Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_arimaforecasting.wasp

Title produced by software

ARIMA Forecasting

Date of computation

Mon, 05 Dec 2011 11:06:32 -0500

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/2011/Dec/05/t13231012526ax1bqvzbpbnz8v.htm/, Retrieved Fri, 03 May 2024 07:58:17 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=151020, Retrieved Fri, 03 May 2024 07:58:17 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

121

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

-     [Univariate Data Series] [] [2008-12-08 19:22:39] [d2d412c7f4d35ffbf5ee5ee89db327d4]
- RMPD  [(Partial) Autocorrelation Function] [] [2011-12-01 17:46:48] [86f7284edee3dbb8ea5c7e2dec87d892]
- R P     [(Partial) Autocorrelation Function] [] [2011-12-05 14:45:05] [86f7284edee3dbb8ea5c7e2dec87d892]
- RMP         [ARIMA Forecasting] [] [2011-12-05 16:06:32] [79818163420d1233b8d9d93d595e6c9e] [Current]
- R P           [ARIMA Forecasting] [] [2011-12-16 07:39:11] [86f7284edee3dbb8ea5c7e2dec87d892] 
- RMPD            [Multiple Regression] [] [2011-12-23 12:25:40] [ad2d4c5ace9fa07b356a7b5098237581] 
- R                 [Multiple Regression] [] [2011-12-23 12:45:24] [ad2d4c5ace9fa07b356a7b5098237581] 
- RMPD          [Multiple Regression] [] [2011-12-16 11:27:07] [86f7284edee3dbb8ea5c7e2dec87d892] 
- R               [Multiple Regression] [] [2011-12-17 18:08:25] [74be16979710d4c4e7c6647856088456] 

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

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' @ jenkins.wessa.net

\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' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=151020&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' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=151020&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=151020&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' @ jenkins.wessa.net

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])
36	492	-	-	-	-	-	-	-
37	493	-	-	-	-	-	-	-
38	481	-	-	-	-	-	-	-
39	462	-	-	-	-	-	-	-
40	457	-	-	-	-	-	-	-
41	442	-	-	-	-	-	-	-
42	439	-	-	-	-	-	-	-
43	488	-	-	-	-	-	-	-
44	521	-	-	-	-	-	-	-
45	501	-	-	-	-	-	-	-
46	485	-	-	-	-	-	-	-
47	464	-	-	-	-	-	-	-
48	460	-	-	-	-	-	-	-
49	467	461.3537	452.633	470.0744	0.1022	0.6195	0	0.6195
50	460	450.5727	440.0305	461.1149	0.0398	0.0011	0	0.0398
51	448	433.2	421.4555	444.9445	0.0068	0	0	0
52	443	426.3835	413.6287	439.1382	0.0053	4e-04	0	0
53	436	412.0328	398.3619	425.7038	3e-04	0	0	0
54	431	409.2682	394.7437	423.7926	0.0017	2e-04	0	0
55	484	458.0956	442.7663	473.425	5e-04	0.9997	1e-04	0.4038
56	510	489.1908	473.0971	505.2845	0.0056	0.7364	1e-04	0.9998
57	513	470.5724	453.7492	487.3957	0	0	2e-04	0.891
58	503	452.7577	435.2352	470.2803	0	0	2e-04	0.2089
59	471	432.672	414.4767	450.8673	0	0	4e-04	0.0016
60	471	430.1947	411.3497	449.0397	0	0	0.001	0.001

