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*Unverified author*

R Software Module

rwasp_arimaforecasting.wasp

Title produced by software

ARIMA Forecasting

Date of computation

Mon, 15 Dec 2008 13:58:10 -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/2008/Dec/15/t1229374760xtga7jav95oyzno.htm/, Retrieved Wed, 15 May 2024 07:30:03 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=33821, Retrieved Wed, 15 May 2024 07:30:03 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

231

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

F     [ARIMA Forecasting] [hfdst 21 arima fo...] [2008-12-15 08:32:33] [11edab5c4db3615abbf782b1c6e7cacf]
-   PD    [ARIMA Forecasting] [Gilliam Schoorel] [2008-12-15 20:58:10] [d41d8cd98f00b204e9800998ecf8427e] [Current]
F   P       [ARIMA Forecasting] [Gilliam Schoorel] [2008-12-16 11:15:34] [74be16979710d4c4e7c6647856088456] 
F             [ARIMA Forecasting] [Toon Wouters] [2008-12-16 21:16:16] [810fefdbb91d48e1fca60d884166311f] 
F R             [ARIMA Forecasting] [S�ren Van Donink] [2008-12-17 07:03:17] [74be16979710d4c4e7c6647856088456] 
F R             [ARIMA Forecasting] [S�ren Van Donink] [2008-12-17 07:04:55] [74be16979710d4c4e7c6647856088456] 
- R             [ARIMA Forecasting] [S�ren Van Donink] [2008-12-17 07:06:42] [74be16979710d4c4e7c6647856088456] 
- R             [ARIMA Forecasting] [S�ren Van Donink] [2008-12-17 07:08:22] [74be16979710d4c4e7c6647856088456] 
-             [ARIMA Forecasting] [Toon Wouters] [2008-12-19 07:54:53] [74be16979710d4c4e7c6647856088456] 
- R             [ARIMA Forecasting] [Paper - s0410061] [2008-12-23 20:33:40] [74be16979710d4c4e7c6647856088456] 
- RM            [ARIMA Forecasting] [Soren] [2009-12-21 07:40:46] [d70851d7a1b5fbddaadf8fdd99e807cd] 
-   PD      [ARIMA Forecasting] [Gilliam Schoorel ...] [2008-12-16 11:51:33] [74be16979710d4c4e7c6647856088456] 
-             [ARIMA Forecasting] [Toon Wouters] [2008-12-16 17:15:29] [74be16979710d4c4e7c6647856088456] 
F             [ARIMA Forecasting] [] [2008-12-16 22:48:01] [74be16979710d4c4e7c6647856088456] 
-   P         [ARIMA Forecasting] [Gilliam Schoorel] [2008-12-18 18:40:57] [74be16979710d4c4e7c6647856088456] 
-               [ARIMA Forecasting] [Toon Wouters] [2008-12-19 07:57:41] [74be16979710d4c4e7c6647856088456] 
- R               [ARIMA Forecasting] [Paper - s0410061] [2008-12-23 20:34:30] [74be16979710d4c4e7c6647856088456] 
- R P             [ARIMA Forecasting] [S�ren Van Donink ...] [2008-12-24 12:24:49] [74be16979710d4c4e7c6647856088456] 
- RM              [ARIMA Forecasting] [S�ren Van Donink ...] [2009-12-21 08:49:19] [d70851d7a1b5fbddaadf8fdd99e807cd] 

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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	'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33821&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33821&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33821&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	'George Udny Yule' @ 72.249.76.132

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[34])
