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R Software Module

rwasp_arimaforecasting.wasp

Title produced by software

ARIMA Forecasting

Date of computation

Wed, 21 Dec 2016 13:22:07 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/21/t1482323370tcp1zxioj0ch6am.htm/, Retrieved Sun, 04 May 2025 05:40:42 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=302223, Retrieved Sun, 04 May 2025 05:40:42 +0000

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No (this computation is public)

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Estimated Impact

135

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

-       [ARIMA Forecasting] [ARIMA forecasting] [2016-12-21 12:22:07] [36884fbde1107444791dd71ee0072a5a] [Current]

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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	1 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time1 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302223&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

1 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=302223&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302223&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	Big Analytics Cloud Computing Center

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[102])
90	3432	-	-	-	-	-	-	-
91	5282	-	-	-	-	-	-	-
92	6635	-	-	-	-	-	-	-
93	4222	-	-	-	-	-	-	-
94	7317	-	-	-	-	-	-	-
95	4132	-	-	-	-	-	-	-
96	5048	-	-	-	-	-	-	-
97	4383	-	-	-	-	-	-	-
98	3761	-	-	-	-	-	-	-
99	4081	-	-	-	-	-	-	-
100	6491	-	-	-	-	-	-	-
101	5859	-	-	-	-	-	-	-
102	7139	-	-	-	-	-	-	-
103	7682	4841.8538	2097.7589	14828.5883	0.2886	0.3261	0.4656	0.3261
104	8649	3942.1822	1705.3549	12106.6173	0.1292	0.1846	0.259	0.2214
105	6146	4401.7622	1838.4143	14414.5441	0.3664	0.2029	0.514	0.296
106	7137	5044.5782	2031.2103	17698.4973	0.3729	0.4323	0.3624	0.3728
107	9948	5064.1779	2030.328	17914.8288	0.2282	0.3759	0.5565	0.3758
108	15819	4941.2635	1986.5497	17387.2321	0.0434	0.2152	0.4933	0.3646
109	8370	4294.3724	1780.1737	14260.3241	0.2114	0.0117	0.493	0.2879
110	13222	4215.1297	1750.2565	13953.4752	0.0349	0.2015	0.5364	0.2781
111	16711	5622.9758	2172.9836	21372.6197	0.0838	0.1722	0.5761	0.4252
112	19059	5626.0315	2167.8329	21508.5744	0.0487	0.0857	0.4575	0.4259
113	8303	5113.3374	2011.7118	18762.4734	0.3235	0.0226	0.4574	0.3856
114	20781	5441.7325	2102.6363	20689.7072	0.0243	0.3565	0.4136	0.4136
115	9638	4770.3862	1870.9683	17610.7399	0.2287	0.0073	0.3284	0.3588
116	13444	4197.7954	1687.4233	14774.9541	0.0433	0.1567	0.2047	0.2929
117	6072	4649.1785	1815.7233	17306.5761	0.4128	0.0866	0.4084	0.3499
118	13442	5482.7048	2047.0784	22337.4899	0.1773	0.4727	0.4237	0.4236
119	14457	5354.7023	2004.2468	21705.6598	0.1376	0.1662	0.291	0.4153
120	17705	5288.0692	1978.3937	21455.7728	0.0661	0.1332	0.1009	0.4112
121	16463	4583.4949	1771.5531	17416.2611	0.0348	0.0225	0.2815	0.3482
122	19194	4465.7073	1731.7226	16858.2562	0.0099	0.0289	0.083	0.3362
123	20688	5923.7686	2130.104	26102.6997	0.0758	0.0987	0.1474	0.453
124	14739	6075.9995	2162.9752	27353.5829	0.2124	0.0892	0.1159	0.461
125	12702	5509.4164	2006.0143	23645.7654	0.2185	0.1593	0.3814	0.4301
126	15760	5910.0637	2105.5249	26562.7468	0.1749	0.2596	0.0791	0.4536