\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
36 & 492 & - & - & - & - & - & - & - \tabularnewline
37 & 493 & - & - & - & - & - & - & - \tabularnewline
38 & 481 & - & - & - & - & - & - & - \tabularnewline
39 & 462 & - & - & - & - & - & - & - \tabularnewline
40 & 457 & - & - & - & - & - & - & - \tabularnewline
41 & 442 & - & - & - & - & - & - & - \tabularnewline
42 & 439 & - & - & - & - & - & - & - \tabularnewline
43 & 488 & - & - & - & - & - & - & - \tabularnewline
44 & 521 & - & - & - & - & - & - & - \tabularnewline
45 & 501 & - & - & - & - & - & - & - \tabularnewline
46 & 485 & - & - & - & - & - & - & - \tabularnewline
47 & 464 & - & - & - & - & - & - & - \tabularnewline
48 & 460 & - & - & - & - & - & - & - \tabularnewline
49 & 467 & 461.3537 & 452.633 & 470.0744 & 0.1022 & 0.6195 & 0 & 0.6195 \tabularnewline
50 & 460 & 450.5727 & 440.0305 & 461.1149 & 0.0398 & 0.0011 & 0 & 0.0398 \tabularnewline
51 & 448 & 433.2 & 421.4555 & 444.9445 & 0.0068 & 0 & 0 & 0 \tabularnewline
52 & 443 & 426.3835 & 413.6287 & 439.1382 & 0.0053 & 4e-04 & 0 & 0 \tabularnewline
53 & 436 & 412.0328 & 398.3619 & 425.7038 & 3e-04 & 0 & 0 & 0 \tabularnewline
54 & 431 & 409.2682 & 394.7437 & 423.7926 & 0.0017 & 2e-04 & 0 & 0 \tabularnewline
55 & 484 & 458.0956 & 442.7663 & 473.425 & 5e-04 & 0.9997 & 1e-04 & 0.4038 \tabularnewline
56 & 510 & 489.1908 & 473.0971 & 505.2845 & 0.0056 & 0.7364 & 1e-04 & 0.9998 \tabularnewline
57 & 513 & 470.5724 & 453.7492 & 487.3957 & 0 & 0 & 2e-04 & 0.891 \tabularnewline
58 & 503 & 452.7577 & 435.2352 & 470.2803 & 0 & 0 & 2e-04 & 0.2089 \tabularnewline
59 & 471 & 432.672 & 414.4767 & 450.8673 & 0 & 0 & 4e-04 & 0.0016 \tabularnewline
60 & 471 & 430.1947 & 411.3497 & 449.0397 & 0 & 0 & 0.001 & 0.001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=151020&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]36[/C][C]492[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]37[/C][C]493[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]38[/C][C]481[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]39[/C][C]462[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]40[/C][C]457[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]41[/C][C]442[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]42[/C][C]439[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]43[/C][C]488[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]44[/C][C]521[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]45[/C][C]501[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]46[/C][C]485[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]47[/C][C]464[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]48[/C][C]460[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]49[/C][C]467[/C][C]461.3537[/C][C]452.633[/C][C]470.0744[/C][C]0.1022[/C][C]0.6195[/C][C]0[/C][C]0.6195[/C][/ROW]