22	519	-	-	-	-	-	-	-
23	509	-	-	-	-	-	-	-
24	512	-	-	-	-	-	-	-
25	519	-	-	-	-	-	-	-
26	517	-	-	-	-	-	-	-
27	510	-	-	-	-	-	-	-
28	509	-	-	-	-	-	-	-
29	501	-	-	-	-	-	-	-
30	507	-	-	-	-	-	-	-
31	569	-	-	-	-	-	-	-
32	580	-	-	-	-	-	-	-
33	578	-	-	-	-	-	-	-
34	565	-	-	-	-	-	-	-
35	547	556.1203	544.0685	568.1721	0.069	0.0744	1	0.0744
36	555	560.26	540.687	579.8329	0.2992	0.9079	1	0.3175
37	562	568.4567	543.185	593.7284	0.3083	0.8517	0.9999	0.6057
38	561	564.5419	537.4849	591.5988	0.3988	0.573	0.9997	0.4868
39	555	557.6536	527.8601	587.4472	0.4307	0.4129	0.9991	0.3144
40	544	557.5268	524.2789	590.7747	0.2126	0.5592	0.9979	0.3298
41	537	549.4481	513.6652	585.2311	0.2477	0.6173	0.996	0.1971
42	543	554.8696	517.1801	592.5591	0.2685	0.8236	0.9936	0.2992
43	594	617.0445	577.1374	656.9516	0.1289	0.9999	0.9909	0.9947
44	611	628.3475	586.1611	670.5339	0.2101	0.9447	0.9877	0.9984
45	613	626.2011	582.1011	670.3011	0.2787	0.7504	0.9839	0.9967
46	611	613.0385	567.1654	658.9115	0.4653	0.5007	0.9799	0.9799
47	594	604.2755	551.9209	656.6302	0.3502	0.4006	0.984	0.9293
48	595	608.493	548.8184	668.1675	0.3288	0.683	0.9605	0.9234
49	591	616.6073	550.3167	682.8979	0.2245	0.7385	0.9468	0.9365
50	589	612.6588	542.6291	682.6885	0.2539	0.7278	0.9259	0.9089
51	584	605.8258	531.2705	680.381	0.2831	0.6709	0.9093	0.8584
52	573	605.7101	526.1358	685.2845	0.2102	0.7036	0.9357	0.842
53	567	597.5964	513.8274	681.3655	0.237	0.7175	0.9219	0.7772
54	569	603.0169	515.6452	690.3886	0.2227	0.7904	0.9109	0.8031
55	621	665.213	574.006	756.4201	0.171	0.9807	0.937	0.9844
56	629	676.5132	581.4801	771.5462	0.1636	0.8739	0.9117	0.9893
57	628	674.3545	575.872	772.8369	0.1781	0.8166	0.889	0.9852
58	612	661.1955	559.4291	762.962	0.1717	0.7387	0.8332	0.968
59	595	652.4394	543.7183	761.1606	0.1502	0.767	0.854	0.9425
60	597	656.6535	540.2293	773.0776	0.1576	0.8504	0.8504	0.9386
61	593	664.7642	541.0394	788.489	0.1278	0.8585	0.8787	0.943
62	590	660.8184	532.0758	789.5609	0.1405	0.8491	0.8629	0.9277
63	580	653.9871	519.5837	788.3905	0.1403	0.8246	0.8463	0.9028
64	574	653.8696	513.4035	794.3357	0.1325	0.8487	0.8704	0.8925
65	573	645.7551	499.9257	791.5845	0.1641	0.8326	0.8551	0.8611
66	573	651.1768	500.5169	801.8367	0.1546	0.8454	0.8575	0.8689
67	620	713.3732	557.7013	869.0451	0.1199	0.9614	0.8776	0.9691
68	626	724.6726	564.0172	885.3279	0.1143	0.8992	0.8784	0.9743
69	620	722.5138	557.2285	887.7992	0.1121	0.8738	0.8688	0.9691
70	588	709.3554	539.6097	879.1011	0.0806	0.8489	0.8695	0.9522
71	566	700.5992	523.2788	877.9197	0.0684	0.8934	0.8784	0.933
72	557	704.813	519.2603	890.3658	0.0592	0.9287	0.8726	0.9301
73	561	712.9239	519.4245	906.4232	0.0619	0.9429	0.8878	0.933
74	549	708.9781	509.4677	908.4885	0.058	0.927	0.8788	0.9214
75	532	702.1468	496.0698	908.2237	0.0528	0.9274	0.8773	0.904
76	526	702.0292	489.0368	915.0216	0.0526	0.9412	0.8806	0.8963
77	511	693.9148	474.6198	913.2098	0.051	0.9333	0.8601	0.8754
78	499	699.3365	474.2218	924.4512	0.0406	0.9495	0.8643	0.8789
79	555	761.5329	530.4465	992.6193	0.0399	0.987	0.885	0.9522
80	565	772.8322	535.8093	1009.8551	0.0428	0.9642	0.8877	0.9572
