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R Software Module

rwasp_arimaforecasting.wasp

Title produced by software

ARIMA Forecasting

Date of computation

Fri, 18 Dec 2009 04:38:59 -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/18/t1261136392pjx3gqiyojmsgwh.htm/, Retrieved Sat, 27 Apr 2024 17:51:34 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=69250, Retrieved Sat, 27 Apr 2024 17:51:34 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

200

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

-     [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-12 13:32:37] [76963dc1903f0f612b6153510a3818cf]
- R  D  [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-17 12:14:40] [76963dc1903f0f612b6153510a3818cf]
-         [Univariate Explorative Data Analysis] [Run Sequence Plot...] [2008-12-22 18:19:51] [1ce0d16c8f4225c977b42c8fa93bc163]
- RMP       [Univariate Data Series] [Identifying Integ...] [2009-11-22 12:08:06] [b98453cac15ba1066b407e146608df68]
-   PD        [Univariate Data Series] [Totaal Werkzoeken...] [2009-11-24 16:54:07] [ee7c2e7343f5b1451e62c5c16ec521f1]
-   P           [Univariate Data Series] [Totaal Werkzoeken...] [2009-11-24 17:23:40] [ee7c2e7343f5b1451e62c5c16ec521f1]
- RMPD            [(Partial) Autocorrelation Function] [] [2009-11-26 08:57:08] [5edbdb7a459c4059b6c3b063ba86821c]
- RMP               [Spectral Analysis] [] [2009-11-26 10:31:07] [5edbdb7a459c4059b6c3b063ba86821c]
-                     [Spectral Analysis] [] [2009-11-26 10:38:09] [5edbdb7a459c4059b6c3b063ba86821c]
-   P                   [Spectral Analysis] [] [2009-12-18 10:36:19] [5edbdb7a459c4059b6c3b063ba86821c]
- RMP                       [ARIMA Forecasting] [] [2009-12-18 11:38:59] [24029b2c7217429de6ff94b5379eb52c] [Current]

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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	1 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=69250&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=69250&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69250&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	1 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[48])
47	14	-	-	-	-	-	-	-
48	16	-	-	-	-	-	-	-
49	8	16	9.8749	22.1251	0.0052	0.5	0.5	0.5
50	3	16	7.3378	24.6622	0.0016	0.9649	0.9649	0.5
51	0	16	5.391	26.609	0.0016	0.9918	0.9918	0.5
52	5	16	3.7498	28.2502	0.0392	0.9948	0.9948	0.5
53	1	16	2.3039	29.6961	0.0159	0.9423	0.9423	0.5
54	1	16	0.9966	31.0034	0.025	0.975	0.975	0.5
55	3	16	-0.2055	32.2055	0.0579	0.9652	0.9652	0.5
56	6	16	-1.3244	33.3244	0.129	0.9293	0.9293	0.5
57	7	16	-2.3753	34.3753	0.1685	0.8569	0.8569	0.5
58	8	16	-3.3693	35.3693	0.2091	0.8188	0.8188	0.5
59	14	16	-4.3147	36.3147	0.4235	0.7799	0.7799	0.5
60	14	16	-5.218	37.218	0.4267	0.5733	0.5733	0.5
61	13	16	-6.0844	38.0844	0.395	0.5704	0.5704	0.5