[ROW][C]50[/C][C]460[/C][C]450.5727[/C][C]440.0305[/C][C]461.1149[/C][C]0.0398[/C][C]0.0011[/C][C]0[/C][C]0.0398[/C][/ROW]
[ROW][C]51[/C][C]448[/C][C]433.2[/C][C]421.4555[/C][C]444.9445[/C][C]0.0068[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]52[/C][C]443[/C][C]426.3835[/C][C]413.6287[/C][C]439.1382[/C][C]0.0053[/C][C]4e-04[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]53[/C][C]436[/C][C]412.0328[/C][C]398.3619[/C][C]425.7038[/C][C]3e-04[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]54[/C][C]431[/C][C]409.2682[/C][C]394.7437[/C][C]423.7926[/C][C]0.0017[/C][C]2e-04[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]55[/C][C]484[/C][C]458.0956[/C][C]442.7663[/C][C]473.425[/C][C]5e-04[/C][C]0.9997[/C][C]1e-04[/C][C]0.4038[/C][/ROW]
[ROW][C]56[/C][C]510[/C][C]489.1908[/C][C]473.0971[/C][C]505.2845[/C][C]0.0056[/C][C]0.7364[/C][C]1e-04[/C][C]0.9998[/C][/ROW]
[ROW][C]57[/C][C]513[/C][C]470.5724[/C][C]453.7492[/C][C]487.3957[/C][C]0[/C][C]0[/C][C]2e-04[/C][C]0.891[/C][/ROW]
[ROW][C]58[/C][C]503[/C][C]452.7577[/C][C]435.2352[/C][C]470.2803[/C][C]0[/C][C]0[/C][C]2e-04[/C][C]0.2089[/C][/ROW]
[ROW][C]59[/C][C]471[/C][C]432.672[/C][C]414.4767[/C][C]450.8673[/C][C]0[/C][C]0[/C][C]4e-04[/C][C]0.0016[/C][/ROW]
[ROW][C]60[/C][C]471[/C][C]430.1947[/C][C]411.3497[/C][C]449.0397[/C][C]0[/C][C]0[/C][C]0.001[/C][C]0.001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=151020&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=151020&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])
36	492	-	-	-	-	-	-	-
37	493	-	-	-	-	-	-	-
38	481	-	-	-	-	-	-	-
39	462	-	-	-	-	-	-	-
40	457	-	-	-	-	-	-	-
41	442	-	-	-	-	-	-	-
42	439	-	-	-	-	-	-	-
43	488	-	-	-	-	-	-	-
44	521	-	-	-	-	-	-	-
45	501	-	-	-	-	-	-	-
46	485	-	-	-	-	-	-	-
47	464	-	-	-	-	-	-	-
48	460	-	-	-	-	-	-	-
49	467	461.3537	452.633	470.0744	0.1022	0.6195	0	0.6195
50	460	450.5727	440.0305	461.1149	0.0398	0.0011	0	0.0398
51	448	433.2	421.4555	444.9445	0.0068	0	0	0
52	443	426.3835	413.6287	439.1382	0.0053	4e-04	0	0
53	436	412.0328	398.3619	425.7038	3e-04	0	0	0
54	431	409.2682	394.7437	423.7926	0.0017	2e-04	0	0
55	484	458.0956	442.7663	473.425	5e-04	0.9997	1e-04	0.4038
56	510	489.1908	473.0971	505.2845	0.0056	0.7364	1e-04	0.9998
57	513	470.5724	453.7492	487.3957	0	0	2e-04	0.891
58	503	452.7577	435.2352	470.2803	0	0	2e-04	0.2089
59	471	432.672	414.4767	450.8673	0	0	4e-04	0.0016
60	471	430.1947	411.3497	449.0397	0	0	0.001	0.001