81	542	770.6735	528.0438	1013.3032	0.0324	0.9517	0.8882	0.9517
82	527	757.5151	509.445	1005.5852	0.0343	0.9557	0.9098	0.9359
83	510	748.7589	492.4946	1005.0232	0.0339	0.9551	0.9189	0.9201
84	514	752.9727	487.9249	1018.0204	0.0386	0.9638	0.9264	0.9177

\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[34]) \tabularnewline
22 & 519 & - & - & - & - & - & - & - \tabularnewline
23 & 509 & - & - & - & - & - & - & - \tabularnewline
24 & 512 & - & - & - & - & - & - & - \tabularnewline
25 & 519 & - & - & - & - & - & - & - \tabularnewline
26 & 517 & - & - & - & - & - & - & - \tabularnewline
27 & 510 & - & - & - & - & - & - & - \tabularnewline
28 & 509 & - & - & - & - & - & - & - \tabularnewline
29 & 501 & - & - & - & - & - & - & - \tabularnewline
30 & 507 & - & - & - & - & - & - & - \tabularnewline
31 & 569 & - & - & - & - & - & - & - \tabularnewline
32 & 580 & - & - & - & - & - & - & - \tabularnewline
33 & 578 & - & - & - & - & - & - & - \tabularnewline
34 & 565 & - & - & - & - & - & - & - \tabularnewline
35 & 547 & 556.1203 & 544.0685 & 568.1721 & 0.069 & 0.0744 & 1 & 0.0744 \tabularnewline
36 & 555 & 560.26 & 540.687 & 579.8329 & 0.2992 & 0.9079 & 1 & 0.3175 \tabularnewline
37 & 562 & 568.4567 & 543.185 & 593.7284 & 0.3083 & 0.8517 & 0.9999 & 0.6057 \tabularnewline
38 & 561 & 564.5419 & 537.4849 & 591.5988 & 0.3988 & 0.573 & 0.9997 & 0.4868 \tabularnewline
39 & 555 & 557.6536 & 527.8601 & 587.4472 & 0.4307 & 0.4129 & 0.9991 & 0.3144 \tabularnewline
40 & 544 & 557.5268 & 524.2789 & 590.7747 & 0.2126 & 0.5592 & 0.9979 & 0.3298 \tabularnewline
41 & 537 & 549.4481 & 513.6652 & 585.2311 & 0.2477 & 0.6173 & 0.996 & 0.1971 \tabularnewline
42 & 543 & 554.8696 & 517.1801 & 592.5591 & 0.2685 & 0.8236 & 0.9936 & 0.2992 \tabularnewline
43 & 594 & 617.0445 & 577.1374 & 656.9516 & 0.1289 & 0.9999 & 0.9909 & 0.9947 \tabularnewline
44 & 611 & 628.3475 & 586.1611 & 670.5339 & 0.2101 & 0.9447 & 0.9877 & 0.9984 \tabularnewline
45 & 613 & 626.2011 & 582.1011 & 670.3011 & 0.2787 & 0.7504 & 0.9839 & 0.9967 \tabularnewline
46 & 611 & 613.0385 & 567.1654 & 658.9115 & 0.4653 & 0.5007 & 0.9799 & 0.9799 \tabularnewline
47 & 594 & 604.2755 & 551.9209 & 656.6302 & 0.3502 & 0.4006 & 0.984 & 0.9293 \tabularnewline
48 & 595 & 608.493 & 548.8184 & 668.1675 & 0.3288 & 0.683 & 0.9605 & 0.9234 \tabularnewline
49 & 591 & 616.6073 & 550.3167 & 682.8979 & 0.2245 & 0.7385 & 0.9468 & 0.9365 \tabularnewline
50 & 589 & 612.6588 & 542.6291 & 682.6885 & 0.2539 & 0.7278 & 0.9259 & 0.9089 \tabularnewline
51 & 584 & 605.8258 & 531.2705 & 680.381 & 0.2831 & 0.6709 & 0.9093 & 0.8584 \tabularnewline
52 & 573 & 605.7101 & 526.1358 & 685.2845 & 0.2102 & 0.7036 & 0.9357 & 0.842 \tabularnewline
53 & 567 & 597.5964 & 513.8274 & 681.3655 & 0.237 & 0.7175 & 0.9219 & 0.7772 \tabularnewline
54 & 569 & 603.0169 & 515.6452 & 690.3886 & 0.2227 & 0.7904 & 0.9109 & 0.8031 \tabularnewline
55 & 621 & 665.213 & 574.006 & 756.4201 & 0.171 & 0.9807 & 0.937 & 0.9844 \tabularnewline
56 & 629 & 676.5132 & 581.4801 & 771.5462 & 0.1636 & 0.8739 & 0.9117 & 0.9893 \tabularnewline
57 & 628 & 674.3545 & 575.872 & 772.8369 & 0.1781 & 0.8166 & 0.889 & 0.9852 \tabularnewline