\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[102]) \tabularnewline
90 & 3432 & - & - & - & - & - & - & - \tabularnewline
91 & 5282 & - & - & - & - & - & - & - \tabularnewline
92 & 6635 & - & - & - & - & - & - & - \tabularnewline
93 & 4222 & - & - & - & - & - & - & - \tabularnewline
94 & 7317 & - & - & - & - & - & - & - \tabularnewline
95 & 4132 & - & - & - & - & - & - & - \tabularnewline
96 & 5048 & - & - & - & - & - & - & - \tabularnewline
97 & 4383 & - & - & - & - & - & - & - \tabularnewline
98 & 3761 & - & - & - & - & - & - & - \tabularnewline
99 & 4081 & - & - & - & - & - & - & - \tabularnewline
100 & 6491 & - & - & - & - & - & - & - \tabularnewline
101 & 5859 & - & - & - & - & - & - & - \tabularnewline
102 & 7139 & - & - & - & - & - & - & - \tabularnewline
103 & 7682 & 4841.8538 & 2097.7589 & 14828.5883 & 0.2886 & 0.3261 & 0.4656 & 0.3261 \tabularnewline
104 & 8649 & 3942.1822 & 1705.3549 & 12106.6173 & 0.1292 & 0.1846 & 0.259 & 0.2214 \tabularnewline
105 & 6146 & 4401.7622 & 1838.4143 & 14414.5441 & 0.3664 & 0.2029 & 0.514 & 0.296 \tabularnewline
106 & 7137 & 5044.5782 & 2031.2103 & 17698.4973 & 0.3729 & 0.4323 & 0.3624 & 0.3728 \tabularnewline
107 & 9948 & 5064.1779 & 2030.328 & 17914.8288 & 0.2282 & 0.3759 & 0.5565 & 0.3758 \tabularnewline
108 & 15819 & 4941.2635 & 1986.5497 & 17387.2321 & 0.0434 & 0.2152 & 0.4933 & 0.3646 \tabularnewline
109 & 8370 & 4294.3724 & 1780.1737 & 14260.3241 & 0.2114 & 0.0117 & 0.493 & 0.2879 \tabularnewline
110 & 13222 & 4215.1297 & 1750.2565 & 13953.4752 & 0.0349 & 0.2015 & 0.5364 & 0.2781 \tabularnewline
111 & 16711 & 5622.9758 & 2172.9836 & 21372.6197 & 0.0838 & 0.1722 & 0.5761 & 0.4252 \tabularnewline
112 & 19059 & 5626.0315 & 2167.8329 & 21508.5744 & 0.0487 & 0.0857 & 0.4575 & 0.4259 \tabularnewline
113 & 8303 & 5113.3374 & 2011.7118 & 18762.4734 & 0.3235 & 0.0226 & 0.4574 & 0.3856 \tabularnewline
114 & 20781 & 5441.7325 & 2102.6363 & 20689.7072 & 0.0243 & 0.3565 & 0.4136 & 0.4136 \tabularnewline
115 & 9638 & 4770.3862 & 1870.9683 & 17610.7399 & 0.2287 & 0.0073 & 0.3284 & 0.3588 \tabularnewline
116 & 13444 & 4197.7954 & 1687.4233 & 14774.9541 & 0.0433 & 0.1567 & 0.2047 & 0.2929 \tabularnewline
117 & 6072 & 4649.1785 & 1815.7233 & 17306.5761 & 0.4128 & 0.0866 & 0.4084 & 0.3499 \tabularnewline
118 & 13442 & 5482.7048 & 2047.0784 & 22337.4899 & 0.1773 & 0.4727 & 0.4237 & 0.4236 \tabularnewline
119 & 14457 & 5354.7023 & 2004.2468 & 21705.6598 & 0.1376 & 0.1662 & 0.291 & 0.4153 \tabularnewline
120 & 17705 & 5288.0692 & 1978.3937 & 21455.7728 & 0.0661 & 0.1332 & 0.1009 & 0.4112 \tabularnewline
121 & 16463 & 4583.4949 & 1771.5531 & 17416.2611 & 0.0348 & 0.0225 & 0.2815 & 0.3482 \tabularnewline
122 & 19194 & 4465.7073 & 1731.7226 & 16858.2562 & 0.0099 & 0.0289 & 0.083 & 0.3362 \tabularnewline
123 & 20688 & 5923.7686 & 2130.104 & 26102.6997 & 0.0758 & 0.0987 & 0.1474 & 0.453 \tabularnewline
124 & 14739 & 6075.9995 & 2162.9752 & 27353.5829 & 0.2124 & 0.0892 & 0.1159 & 0.461 \tabularnewline
125 & 12702 & 5509.4164 & 2006.0143 & 23645.7654 & 0.2185 & 0.1593 & 0.3814 & 0.4301 \tabularnewline
126 & 15760 & 5910.0637 & 2105.5249 & 26562.7468 & 0.1749 & 0.2596 & 0.0791 & 0.4536 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302223&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[102])[/C][/ROW]
[ROW][C]90[/C][C]3432[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]91[/C][C]5282[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]92[/C][C]6635[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]93[/C][C]4222[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]94[/C][C]7317[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]95[/C][C]4132[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]96[/C][C]5048[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]97[/C][C]4383[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]98[/C][C]3761[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]99[/C][C]4081[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]100[/C][C]6491[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]101[/C][C]5859[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]102[/C][C]7139[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]103[/C][C]7682[/C][C]4841.8538[/C][C]2097.7589[/C][C]14828.5883[/C][C]0.2886[/C][C]0.3261[/C][C]0.4656[/C][C]0.3261[/C][/ROW]
[ROW][C]104[/C][C]8649[/C][C]3942.1822[/C][C]1705.3549[/C][C]12106.6173[/C][C]0.1292[/C][C]0.1846[/C][C]0.259[/C][C]0.2214[/C][/ROW]
[ROW][C]105[/C][C]6146[/C][C]4401.7622[/C][C]1838.4143[/C][C]14414.5441[/C][C]0.3664[/C][C]0.2029[/C][C]0.514[/C][C]0.296[/C][/ROW]
[ROW][C]106[/C][C]7137[/C][C]5044.5782[/C][C]2031.2103[/C][C]17698.4973[/C][C]0.3729[/C][C]0.4323[/C][C]0.3624[/C][C]0.3728[/C][/ROW]