Univariate ARIMA Extrapolation Forecast Performance
time	% S.E.	PE	MAPE	Sq.E	MSE	RMSE
49	0.0096	0.0122	0	31.8811	0	0
50	0.0119	0.0209	0.0166	88.8738	60.3775	7.7703
51	0.0138	0.0342	0.0224	219.0413	113.2654	10.6426
52	0.0153	0.039	0.0266	276.1096	153.9765	12.4087
53	0.0169	0.0582	0.0329	574.4244	238.0661	15.4294
54	0.0181	0.0531	0.0363	472.2728	277.1005	16.6463
55	0.0171	0.0565	0.0392	671.0364	333.3771	18.2586
56	0.0168	0.0425	0.0396	433.0215	345.8326	18.5966
57	0.0182	0.0902	0.0452	1800.0979	507.4177	22.5259
58	0.0197	0.111	0.0518	2524.2856	709.1045	26.629
59	0.0215	0.0886	0.0551	1469.0365	778.1892	27.896
60	0.0223	0.0949	0.0584	1665.0711	852.096	29.1907

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
49 & 0.0096 & 0.0122 & 0 & 31.8811 & 0 & 0 \tabularnewline
50 & 0.0119 & 0.0209 & 0.0166 & 88.8738 & 60.3775 & 7.7703 \tabularnewline
51 & 0.0138 & 0.0342 & 0.0224 & 219.0413 & 113.2654 & 10.6426 \tabularnewline
52 & 0.0153 & 0.039 & 0.0266 & 276.1096 & 153.9765 & 12.4087 \tabularnewline
53 & 0.0169 & 0.0582 & 0.0329 & 574.4244 & 238.0661 & 15.4294 \tabularnewline
54 & 0.0181 & 0.0531 & 0.0363 & 472.2728 & 277.1005 & 16.6463 \tabularnewline
55 & 0.0171 & 0.0565 & 0.0392 & 671.0364 & 333.3771 & 18.2586 \tabularnewline
56 & 0.0168 & 0.0425 & 0.0396 & 433.0215 & 345.8326 & 18.5966 \tabularnewline
57 & 0.0182 & 0.0902 & 0.0452 & 1800.0979 & 507.4177 & 22.5259 \tabularnewline
58 & 0.0197 & 0.111 & 0.0518 & 2524.2856 & 709.1045 & 26.629 \tabularnewline
59 & 0.0215 & 0.0886 & 0.0551 & 1469.0365 & 778.1892 & 27.896 \tabularnewline
60 & 0.0223 & 0.0949 & 0.0584 & 1665.0711 & 852.096 & 29.1907 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=151020&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]0.0096[/C][C]0.0122[/C][C]0[/C][C]31.8811[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]50[/C][C]0.0119[/C][C]0.0209[/C][C]0.0166[/C][C]88.8738[/C][C]60.3775[/C][C]7.7703[/C][/ROW]
[ROW][C]51[/C][C]0.0138[/C][C]0.0342[/C][C]0.0224[/C][C]219.0413[/C][C]113.2654[/C][C]10.6426[/C][/ROW]
[ROW][C]52[/C][C]0.0153[/C][C]0.039[/C][C]0.0266[/C][C]276.1096[/C][C]153.9765[/C][C]12.4087[/C][/ROW]
[ROW][C]53[/C][C]0.0169[/C][C]0.0582[/C][C]0.0329[/C][C]574.4244[/C][C]238.0661[/C][C]15.4294[/C][/ROW]
[ROW][C]54[/C][C]0.0181[/C][C]0.0531[/C][C]0.0363[/C][C]472.2728[/C][C]277.1005[/C][C]16.6463[/C][/ROW]
[ROW][C]55[/C][C]0.0171[/C][C]0.0565[/C][C]0.0392[/C][C]671.0364[/C][C]333.3771[/C][C]18.2586[/C][/ROW]
[ROW][C]56[/C][C]0.0168[/C][C]0.0425[/C][C]0.0396[/C][C]433.0215[/C][C]345.8326[/C][C]18.5966[/C][/ROW]
[ROW][C]57[/C][C]0.0182[/C][C]0.0902[/C][C]0.0452[/C][C]1800.0979[/C][C]507.4177[/C][C]22.5259[/C][/ROW]
[ROW][C]58[/C][C]0.0197[/C][C]0.111[/C][C]0.0518[/C][C]2524.2856[/C][C]709.1045[/C][C]26.629[/C][/ROW]
[ROW][C]59[/C][C]0.0215[/C][C]0.0886[/C][C]0.0551[/C][C]1469.0365[/C][C]778.1892[/C][C]27.896[/C][/ROW]
[ROW][C]60[/C][C]0.0223[/C][C]0.0949[/C][C]0.0584[/C][C]1665.0711[/C][C]852.096[/C][C]29.1907[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=151020&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=151020&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	0.0096	0.0122	0	31.8811	0	0
50	0.0119	0.0209	0.0166	88.8738	60.3775	7.7703
51	0.0138	0.0342	0.0224	219.0413	113.2654	10.6426
52	0.0153	0.039	0.0266	276.1096	153.9765	12.4087
53	0.0169	0.0582	0.0329	574.4244	238.0661	15.4294
54	0.0181	0.0531	0.0363	472.2728	277.1005	16.6463
55	0.0171	0.0565	0.0392	671.0364	333.3771	18.2586
56	0.0168	0.0425	0.0396	433.0215	345.8326	18.5966
57	0.0182	0.0902	0.0452	1800.0979	507.4177	22.5259
58	0.0197	0.111	0.0518	2524.2856	709.1045	26.629
59	0.0215	0.0886	0.0551	1469.0365	778.1892	27.896
60	0.0223	0.0949	0.0584	1665.0711	852.096	29.1907

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

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

Parameters (R input):

par1 = 12 ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 1 ; par7 = 1 ; 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,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