58 & 612 & 661.1955 & 559.4291 & 762.962 & 0.1717 & 0.7387 & 0.8332 & 0.968 \tabularnewline
59 & 595 & 652.4394 & 543.7183 & 761.1606 & 0.1502 & 0.767 & 0.854 & 0.9425 \tabularnewline
60 & 597 & 656.6535 & 540.2293 & 773.0776 & 0.1576 & 0.8504 & 0.8504 & 0.9386 \tabularnewline
61 & 593 & 664.7642 & 541.0394 & 788.489 & 0.1278 & 0.8585 & 0.8787 & 0.943 \tabularnewline
62 & 590 & 660.8184 & 532.0758 & 789.5609 & 0.1405 & 0.8491 & 0.8629 & 0.9277 \tabularnewline
63 & 580 & 653.9871 & 519.5837 & 788.3905 & 0.1403 & 0.8246 & 0.8463 & 0.9028 \tabularnewline
64 & 574 & 653.8696 & 513.4035 & 794.3357 & 0.1325 & 0.8487 & 0.8704 & 0.8925 \tabularnewline
65 & 573 & 645.7551 & 499.9257 & 791.5845 & 0.1641 & 0.8326 & 0.8551 & 0.8611 \tabularnewline
66 & 573 & 651.1768 & 500.5169 & 801.8367 & 0.1546 & 0.8454 & 0.8575 & 0.8689 \tabularnewline
67 & 620 & 713.3732 & 557.7013 & 869.0451 & 0.1199 & 0.9614 & 0.8776 & 0.9691 \tabularnewline
68 & 626 & 724.6726 & 564.0172 & 885.3279 & 0.1143 & 0.8992 & 0.8784 & 0.9743 \tabularnewline
69 & 620 & 722.5138 & 557.2285 & 887.7992 & 0.1121 & 0.8738 & 0.8688 & 0.9691 \tabularnewline
70 & 588 & 709.3554 & 539.6097 & 879.1011 & 0.0806 & 0.8489 & 0.8695 & 0.9522 \tabularnewline
71 & 566 & 700.5992 & 523.2788 & 877.9197 & 0.0684 & 0.8934 & 0.8784 & 0.933 \tabularnewline
72 & 557 & 704.813 & 519.2603 & 890.3658 & 0.0592 & 0.9287 & 0.8726 & 0.9301 \tabularnewline
73 & 561 & 712.9239 & 519.4245 & 906.4232 & 0.0619 & 0.9429 & 0.8878 & 0.933 \tabularnewline
74 & 549 & 708.9781 & 509.4677 & 908.4885 & 0.058 & 0.927 & 0.8788 & 0.9214 \tabularnewline
75 & 532 & 702.1468 & 496.0698 & 908.2237 & 0.0528 & 0.9274 & 0.8773 & 0.904 \tabularnewline
76 & 526 & 702.0292 & 489.0368 & 915.0216 & 0.0526 & 0.9412 & 0.8806 & 0.8963 \tabularnewline
77 & 511 & 693.9148 & 474.6198 & 913.2098 & 0.051 & 0.9333 & 0.8601 & 0.8754 \tabularnewline
78 & 499 & 699.3365 & 474.2218 & 924.4512 & 0.0406 & 0.9495 & 0.8643 & 0.8789 \tabularnewline
79 & 555 & 761.5329 & 530.4465 & 992.6193 & 0.0399 & 0.987 & 0.885 & 0.9522 \tabularnewline
80 & 565 & 772.8322 & 535.8093 & 1009.8551 & 0.0428 & 0.9642 & 0.8877 & 0.9572 \tabularnewline
81 & 542 & 770.6735 & 528.0438 & 1013.3032 & 0.0324 & 0.9517 & 0.8882 & 0.9517 \tabularnewline
82 & 527 & 757.5151 & 509.445 & 1005.5852 & 0.0343 & 0.9557 & 0.9098 & 0.9359 \tabularnewline
83 & 510 & 748.7589 & 492.4946 & 1005.0232 & 0.0339 & 0.9551 & 0.9189 & 0.9201 \tabularnewline
84 & 514 & 752.9727 & 487.9249 & 1018.0204 & 0.0386 & 0.9638 & 0.9264 & 0.9177 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33821&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[34])[/C][/ROW]