[ROW][C]107[/C][C]9948[/C][C]5064.1779[/C][C]2030.328[/C][C]17914.8288[/C][C]0.2282[/C][C]0.3759[/C][C]0.5565[/C][C]0.3758[/C][/ROW]
[ROW][C]108[/C][C]15819[/C][C]4941.2635[/C][C]1986.5497[/C][C]17387.2321[/C][C]0.0434[/C][C]0.2152[/C][C]0.4933[/C][C]0.3646[/C][/ROW]
[ROW][C]109[/C][C]8370[/C][C]4294.3724[/C][C]1780.1737[/C][C]14260.3241[/C][C]0.2114[/C][C]0.0117[/C][C]0.493[/C][C]0.2879[/C][/ROW]
[ROW][C]110[/C][C]13222[/C][C]4215.1297[/C][C]1750.2565[/C][C]13953.4752[/C][C]0.0349[/C][C]0.2015[/C][C]0.5364[/C][C]0.2781[/C][/ROW]
[ROW][C]111[/C][C]16711[/C][C]5622.9758[/C][C]2172.9836[/C][C]21372.6197[/C][C]0.0838[/C][C]0.1722[/C][C]0.5761[/C][C]0.4252[/C][/ROW]
[ROW][C]112[/C][C]19059[/C][C]5626.0315[/C][C]2167.8329[/C][C]21508.5744[/C][C]0.0487[/C][C]0.0857[/C][C]0.4575[/C][C]0.4259[/C][/ROW]
[ROW][C]113[/C][C]8303[/C][C]5113.3374[/C][C]2011.7118[/C][C]18762.4734[/C][C]0.3235[/C][C]0.0226[/C][C]0.4574[/C][C]0.3856[/C][/ROW]
[ROW][C]114[/C][C]20781[/C][C]5441.7325[/C][C]2102.6363[/C][C]20689.7072[/C][C]0.0243[/C][C]0.3565[/C][C]0.4136[/C][C]0.4136[/C][/ROW]
[ROW][C]115[/C][C]9638[/C][C]4770.3862[/C][C]1870.9683[/C][C]17610.7399[/C][C]0.2287[/C][C]0.0073[/C][C]0.3284[/C][C]0.3588[/C][/ROW]
[ROW][C]116[/C][C]13444[/C][C]4197.7954[/C][C]1687.4233[/C][C]14774.9541[/C][C]0.0433[/C][C]0.1567[/C][C]0.2047[/C][C]0.2929[/C][/ROW]
[ROW][C]117[/C][C]6072[/C][C]4649.1785[/C][C]1815.7233[/C][C]17306.5761[/C][C]0.4128[/C][C]0.0866[/C][C]0.4084[/C][C]0.3499[/C][/ROW]
[ROW][C]118[/C][C]13442[/C][C]5482.7048[/C][C]2047.0784[/C][C]22337.4899[/C][C]0.1773[/C][C]0.4727[/C][C]0.4237[/C][C]0.4236[/C][/ROW]
[ROW][C]119[/C][C]14457[/C][C]5354.7023[/C][C]2004.2468[/C][C]21705.6598[/C][C]0.1376[/C][C]0.1662[/C][C]0.291[/C][C]0.4153[/C][/ROW]
[ROW][C]120[/C][C]17705[/C][C]5288.0692[/C][C]1978.3937[/C][C]21455.7728[/C][C]0.0661[/C][C]0.1332[/C][C]0.1009[/C][C]0.4112[/C][/ROW]
[ROW][C]121[/C][C]16463[/C][C]4583.4949[/C][C]1771.5531[/C][C]17416.2611[/C][C]0.0348[/C][C]0.0225[/C][C]0.2815[/C][C]0.3482[/C][/ROW]
[ROW][C]122[/C][C]19194[/C][C]4465.7073[/C][C]1731.7226[/C][C]16858.2562[/C][C]0.0099[/C][C]0.0289[/C][C]0.083[/C][C]0.3362[/C][/ROW]
[ROW][C]123[/C][C]20688[/C][C]5923.7686[/C][C]2130.104[/C][C]26102.6997[/C][C]0.0758[/C][C]0.0987[/C][C]0.1474[/C][C]0.453[/C][/ROW]
[ROW][C]124[/C][C]14739[/C][C]6075.9995[/C][C]2162.9752[/C][C]27353.5829[/C][C]0.2124[/C][C]0.0892[/C][C]0.1159[/C][C]0.461[/C][/ROW]
[ROW][C]125[/C][C]12702[/C][C]5509.4164[/C][C]2006.0143[/C][C]23645.7654[/C][C]0.2185[/C][C]0.1593[/C][C]0.3814[/C][C]0.4301[/C][/ROW]
[ROW][C]126[/C][C]15760[/C][C]5910.0637[/C][C]2105.5249[/C][C]26562.7468[/C][C]0.1749[/C][C]0.2596[/C][C]0.0791[/C][C]0.4536[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302223&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302223&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[102])
90	3432	-	-	-	-	-	-	-
91	5282	-	-	-	-	-	-	-
92	6635	-	-	-	-	-	-	-
93	4222	-	-	-	-	-	-	-
94	7317	-	-	-	-	-	-	-
95	4132	-	-	-	-	-	-	-
96	5048	-	-	-	-	-	-	-
97	4383	-	-	-	-	-	-	-
98	3761	-	-	-	-	-	-	-
99	4081	-	-	-	-	-	-	-
100	6491	-	-	-	-	-	-	-
101	5859	-	-	-	-	-	-	-
102	7139	-	-	-	-	-	-	-
103	7682	4841.8538	2097.7589	14828.5883	0.2886	0.3261	0.4656	0.3261
104	8649	3942.1822	1705.3549	12106.6173	0.1292	0.1846	0.259	0.2214
105	6146	4401.7622	1838.4143	14414.5441	0.3664	0.2029	0.514	0.296
106	7137	5044.5782	2031.2103	17698.4973	0.3729	0.4323	0.3624	0.3728
107	9948	5064.1779	2030.328	17914.8288	0.2282	0.3759	0.5565	0.3758
108	15819	4941.2635	1986.5497	17387.2321	0.0434	0.2152	0.4933	0.3646
109	8370	4294.3724	1780.1737	14260.3241	0.2114	0.0117	0.493	0.2879
110	13222	4215.1297	1750.2565	13953.4752	0.0349	0.2015	0.5364	0.2781
111	16711	5622.9758	2172.9836	21372.6197	0.0838	0.1722	0.5761	0.4252
112	19059	5626.0315	2167.8329	21508.5744	0.0487	0.0857	0.4575	0.4259
113	8303	5113.3374	2011.7118	18762.4734	0.3235	0.0226	0.4574	0.3856
114	20781	5441.7325	2102.6363	20689.7072	0.0243	0.3565	0.4136	0.4136
115	9638	4770.3862	1870.9683	17610.7399	0.2287	0.0073	0.3284	0.3588
116	13444	4197.7954	1687.4233	14774.9541	0.0433	0.1567	0.2047	0.2929
117	6072	4649.1785	1815.7233	17306.5761	0.4128	0.0866	0.4084	0.3499
118	13442	5482.7048	2047.0784	22337.4899	0.1773	0.4727	0.4237	0.4236
119	14457	5354.7023	2004.2468	21705.6598	0.1376	0.1662	0.291	0.4153
120	17705	5288.0692	1978.3937	21455.7728	0.0661	0.1332	0.1009	0.4112
121	16463	4583.4949	1771.5531	17416.2611	0.0348	0.0225	0.2815	0.3482
122	19194	4465.7073	1731.7226	16858.2562	0.0099	0.0289	0.083	0.3362
123	20688	5923.7686	2130.104	26102.6997	0.0758	0.0987	0.1474	0.453
124	14739	6075.9995	2162.9752	27353.5829	0.2124	0.0892	0.1159	0.461
125	12702	5509.4164	2006.0143	23645.7654	0.2185	0.1593	0.3814	0.4301
126	15760	5910.0637	2105.5249	26562.7468	0.1749	0.2596	0.0791	0.4536

