA	NA	NA	NA	NA
75	0.0265	NA	NA	NA	NA	NA
76	0.0339	NA	NA	NA	NA	NA
77	0.0405	NA	NA	NA	NA	NA
78	0.0475	NA	NA	NA	NA	NA
79	0.0547	NA	NA	NA	NA	NA
80	0.0615	NA	NA	NA	NA	NA
81	0.0697	NA	NA	NA	NA	NA
82	0.076	NA	NA	NA	NA	NA
83	0.0766	NA	NA	NA	NA	NA
84	0.0816	NA	NA	NA	NA	NA

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
73 & 0.013 & NA & NA & NA & NA & NA \tabularnewline
74 & 0.0195 & NA & NA & NA & NA & NA \tabularnewline
75 & 0.0265 & NA & NA & NA & NA & NA \tabularnewline
76 & 0.0339 & NA & NA & NA & NA & NA \tabularnewline
77 & 0.0405 & NA & NA & NA & NA & NA \tabularnewline
78 & 0.0475 & NA & NA & NA & NA & NA \tabularnewline
79 & 0.0547 & NA & NA & NA & NA & NA \tabularnewline
80 & 0.0615 & NA & NA & NA & NA & NA \tabularnewline
81 & 0.0697 & NA & NA & NA & NA & NA \tabularnewline
82 & 0.076 & NA & NA & NA & NA & NA \tabularnewline
83 & 0.0766 & NA & NA & NA & NA & NA \tabularnewline
84 & 0.0816 & NA & NA & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63659&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]0.013[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]74[/C][C]0.0195[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]75[/C][C]0.0265[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]76[/C][C]0.0339[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]77[/C][C]0.0405[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]78[/C][C]0.0475[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]79[/C][C]0.0547[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]80[/C][C]0.0615[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]81[/C][C]0.0697[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]82[/C][C]0.076[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]83[/C][C]0.0766[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]84[/C][C]0.0816[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63659&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63659&T=2

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Univariate ARIMA Extrapolation Forecast Performance
time	% S.E.	PE	MAPE	Sq.E	MSE	RMSE
73	0.013	NA	NA	NA	NA	NA
74	0.0195	NA	NA	NA	NA	NA
75	0.0265	NA	NA	NA	NA	NA
76	0.0339	NA	NA	NA	NA	NA
77	0.0405	NA	NA	NA	NA	NA
78	0.0475	NA	NA	NA	NA	NA
79	0.0547	NA	NA	NA	NA	NA
80	0.0615	NA	NA	NA	NA	NA
81	0.0697	NA	NA	NA	NA	NA
82	0.076	NA	NA	NA	NA	NA
83	0.0766	NA	NA	NA	NA	NA
84	0.0816	NA	NA	NA	NA	NA

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 0 ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 0 ; par8 = 0 ; par9 = 1 ; par10 = FALSE ;

Parameters (R input):

par1 = 0 ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; 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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code