[ROW][C]22[/C][C]519[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]23[/C][C]509[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]24[/C][C]512[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]25[/C][C]519[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]26[/C][C]517[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]27[/C][C]510[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]28[/C][C]509[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]29[/C][C]501[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]30[/C][C]507[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]31[/C][C]569[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]32[/C][C]580[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]33[/C][C]578[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]34[/C][C]565[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]35[/C][C]547[/C][C]556.1203[/C][C]544.0685[/C][C]568.1721[/C][C]0.069[/C][C]0.0744[/C][C]1[/C][C]0.0744[/C][/ROW]
[ROW][C]36[/C][C]555[/C][C]560.26[/C][C]540.687[/C][C]579.8329[/C][C]0.2992[/C][C]0.9079[/C][C]1[/C][C]0.3175[/C][/ROW]
[ROW][C]37[/C][C]562[/C][C]568.4567[/C][C]543.185[/C][C]593.7284[/C][C]0.3083[/C][C]0.8517[/C][C]0.9999[/C][C]0.6057[/C][/ROW]
[ROW][C]38[/C][C]561[/C][C]564.5419[/C][C]537.4849[/C][C]591.5988[/C][C]0.3988[/C][C]0.573[/C][C]0.9997[/C][C]0.4868[/C][/ROW]
[ROW][C]39[/C][C]555[/C][C]557.6536[/C][C]527.8601[/C][C]587.4472[/C][C]0.4307[/C][C]0.4129[/C][C]0.9991[/C][C]0.3144[/C][/ROW]
[ROW][C]40[/C][C]544[/C][C]557.5268[/C][C]524.2789[/C][C]590.7747[/C][C]0.2126[/C][C]0.5592[/C][C]0.9979[/C][C]0.3298[/C][/ROW]
[ROW][C]41[/C][C]537[/C][C]549.4481[/C][C]513.6652[/C][C]585.2311[/C][C]0.2477[/C][C]0.6173[/C][C]0.996[/C][C]0.1971[/C][/ROW]
[ROW][C]42[/C][C]543[/C][C]554.8696[/C][C]517.1801[/C][C]592.5591[/C][C]0.2685[/C][C]0.8236[/C][C]0.9936[/C][C]0.2992[/C][/ROW]
[ROW][C]43[/C][C]594[/C][C]617.0445[/C][C]577.1374[/C][C]656.9516[/C][C]0.1289[/C][C]0.9999[/C][C]0.9909[/C][C]0.9947[/C][/ROW]
[ROW][C]44[/C][C]611[/C][C]628.3475[/C][C]586.1611[/C][C]670.5339[/C][C]0.2101[/C][C]0.9447[/C][C]0.9877[/C][C]0.9984[/C][/ROW]
[ROW][C]45[/C][C]613[/C][C]626.2011[/C][C]582.1011[/C][C]670.3011[/C][C]0.2787[/C][C]0.7504[/C][C]0.9839[/C][C]0.9967[/C][/ROW]
[ROW][C]46[/C][C]611[/C][C]613.0385[/C][C]567.1654[/C][C]658.9115[/C][C]0.4653[/C][C]0.5007[/C][C]0.9799[/C][C]0.9799[/C][/ROW]
[ROW][C]47[/C][C]594[/C][C]604.2755[/C][C]551.9209[/C][C]656.6302[/C][C]0.3502[/C][C]0.4006[/C][C]0.984[/C][C]0.9293[/C][/ROW]
[ROW][C]48[/C][C]595[/C][C]608.493[/C][C]548.8184[/C][C]668.1675[/C][C]0.3288[/C][C]0.683[/C][C]0.9605[/C][C]0.9234[/C][/ROW]
[ROW][C]49[/C][C]591[/C][C]616.6073[/C][C]550.3167[/C][C]682.8979[/C][C]0.2245[/C][C]0.7385[/C][C]0.9468[/C][C]0.9365[/C][/ROW]
[ROW][C]50[/C][C]589[/C][C]612.6588[/C][C]542.6291[/C][C]682.6885[/C][C]0.2539[/C][C]0.7278[/C][C]0.9259[/C][C]0.9089[/C][/ROW]
[ROW][C]51[/C][C]584[/C][C]605.8258[/C][C]531.2705[/C][C]680.381[/C][C]0.2831[/C][C]0.6709[/C][C]0.9093[/C][C]0.8584[/C][/ROW]
[ROW][C]52[/C][C]573[/C][C]605.7101[/C][C]526.1358[/C][C]685.2845[/C][C]0.2102[/C][C]0.7036[/C][C]0.9357[/C][C]0.842[/C][/ROW]
[ROW][C]53[/C][C]567[/C][C]597.5964[/C][C]513.8274[/C][C]681.3655[/C][C]0.237[/C][C]0.7175[/C][C]0.9219[/C][C]0.7772[/C][/ROW]
[ROW][C]54[/C][C]569[/C][C]603.0169[/C][C]515.6452[/C][C]690.3886[/C][C]0.2227[/C][C]0.7904[/C][C]0.9109[/C][C]0.8031[/C][/ROW]
[ROW][C]55[/C][C]621[/C][C]665.213[/C][C]574.006[/C][C]756.4201[/C][C]0.171[/C][C]0.9807[/C][C]0.937[/C][C]0.9844[/C][/ROW]