Univariate ARIMA Extrapolation Forecast Performance
time	% S.E.	PE	MAPE	sMAPE	Sq.E	MSE	RMSE	ScaledE	MASE
103	1.0523	0.3697	0.3697	0.4536	8066430.5478	0	0	0.6222	0.6222
104	1.0567	0.5442	0.457	0.6006	22154133.8083	15110282.178	3887.1946	1.0312	0.8267
105	1.1606	0.2838	0.3992	0.5106	3042365.673	11087643.343	3329.8113	0.3821	0.6785
106	1.2798	0.2932	0.3727	0.4689	4378228.8387	9410289.717	3067.6196	0.4584	0.6235
107	1.2947	0.4909	0.3964	0.5052	23851718.7018	12298575.5139	3506.9325	1.07	0.7128
108	1.2851	0.6876	0.4449	0.5957	118325151.3407	29969671.4851	5474.4563	2.3832	0.9912
109	1.184	0.4869	0.4509	0.6025	16610740.213	28061252.7319	5297.2873	0.8929	0.9772
110	1.1787	0.6812	0.4797	0.6563	81123712.9974	34694060.2651	5890.1664	1.9733	1.1017
111	1.4291	0.6635	0.5001	0.6937	122944279.8371	44499640.2175	6670.8051	2.4293	1.2492
112	1.4403	0.7048	0.5206	0.7332	180444644.0173	58094140.5975	7621.9512	2.943	1.4186
113	1.3619	0.3842	0.5082	0.7098	10173947.5815	53737759.4142	7330.6043	0.6988	1.3531
114	1.4296	0.7381	0.5274	0.7481	235293127.2176	68867373.3979	8298.6368	3.3607	1.5204
115	1.3733	0.505	0.5256	0.7425	23693664.2549	65392472.6946	8086.5612	1.0664	1.4855
116	1.2856	0.6878	0.5372	0.7644	85492299.7672	66828174.6283	8174.8501	2.0257	1.5241
117	1.389	0.2343	0.517	0.7311	2024421.0698	62507924.3911	7906.1953	0.3117	1.4433
118	1.5685	0.5921	0.5217	0.738	63350380.5646	62560577.9019	7909.5245	1.7438	1.4621
119	1.5579	0.6296	0.5281	0.7486	82851823.6644	63754180.5938	7984.6215	1.9942	1.4934
120	1.5599	0.7013	0.5377	0.767	154180169.4615	68777846.642	8293.241	2.7204	1.5615
121	1.4285	0.7216	0.5474	0.7861	141122640.7214	72585467.3831	8519.7105	2.6027	1.6163
122	1.4158	0.7673	0.5584	0.809	216922604.622	79802324.245	8933.2147	3.2268	1.6969
123	1.738	0.7137	0.5658	0.8233	217982528.3879	86382333.9661	9294.2097	3.2347	1.7701
124	1.7867	0.5878	0.5668	0.8238	75047577.533	85867117.7646	9266.4512	1.898	1.7759
125	1.6795	0.5663	0.5667	0.8223	51733258.6099	84383036.9318	9186.024	1.5758	1.7672
126	1.7829	0.625	0.5692	0.8259	97021245.591	84909628.9592	9214.6421	2.158	1.7835