\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[48]) \tabularnewline
47 & 14 & - & - & - & - & - & - & - \tabularnewline
48 & 16 & - & - & - & - & - & - & - \tabularnewline
49 & 8 & 16 & 9.8749 & 22.1251 & 0.0052 & 0.5 & 0.5 & 0.5 \tabularnewline
50 & 3 & 16 & 7.3378 & 24.6622 & 0.0016 & 0.9649 & 0.9649 & 0.5 \tabularnewline
51 & 0 & 16 & 5.391 & 26.609 & 0.0016 & 0.9918 & 0.9918 & 0.5 \tabularnewline
52 & 5 & 16 & 3.7498 & 28.2502 & 0.0392 & 0.9948 & 0.9948 & 0.5 \tabularnewline
53 & 1 & 16 & 2.3039 & 29.6961 & 0.0159 & 0.9423 & 0.9423 & 0.5 \tabularnewline
54 & 1 & 16 & 0.9966 & 31.0034 & 0.025 & 0.975 & 0.975 & 0.5 \tabularnewline
55 & 3 & 16 & -0.2055 & 32.2055 & 0.0579 & 0.9652 & 0.9652 & 0.5 \tabularnewline
56 & 6 & 16 & -1.3244 & 33.3244 & 0.129 & 0.9293 & 0.9293 & 0.5 \tabularnewline
57 & 7 & 16 & -2.3753 & 34.3753 & 0.1685 & 0.8569 & 0.8569 & 0.5 \tabularnewline
58 & 8 & 16 & -3.3693 & 35.3693 & 0.2091 & 0.8188 & 0.8188 & 0.5 \tabularnewline
59 & 14 & 16 & -4.3147 & 36.3147 & 0.4235 & 0.7799 & 0.7799 & 0.5 \tabularnewline
60 & 14 & 16 & -5.218 & 37.218 & 0.4267 & 0.5733 & 0.5733 & 0.5 \tabularnewline
61 & 13 & 16 & -6.0844 & 38.0844 & 0.395 & 0.5704 & 0.5704 & 0.5 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69250&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[48])[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]48[/C][C]16[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]49[/C][C]8[/C][C]16[/C][C]9.8749[/C][C]22.1251[/C][C]0.0052[/C][C]0.5[/C][C]0.5[/C][C]0.5[/C][/ROW]
[ROW][C]50[/C][C]3[/C][C]16[/C][C]7.3378[/C][C]24.6622[/C][C]0.0016[/C][C]0.9649[/C][C]0.9649[/C][C]0.5[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]16[/C][C]5.391[/C][C]26.609[/C][C]0.0016[/C][C]0.9918[/C][C]0.9918[/C][C]0.5[/C][/ROW]
[ROW][C]52[/C][C]5[/C][C]16[/C][C]3.7498[/C][C]28.2502[/C][C]0.0392[/C][C]0.9948[/C][C]0.9948[/C][C]0.5[/C][/ROW]