[ROW][C]56[/C][C]629[/C][C]676.5132[/C][C]581.4801[/C][C]771.5462[/C][C]0.1636[/C][C]0.8739[/C][C]0.9117[/C][C]0.9893[/C][/ROW]
[ROW][C]57[/C][C]628[/C][C]674.3545[/C][C]575.872[/C][C]772.8369[/C][C]0.1781[/C][C]0.8166[/C][C]0.889[/C][C]0.9852[/C][/ROW]
[ROW][C]58[/C][C]612[/C][C]661.1955[/C][C]559.4291[/C][C]762.962[/C][C]0.1717[/C][C]0.7387[/C][C]0.8332[/C][C]0.968[/C][/ROW]
[ROW][C]59[/C][C]595[/C][C]652.4394[/C][C]543.7183[/C][C]761.1606[/C][C]0.1502[/C][C]0.767[/C][C]0.854[/C][C]0.9425[/C][/ROW]
[ROW][C]60[/C][C]597[/C][C]656.6535[/C][C]540.2293[/C][C]773.0776[/C][C]0.1576[/C][C]0.8504[/C][C]0.8504[/C][C]0.9386[/C][/ROW]
[ROW][C]61[/C][C]593[/C][C]664.7642[/C][C]541.0394[/C][C]788.489[/C][C]0.1278[/C][C]0.8585[/C][C]0.8787[/C][C]0.943[/C][/ROW]
[ROW][C]62[/C][C]590[/C][C]660.8184[/C][C]532.0758[/C][C]789.5609[/C][C]0.1405[/C][C]0.8491[/C][C]0.8629[/C][C]0.9277[/C][/ROW]
[ROW][C]63[/C][C]580[/C][C]653.9871[/C][C]519.5837[/C][C]788.3905[/C][C]0.1403[/C][C]0.8246[/C][C]0.8463[/C][C]0.9028[/C][/ROW]
[ROW][C]64[/C][C]574[/C][C]653.8696[/C][C]513.4035[/C][C]794.3357[/C][C]0.1325[/C][C]0.8487[/C][C]0.8704[/C][C]0.8925[/C][/ROW]
[ROW][C]65[/C][C]573[/C][C]645.7551[/C][C]499.9257[/C][C]791.5845[/C][C]0.1641[/C][C]0.8326[/C][C]0.8551[/C][C]0.8611[/C][/ROW]
[ROW][C]66[/C][C]573[/C][C]651.1768[/C][C]500.5169[/C][C]801.8367[/C][C]0.1546[/C][C]0.8454[/C][C]0.8575[/C][C]0.8689[/C][/ROW]
[ROW][C]67[/C][C]620[/C][C]713.3732[/C][C]557.7013[/C][C]869.0451[/C][C]0.1199[/C][C]0.9614[/C][C]0.8776[/C][C]0.9691[/C][/ROW]
[ROW][C]68[/C][C]626[/C][C]724.6726[/C][C]564.0172[/C][C]885.3279[/C][C]0.1143[/C][C]0.8992[/C][C]0.8784[/C][C]0.9743[/C][/ROW]
[ROW][C]69[/C][C]620[/C][C]722.5138[/C][C]557.2285[/C][C]887.7992[/C][C]0.1121[/C][C]0.8738[/C][C]0.8688[/C][C]0.9691[/C][/ROW]
[ROW][C]70[/C][C]588[/C][C]709.3554[/C][C]539.6097[/C][C]879.1011[/C][C]0.0806[/C][C]0.8489[/C][C]0.8695[/C][C]0.9522[/C][/ROW]
[ROW][C]71[/C][C]566[/C][C]700.5992[/C][C]523.2788[/C][C]877.9197[/C][C]0.0684[/C][C]0.8934[/C][C]0.8784[/C][C]0.933[/C][/ROW]
[ROW][C]72[/C][C]557[/C][C]704.813[/C][C]519.2603[/C][C]890.3658[/C][C]0.0592[/C][C]0.9287[/C][C]0.8726[/C][C]0.9301[/C][/ROW]
[ROW][C]73[/C][C]561[/C][C]712.9239[/C][C]519.4245[/C][C]906.4232[/C][C]0.0619[/C][C]0.9429[/C][C]0.8878[/C][C]0.933[/C][/ROW]
[ROW][C]74[/C][C]549[/C][C]708.9781[/C][C]509.4677[/C][C]908.4885[/C][C]0.058[/C][C]0.927[/C][C]0.8788[/C][C]0.9214[/C][/ROW]
[ROW][C]75[/C][C]532[/C][C]702.1468[/C][C]496.0698[/C][C]908.2237[/C][C]0.0528[/C][C]0.9274[/C][C]0.8773[/C][C]0.904[/C][/ROW]
[ROW][C]76[/C][C]526[/C][C]702.0292[/C][C]489.0368[/C][C]915.0216[/C][C]0.0526[/C][C]0.9412[/C][C]0.8806[/C][C]0.8963[/C][/ROW]
[ROW][C]77[/C][C]511[/C][C]693.9148[/C][C]474.6198[/C][C]913.2098[/C][C]0.051[/C][C]0.9333[/C][C]0.8601[/C][C]0.8754[/C][/ROW]
[ROW][C]78[/C][C]499[/C][C]699.3365[/C][C]474.2218[/C][C]924.4512[/C][C]0.0406[/C][C]0.9495[/C][C]0.8643[/C][C]0.8789[/C][/ROW]
[ROW][C]79[/C][C]555[/C][C]761.5329[/C][C]530.4465[/C][C]992.6193[/C][C]0.0399[/C][C]0.987[/C][C]0.885[/C][C]0.9522[/C][/ROW]