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & sMAPE & Sq.E & MSE & RMSE & ScaledE & MASE \tabularnewline
103 & 1.0523 & 0.3697 & 0.3697 & 0.4536 & 8066430.5478 & 0 & 0 & 0.6222 & 0.6222 \tabularnewline
104 & 1.0567 & 0.5442 & 0.457 & 0.6006 & 22154133.8083 & 15110282.178 & 3887.1946 & 1.0312 & 0.8267 \tabularnewline
105 & 1.1606 & 0.2838 & 0.3992 & 0.5106 & 3042365.673 & 11087643.343 & 3329.8113 & 0.3821 & 0.6785 \tabularnewline
106 & 1.2798 & 0.2932 & 0.3727 & 0.4689 & 4378228.8387 & 9410289.717 & 3067.6196 & 0.4584 & 0.6235 \tabularnewline
107 & 1.2947 & 0.4909 & 0.3964 & 0.5052 & 23851718.7018 & 12298575.5139 & 3506.9325 & 1.07 & 0.7128 \tabularnewline
108 & 1.2851 & 0.6876 & 0.4449 & 0.5957 & 118325151.3407 & 29969671.4851 & 5474.4563 & 2.3832 & 0.9912 \tabularnewline
109 & 1.184 & 0.4869 & 0.4509 & 0.6025 & 16610740.213 & 28061252.7319 & 5297.2873 & 0.8929 & 0.9772 \tabularnewline
110 & 1.1787 & 0.6812 & 0.4797 & 0.6563 & 81123712.9974 & 34694060.2651 & 5890.1664 & 1.9733 & 1.1017 \tabularnewline
111 & 1.4291 & 0.6635 & 0.5001 & 0.6937 & 122944279.8371 & 44499640.2175 & 6670.8051 & 2.4293 & 1.2492 \tabularnewline
112 & 1.4403 & 0.7048 & 0.5206 & 0.7332 & 180444644.0173 & 58094140.5975 & 7621.9512 & 2.943 & 1.4186 \tabularnewline
113 & 1.3619 & 0.3842 & 0.5082 & 0.7098 & 10173947.5815 & 53737759.4142 & 7330.6043 & 0.6988 & 1.3531 \tabularnewline
114 & 1.4296 & 0.7381 & 0.5274 & 0.7481 & 235293127.2176 & 68867373.3979 & 8298.6368 & 3.3607 & 1.5204 \tabularnewline
115 & 1.3733 & 0.505 & 0.5256 & 0.7425 & 23693664.2549 & 65392472.6946 & 8086.5612 & 1.0664 & 1.4855 \tabularnewline
116 & 1.2856 & 0.6878 & 0.5372 & 0.7644 & 85492299.7672 & 66828174.6283 & 8174.8501 & 2.0257 & 1.5241 \tabularnewline
117 & 1.389 & 0.2343 & 0.517 & 0.7311 & 2024421.0698 & 62507924.3911 & 7906.1953 & 0.3117 & 1.4433 \tabularnewline
118 & 1.5685 & 0.5921 & 0.5217 & 0.738 & 63350380.5646 & 62560577.9019 & 7909.5245 & 1.7438 & 1.4621 \tabularnewline
119 & 1.5579 & 0.6296 & 0.5281 & 0.7486 & 82851823.6644 & 63754180.5938 & 7984.6215 & 1.9942 & 1.4934 \tabularnewline
120 & 1.5599 & 0.7013 & 0.5377 & 0.767 & 154180169.4615 & 68777846.642 & 8293.241 & 2.7204 & 1.5615 \tabularnewline
121 & 1.4285 & 0.7216 & 0.5474 & 0.7861 & 141122640.7214 & 72585467.3831 & 8519.7105 & 2.6027 & 1.6163 \tabularnewline
122 & 1.4158 & 0.7673 & 0.5584 & 0.809 & 216922604.622 & 79802324.245 & 8933.2147 & 3.2268 & 1.6969 \tabularnewline
123 & 1.738 & 0.7137 & 0.5658 & 0.8233 & 217982528.3879 & 86382333.9661 & 9294.2097 & 3.2347 & 1.7701 \tabularnewline
124 & 1.7867 & 0.5878 & 0.5668 & 0.8238 & 75047577.533 & 85867117.7646 & 9266.4512 & 1.898 & 1.7759 \tabularnewline
125 & 1.6795 & 0.5663 & 0.5667 & 0.8223 & 51733258.6099 & 84383036.9318 & 9186.024 & 1.5758 & 1.7672 \tabularnewline
126 & 1.7829 & 0.625 & 0.5692 & 0.8259 & 97021245.591 & 84909628.9592 & 9214.6421 & 2.158 & 1.7835 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302223&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]sMAPE[/C][C]Sq.E[/C][C]MSE[/C][C]RMSE[/C][C]ScaledE[/C][C]MASE[/C][/ROW]