[ROW][C]53[/C][C]1[/C][C]16[/C][C]2.3039[/C][C]29.6961[/C][C]0.0159[/C][C]0.9423[/C][C]0.9423[/C][C]0.5[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]16[/C][C]0.9966[/C][C]31.0034[/C][C]0.025[/C][C]0.975[/C][C]0.975[/C][C]0.5[/C][/ROW]
[ROW][C]55[/C][C]3[/C][C]16[/C][C]-0.2055[/C][C]32.2055[/C][C]0.0579[/C][C]0.9652[/C][C]0.9652[/C][C]0.5[/C][/ROW]
[ROW][C]56[/C][C]6[/C][C]16[/C][C]-1.3244[/C][C]33.3244[/C][C]0.129[/C][C]0.9293[/C][C]0.9293[/C][C]0.5[/C][/ROW]
[ROW][C]57[/C][C]7[/C][C]16[/C][C]-2.3753[/C][C]34.3753[/C][C]0.1685[/C][C]0.8569[/C][C]0.8569[/C][C]0.5[/C][/ROW]
[ROW][C]58[/C][C]8[/C][C]16[/C][C]-3.3693[/C][C]35.3693[/C][C]0.2091[/C][C]0.8188[/C][C]0.8188[/C][C]0.5[/C][/ROW]
[ROW][C]59[/C][C]14[/C][C]16[/C][C]-4.3147[/C][C]36.3147[/C][C]0.4235[/C][C]0.7799[/C][C]0.7799[/C][C]0.5[/C][/ROW]
[ROW][C]60[/C][C]14[/C][C]16[/C][C]-5.218[/C][C]37.218[/C][C]0.4267[/C][C]0.5733[/C][C]0.5733[/C][C]0.5[/C][/ROW]
[ROW][C]61[/C][C]13[/C][C]16[/C][C]-6.0844[/C][C]38.0844[/C][C]0.395[/C][C]0.5704[/C][C]0.5704[/C][C]0.5[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69250&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69250&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[48])
47	14	-	-	-	-	-	-	-
48	16	-	-	-	-	-	-	-
49	8	16	9.8749	22.1251	0.0052	0.5	0.5	0.5
50	3	16	7.3378	24.6622	0.0016	0.9649	0.9649	0.5
51	0	16	5.391	26.609	0.0016	0.9918	0.9918	0.5
52	5	16	3.7498	28.2502	0.0392	0.9948	0.9948	0.5
53	1	16	2.3039	29.6961	0.0159	0.9423	0.9423	0.5
54	1	16	0.9966	31.0034	0.025	0.975	0.975	0.5
55	3	16	-0.2055	32.2055	0.0579	0.9652	0.9652	0.5
56	6	16	-1.3244	33.3244	0.129	0.9293	0.9293	0.5
57	7	16	-2.3753	34.3753	0.1685	0.8569	0.8569	0.5
58	8	16	-3.3693	35.3693	0.2091	0.8188	0.8188	0.5
59	14	16	-4.3147	36.3147	0.4235	0.7799	0.7799	0.5
60	14	16	-5.218	37.218	0.4267	0.5733	0.5733	0.5
61	13	16	-6.0844	38.0844	0.395	0.5704	0.5704	0.5