[ROW][C]80[/C][C]565[/C][C]772.8322[/C][C]535.8093[/C][C]1009.8551[/C][C]0.0428[/C][C]0.9642[/C][C]0.8877[/C][C]0.9572[/C][/ROW]
[ROW][C]81[/C][C]542[/C][C]770.6735[/C][C]528.0438[/C][C]1013.3032[/C][C]0.0324[/C][C]0.9517[/C][C]0.8882[/C][C]0.9517[/C][/ROW]
[ROW][C]82[/C][C]527[/C][C]757.5151[/C][C]509.445[/C][C]1005.5852[/C][C]0.0343[/C][C]0.9557[/C][C]0.9098[/C][C]0.9359[/C][/ROW]
[ROW][C]83[/C][C]510[/C][C]748.7589[/C][C]492.4946[/C][C]1005.0232[/C][C]0.0339[/C][C]0.9551[/C][C]0.9189[/C][C]0.9201[/C][/ROW]
[ROW][C]84[/C][C]514[/C][C]752.9727[/C][C]487.9249[/C][C]1018.0204[/C][C]0.0386[/C][C]0.9638[/C][C]0.9264[/C][C]0.9177[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33821&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33821&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[34])
22	519	-	-	-	-	-	-	-
23	509	-	-	-	-	-	-	-
24	512	-	-	-	-	-	-	-
25	519	-	-	-	-	-	-	-
26	517	-	-	-	-	-	-	-
27	510	-	-	-	-	-	-	-
28	509	-	-	-	-	-	-	-
29	501	-	-	-	-	-	-	-
30	507	-	-	-	-	-	-	-
31	569	-	-	-	-	-	-	-
32	580	-	-	-	-	-	-	-
33	578	-	-	-	-	-	-	-
34	565	-	-	-	-	-	-	-
35	547	556.1203	544.0685	568.1721	0.069	0.0744	1	0.0744
36	555	560.26	540.687	579.8329	0.2992	0.9079	1	0.3175
37	562	568.4567	543.185	593.7284	0.3083	0.8517	0.9999	0.6057
38	561	564.5419	537.4849	591.5988	0.3988	0.573	0.9997	0.4868
39	555	557.6536	527.8601	587.4472	0.4307	0.4129	0.9991	0.3144
40	544	557.5268	524.2789	590.7747	0.2126	0.5592	0.9979	0.3298
41	537	549.4481	513.6652	585.2311	0.2477	0.6173	0.996	0.1971
42	543	554.8696	517.1801	592.5591	0.2685	0.8236	0.9936	0.2992
43	594	617.0445	577.1374	656.9516	0.1289	0.9999	0.9909	0.9947
44	611	628.3475	586.1611	670.5339	0.2101	0.9447	0.9877	0.9984
45	613	626.2011	582.1011	670.3011	0.2787	0.7504	0.9839	0.9967
46	611	613.0385	567.1654	658.9115	0.4653	0.5007	0.9799	0.9799
47	594	604.2755	551.9209	656.6302	0.3502	0.4006	0.984	0.9293
48	595	608.493	548.8184	668.1675	0.3288	0.683	0.9605	0.9234
49	591	616.6073	550.3167	682.8979	0.2245	0.7385	0.9468	0.9365
50	589	612.6588	542.6291	682.6885	0.2539	0.7278	0.9259	0.9089
51	584	605.8258	531.2705	680.381	0.2831	0.6709	0.9093	0.8584
52	573	605.7101	526.1358	685.2845	0.2102	0.7036	0.9357	0.842
53	567	597.5964	513.8274	681.3655	0.237	0.7175	0.9219	0.7772
54	569	603.0169	515.6452	690.3886	0.2227	0.7904	0.9109	0.8031
55	621	665.213	574.006	756.4201	0.171	0.9807	0.937	0.9844
56	629	676.5132	581.4801	771.5462	0.1636	0.8739	0.9117	0.9893
57	628	674.3545	575.872	772.8369	0.1781	0.8166	0.889	0.9852
58	612	661.1955	559.4291	762.962	0.1717	0.7387	0.8332	0.968
59	595	652.4394	543.7183	761.1606	0.1502	0.767	0.854	0.9425
60	597	656.6535	540.2293	773.0776	0.1576	0.8504	0.8504	0.9386
61	593	664.7642	541.0394	788.489	0.1278	0.8585	0.8787	0.943
62	590	660.8184	532.0758	789.5609	0.1405	0.8491	0.8629	0.9277
63	580	653.9871	519.5837	788.3905	0.1403	0.8246	0.8463	0.9028
64	574	653.8696	513.4035	794.3357	0.1325	0.8487	0.8704	0.8925
65	573	645.7551	499.9257	791.5845	0.1641	0.8326	0.8551	0.8611
66	573	651.1768	500.5169	801.8367	0.1546	0.8454	0.8575	0.8689
67	620	713.3732	557.7013	869.0451	0.1199	0.9614	0.8776	0.9691