[ROW][C]103[/C][C]1.0523[/C][C]0.3697[/C][C]0.3697[/C][C]0.4536[/C][C]8066430.5478[/C][C]0[/C][C]0[/C][C]0.6222[/C][C]0.6222[/C][/ROW]
[ROW][C]104[/C][C]1.0567[/C][C]0.5442[/C][C]0.457[/C][C]0.6006[/C][C]22154133.8083[/C][C]15110282.178[/C][C]3887.1946[/C][C]1.0312[/C][C]0.8267[/C][/ROW]
[ROW][C]105[/C][C]1.1606[/C][C]0.2838[/C][C]0.3992[/C][C]0.5106[/C][C]3042365.673[/C][C]11087643.343[/C][C]3329.8113[/C][C]0.3821[/C][C]0.6785[/C][/ROW]
[ROW][C]106[/C][C]1.2798[/C][C]0.2932[/C][C]0.3727[/C][C]0.4689[/C][C]4378228.8387[/C][C]9410289.717[/C][C]3067.6196[/C][C]0.4584[/C][C]0.6235[/C][/ROW]
[ROW][C]107[/C][C]1.2947[/C][C]0.4909[/C][C]0.3964[/C][C]0.5052[/C][C]23851718.7018[/C][C]12298575.5139[/C][C]3506.9325[/C][C]1.07[/C][C]0.7128[/C][/ROW]
[ROW][C]108[/C][C]1.2851[/C][C]0.6876[/C][C]0.4449[/C][C]0.5957[/C][C]118325151.3407[/C][C]29969671.4851[/C][C]5474.4563[/C][C]2.3832[/C][C]0.9912[/C][/ROW]
[ROW][C]109[/C][C]1.184[/C][C]0.4869[/C][C]0.4509[/C][C]0.6025[/C][C]16610740.213[/C][C]28061252.7319[/C][C]5297.2873[/C][C]0.8929[/C][C]0.9772[/C][/ROW]
[ROW][C]110[/C][C]1.1787[/C][C]0.6812[/C][C]0.4797[/C][C]0.6563[/C][C]81123712.9974[/C][C]34694060.2651[/C][C]5890.1664[/C][C]1.9733[/C][C]1.1017[/C][/ROW]
[ROW][C]111[/C][C]1.4291[/C][C]0.6635[/C][C]0.5001[/C][C]0.6937[/C][C]122944279.8371[/C][C]44499640.2175[/C][C]6670.8051[/C][C]2.4293[/C][C]1.2492[/C][/ROW]
[ROW][C]112[/C][C]1.4403[/C][C]0.7048[/C][C]0.5206[/C][C]0.7332[/C][C]180444644.0173[/C][C]58094140.5975[/C][C]7621.9512[/C][C]2.943[/C][C]1.4186[/C][/ROW]
[ROW][C]113[/C][C]1.3619[/C][C]0.3842[/C][C]0.5082[/C][C]0.7098[/C][C]10173947.5815[/C][C]53737759.4142[/C][C]7330.6043[/C][C]0.6988[/C][C]1.3531[/C][/ROW]
[ROW][C]114[/C][C]1.4296[/C][C]0.7381[/C][C]0.5274[/C][C]0.7481[/C][C]235293127.2176[/C][C]68867373.3979[/C][C]8298.6368[/C][C]3.3607[/C][C]1.5204[/C][/ROW]
[ROW][C]115[/C][C]1.3733[/C][C]0.505[/C][C]0.5256[/C][C]0.7425[/C][C]23693664.2549[/C][C]65392472.6946[/C][C]8086.5612[/C][C]1.0664[/C][C]1.4855[/C][/ROW]
[ROW][C]116[/C][C]1.2856[/C][C]0.6878[/C][C]0.5372[/C][C]0.7644[/C][C]85492299.7672[/C][C]66828174.6283[/C][C]8174.8501[/C][C]2.0257[/C][C]1.5241[/C][/ROW]
[ROW][C]117[/C][C]1.389[/C][C]0.2343[/C][C]0.517[/C][C]0.7311[/C][C]2024421.0698[/C][C]62507924.3911[/C][C]7906.1953[/C][C]0.3117[/C][C]1.4433[/C][/ROW]
[ROW][C]118[/C][C]1.5685[/C][C]0.5921[/C][C]0.5217[/C][C]0.738[/C][C]63350380.5646[/C][C]62560577.9019[/C][C]7909.5245[/C][C]1.7438[/C][C]1.4621[/C][/ROW]
[ROW][C]119[/C][C]1.5579[/C][C]0.6296[/C][C]0.5281[/C][C]0.7486[/C][C]82851823.6644[/C][C]63754180.5938[/C][C]7984.6215[/C][C]1.9942[/C][C]1.4934[/C][/ROW]
[ROW][C]120[/C][C]1.5599[/C][C]0.7013[/C][C]0.5377[/C][C]0.767[/C][C]154180169.4615[/C][C]68777846.642[/C][C]8293.241[/C][C]2.7204[/C][C]1.5615[/C][/ROW]
[ROW][C]121[/C][C]1.4285[/C][C]0.7216[/C][C]0.5474[/C][C]0.7861[/C][C]141122640.7214[/C][C]72585467.3831[/C][C]8519.7105[/C][C]2.6027[/C][C]1.6163[/C][/ROW]