Univariate ARIMA Extrapolation Forecast Performance
time	% S.E.	PE	MAPE	Sq.E	MSE	RMSE
49	0.1953	-0.5	0	64	0	0
50	0.2762	-0.8125	0.6562	169	116.5	10.7935
51	0.3383	-1	0.7708	256	163	12.7671
52	0.3906	-0.6875	0.75	121	152.5	12.3491
53	0.4367	-0.9375	0.7875	225	167	12.9228
54	0.4784	-0.9375	0.8125	225	176.6667	13.2916
55	0.5168	-0.8125	0.8125	169	175.5714	13.2503
56	0.5524	-0.625	0.7891	100	166.125	12.8889
57	0.5859	-0.5625	0.7639	81	156.6667	12.5167
58	0.6176	-0.5	0.7375	64	147.4	12.1408
59	0.6478	-0.125	0.6818	4	134.3636	11.5915
60	0.6766	-0.125	0.6354	4	123.5	11.1131
61	0.7042	-0.1875	0.601	9	114.6923	10.7094

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
49 & 0.1953 & -0.5 & 0 & 64 & 0 & 0 \tabularnewline
50 & 0.2762 & -0.8125 & 0.6562 & 169 & 116.5 & 10.7935 \tabularnewline
51 & 0.3383 & -1 & 0.7708 & 256 & 163 & 12.7671 \tabularnewline
52 & 0.3906 & -0.6875 & 0.75 & 121 & 152.5 & 12.3491 \tabularnewline
53 & 0.4367 & -0.9375 & 0.7875 & 225 & 167 & 12.9228 \tabularnewline
54 & 0.4784 & -0.9375 & 0.8125 & 225 & 176.6667 & 13.2916 \tabularnewline
55 & 0.5168 & -0.8125 & 0.8125 & 169 & 175.5714 & 13.2503 \tabularnewline
56 & 0.5524 & -0.625 & 0.7891 & 100 & 166.125 & 12.8889 \tabularnewline
57 & 0.5859 & -0.5625 & 0.7639 & 81 & 156.6667 & 12.5167 \tabularnewline
58 & 0.6176 & -0.5 & 0.7375 & 64 & 147.4 & 12.1408 \tabularnewline
59 & 0.6478 & -0.125 & 0.6818 & 4 & 134.3636 & 11.5915 \tabularnewline
60 & 0.6766 & -0.125 & 0.6354 & 4 & 123.5 & 11.1131 \tabularnewline
61 & 0.7042 & -0.1875 & 0.601 & 9 & 114.6923 & 10.7094 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69250&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]49[/C][C]0.1953[/C][C]-0.5[/C][C]0[/C][C]64[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]50[/C][C]0.2762[/C][C]-0.8125[/C][C]0.6562[/C][C]169[/C][C]116.5[/C][C]10.7935[/C][/ROW]
[ROW][C]51[/C][C]0.3383[/C][C]-1[/C][C]0.7708[/C][C]256[/C][C]163[/C][C]12.7671[/C][/ROW]
[ROW][C]52[/C][C]0.3906[/C][C]-0.6875[/C][C]0.75[/C][C]121[/C][C]152.5[/C][C]12.3491[/C][/ROW]
[ROW][C]53[/C][C]0.4367[/C][C]-0.9375[/C][C]0.7875[/C][C]225[/C][C]167[/C][C]12.9228[/C][/ROW]
[ROW][C]54[/C][C]0.4784[/C][C]-0.9375[/C][C]0.8125[/C][C]225[/C][C]176.6667[/C][C]13.2916[/C][/ROW]
[ROW][C]55[/C][C]0.5168[/C][C]-0.8125[/C][C]0.8125[/C][C]169[/C][C]175.5714[/C][C]13.2503[/C][/ROW]
[ROW][C]56[/C][C]0.5524[/C][C]-0.625[/C][C]0.7891[/C][C]100[/C][C]166.125[/C][C]12.8889[/C][/ROW]
[ROW][C]57[/C][C]0.5859[/C][C]-0.5625[/C][C]0.7639[/C][C]81[/C][C]156.6667[/C][C]12.5167[/C][/ROW]
[ROW][C]58[/C][C]0.6176[/C][C]-0.5[/C][C]0.7375[/C][C]64[/C][C]147.4[/C][C]12.1408[/C][/ROW]
[ROW][C]59[/C][C]0.6478[/C][C]-0.125[/C][C]0.6818[/C][C]4[/C][C]134.3636[/C][C]11.5915[/C][/ROW]
[ROW][C]60[/C][C]0.6766[/C][C]-0.125[/C][C]0.6354[/C][C]4[/C][C]123.5[/C][C]11.1131[/C][/ROW]
[ROW][C]61[/C][C]0.7042[/C][C]-0.1875[/C][C]0.601[/C][C]9[/C][C]114.6923[/C][C]10.7094[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69250&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69250&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
49	0.1953	-0.5	0	64	0	0
50	0.2762	-0.8125	0.6562	169	116.5	10.7935
51	0.3383	-1	0.7708	256	163	12.7671
52	0.3906	-0.6875	0.75	121	152.5	12.3491
53	0.4367	-0.9375	0.7875	225	167	12.9228
54	0.4784	-0.9375	0.8125	225	176.6667	13.2916
55	0.5168	-0.8125	0.8125	169	175.5714	13.2503
56	0.5524	-0.625	0.7891	100	166.125	12.8889
57	0.5859	-0.5625	0.7639	81	156.6667	12.5167
58	0.6176	-0.5	0.7375	64	147.4	12.1408
59	0.6478	-0.125	0.6818	4	134.3636	11.5915
60	0.6766	-0.125	0.6354	4	123.5	11.1131
61	0.7042	-0.1875	0.601	9	114.6923	10.7094

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 13 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 1 ; par6 = 0 ; par7 = 0 ; par8 = 0 ; par9 = 0 ; par10 = FALSE ;

Parameters (R input):

par1 = 13 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 1 ; par6 = 0 ; par7 = 0 ; 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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code