[ROW][C]122[/C][C]1.4158[/C][C]0.7673[/C][C]0.5584[/C][C]0.809[/C][C]216922604.622[/C][C]79802324.245[/C][C]8933.2147[/C][C]3.2268[/C][C]1.6969[/C][/ROW]
[ROW][C]123[/C][C]1.738[/C][C]0.7137[/C][C]0.5658[/C][C]0.8233[/C][C]217982528.3879[/C][C]86382333.9661[/C][C]9294.2097[/C][C]3.2347[/C][C]1.7701[/C][/ROW]
[ROW][C]124[/C][C]1.7867[/C][C]0.5878[/C][C]0.5668[/C][C]0.8238[/C][C]75047577.533[/C][C]85867117.7646[/C][C]9266.4512[/C][C]1.898[/C][C]1.7759[/C][/ROW]
[ROW][C]125[/C][C]1.6795[/C][C]0.5663[/C][C]0.5667[/C][C]0.8223[/C][C]51733258.6099[/C][C]84383036.9318[/C][C]9186.024[/C][C]1.5758[/C][C]1.7672[/C][/ROW]
[ROW][C]126[/C][C]1.7829[/C][C]0.625[/C][C]0.5692[/C][C]0.8259[/C][C]97021245.591[/C][C]84909628.9592[/C][C]9214.6421[/C][C]2.158[/C][C]1.7835[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302223&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302223&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	sMAPE	Sq.E	MSE	RMSE	ScaledE	MASE
103	1.0523	0.3697	0.3697	0.4536	8066430.5478	0	0	0.6222	0.6222
104	1.0567	0.5442	0.457	0.6006	22154133.8083	15110282.178	3887.1946	1.0312	0.8267
105	1.1606	0.2838	0.3992	0.5106	3042365.673	11087643.343	3329.8113	0.3821	0.6785
106	1.2798	0.2932	0.3727	0.4689	4378228.8387	9410289.717	3067.6196	0.4584	0.6235
107	1.2947	0.4909	0.3964	0.5052	23851718.7018	12298575.5139	3506.9325	1.07	0.7128
108	1.2851	0.6876	0.4449	0.5957	118325151.3407	29969671.4851	5474.4563	2.3832	0.9912
109	1.184	0.4869	0.4509	0.6025	16610740.213	28061252.7319	5297.2873	0.8929	0.9772
110	1.1787	0.6812	0.4797	0.6563	81123712.9974	34694060.2651	5890.1664	1.9733	1.1017
111	1.4291	0.6635	0.5001	0.6937	122944279.8371	44499640.2175	6670.8051	2.4293	1.2492
112	1.4403	0.7048	0.5206	0.7332	180444644.0173	58094140.5975	7621.9512	2.943	1.4186
113	1.3619	0.3842	0.5082	0.7098	10173947.5815	53737759.4142	7330.6043	0.6988	1.3531
114	1.4296	0.7381	0.5274	0.7481	235293127.2176	68867373.3979	8298.6368	3.3607	1.5204
115	1.3733	0.505	0.5256	0.7425	23693664.2549	65392472.6946	8086.5612	1.0664	1.4855
116	1.2856	0.6878	0.5372	0.7644	85492299.7672	66828174.6283	8174.8501	2.0257	1.5241
117	1.389	0.2343	0.517	0.7311	2024421.0698	62507924.3911	7906.1953	0.3117	1.4433
118	1.5685	0.5921	0.5217	0.738	63350380.5646	62560577.9019	7909.5245	1.7438	1.4621
119	1.5579	0.6296	0.5281	0.7486	82851823.6644	63754180.5938	7984.6215	1.9942	1.4934
120	1.5599	0.7013	0.5377	0.767	154180169.4615	68777846.642	8293.241	2.7204	1.5615
121	1.4285	0.7216	0.5474	0.7861	141122640.7214	72585467.3831	8519.7105	2.6027	1.6163
122	1.4158	0.7673	0.5584	0.809	216922604.622	79802324.245	8933.2147	3.2268	1.6969
123	1.738	0.7137	0.5658	0.8233	217982528.3879	86382333.9661	9294.2097	3.2347	1.7701
124	1.7867	0.5878	0.5668	0.8238	75047577.533	85867117.7646	9266.4512	1.898	1.7759
125	1.6795	0.5663	0.5667	0.8223	51733258.6099	84383036.9318	9186.024	1.5758	1.7672
126	1.7829	0.625	0.5692	0.8259	97021245.591	84909628.9592	9214.6421	2.158	1.7835

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 24 ; par2 = -0.3 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 1 ; par7 = 1 ; par8 = 1 ; par9 = 1 ; par10 = FALSE ;

Parameters (R input):

par1 = 24 ; par2 = -0.3 ; 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*2
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.spe <- array(0, dim=fx)
perf.scalederr <- array(0, dim=fx)
perf.mase <- array(0, dim=fx)
perf.mase1 <- array(0, dim=fx)
perf.mape <- array(0, dim=fx)
perf.smape <- array(0, dim=fx)
perf.mape1 <- array(0, dim=fx)
perf.smape1 <- 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)
perf.scaleddenom <- 0
for (i in 2:fx) {
perf.scaleddenom = perf.scaleddenom + abs(x[nx+i] - x[nx+i-1])
}
perf.scaleddenom = perf.scaleddenom / (fx-1)
for (i in 1:fx) {
locSD <- (ub[i] - forecast$pred[i]) / 1.96
perf.scalederr[i] = (x[nx+i] - forecast$pred[i]) / perf.scaleddenom
perf.pe[i] = (x[nx+i] - forecast$pred[i]) / x[nx+i]
perf.spe[i] = 2*(x[nx+i] - forecast$pred[i]) / (x[nx+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.smape[1] = abs(perf.spe[1])
perf.mape1[1] = perf.mape[1]
perf.smape1[1] = perf.smape[1]
perf.mse[1] = perf.se[1]
perf.mase[1] = abs(perf.scalederr[1])
perf.mase1[1] = perf.mase[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.smape[i] = perf.smape[i-1] + abs(perf.spe[i])
perf.smape1[i] = perf.smape[i] / i
perf.mse[i] = perf.mse[i-1] + perf.se[i]
perf.mse1[i] = perf.mse[i] / i
perf.mase[i] = perf.mase[i-1] + abs(perf.scalederr[i])
perf.mase1[i] = perf.mase[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',10,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,'sMAPE',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.element(a,'ScaledE',1,header=TRUE)
a<-table.element(a,'MASE',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.smape1[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.element(a,round(perf.scalederr[i],4))
a<-table.element(a,round(perf.mase1[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