68	626	724.6726	564.0172	885.3279	0.1143	0.8992	0.8784	0.9743
69	620	722.5138	557.2285	887.7992	0.1121	0.8738	0.8688	0.9691
70	588	709.3554	539.6097	879.1011	0.0806	0.8489	0.8695	0.9522
71	566	700.5992	523.2788	877.9197	0.0684	0.8934	0.8784	0.933
72	557	704.813	519.2603	890.3658	0.0592	0.9287	0.8726	0.9301
73	561	712.9239	519.4245	906.4232	0.0619	0.9429	0.8878	0.933
74	549	708.9781	509.4677	908.4885	0.058	0.927	0.8788	0.9214
75	532	702.1468	496.0698	908.2237	0.0528	0.9274	0.8773	0.904
76	526	702.0292	489.0368	915.0216	0.0526	0.9412	0.8806	0.8963
77	511	693.9148	474.6198	913.2098	0.051	0.9333	0.8601	0.8754
78	499	699.3365	474.2218	924.4512	0.0406	0.9495	0.8643	0.8789
79	555	761.5329	530.4465	992.6193	0.0399	0.987	0.885	0.9522
80	565	772.8322	535.8093	1009.8551	0.0428	0.9642	0.8877	0.9572
81	542	770.6735	528.0438	1013.3032	0.0324	0.9517	0.8882	0.9517
82	527	757.5151	509.445	1005.5852	0.0343	0.9557	0.9098	0.9359
83	510	748.7589	492.4946	1005.0232	0.0339	0.9551	0.9189	0.9201
84	514	752.9727	487.9249	1018.0204	0.0386	0.9638	0.9264	0.9177

Univariate ARIMA Extrapolation Forecast Performance
time	% S.E.	PE	MAPE	Sq.E	MSE	RMSE
35	0.0111	-0.0164	3e-04	83.1804	1.6636	1.2898
36	0.0178	-0.0094	2e-04	27.6674	0.5533	0.7439
37	0.0227	-0.0114	2e-04	41.6892	0.8338	0.9131
38	0.0245	-0.0063	1e-04	12.5448	0.2509	0.5009
39	0.0273	-0.0048	1e-04	7.0417	0.1408	0.3753
40	0.0304	-0.0243	5e-04	182.9748	3.6595	1.913
41	0.0332	-0.0227	5e-04	154.9557	3.0991	1.7604
42	0.0347	-0.0214	4e-04	140.8867	2.8177	1.6786
43	0.033	-0.0373	7e-04	531.0488	10.621	3.259
44	0.0343	-0.0276	6e-04	300.9353	6.0187	2.4533
45	0.0359	-0.0211	4e-04	174.2692	3.4854	1.8669
46	0.0382	-0.0033	1e-04	4.1553	0.0831	0.2883
47	0.0442	-0.017	3e-04	105.5869	2.1117	1.4532
48	0.05	-0.0222	4e-04	182.06	3.6412	1.9082
49	0.0549	-0.0415	8e-04	655.7319	13.1146	3.6214
50	0.0583	-0.0386	8e-04	559.7388	11.1948	3.3459
51	0.0628	-0.036	7e-04	476.3651	9.5273	3.0866
52	0.067	-0.054	0.0011	1069.9525	21.3991	4.6259
53	0.0715	-0.0512	0.001	936.1402	18.7228	4.327
54	0.0739	-0.0564	0.0011	1157.1464	23.1429	4.8107
55	0.07	-0.0665	0.0013	1954.792	39.0958	6.2527
56	0.0717	-0.0702	0.0014	2257.4995	45.15	6.7194
57	0.0745	-0.0687	0.0014	2148.7351	42.9747	6.5555
58	0.0785	-0.0744	0.0015	2420.2011	48.404	6.9573
59	0.085	-0.088	0.0018	3299.2888	65.9858	8.1232
60	0.0905	-0.0908	0.0018	3558.5384	71.1708	8.4363
61	0.095	-0.108	0.0022	5150.1066	103.0021	10.149
62	0.0994	-0.1072	0.0021	5015.2432	100.3049	10.0152
63	0.1049	-0.1131	0.0023	5474.0881	109.4818	10.4634
64	0.1096	-0.1221	0.0024	6379.1491	127.583	11.2953
65	0.1152	-0.1127	0.0023	5293.3086	105.8662	10.2891
66	0.118	-0.1201	0.0024	6111.6125	122.2323	11.0559
67	0.1113	-0.1309	0.0026	8718.5572	174.3711	13.205
68	0.1131	-0.1362	0.0027	9736.2737	194.7255	13.9544
69	0.1167	-0.1419	0.0028	10509.0877	210.1818	14.4976
70	0.1221	-0.1711	0.0034	14727.1334	294.5427	17.1622
71	0.1291	-0.1921	0.0038	18116.9528	362.3391	19.0352
72	0.1343	-0.2097	0.0042	21848.6855	436.9737	20.9039
73	0.1385	-0.2131	0.0043	23080.8563	461.6171	21.4853
74	0.1436	-0.2256	0.0045	25593.005	511.8601	22.6243
75	0.1497	-0.2423	0.0048	28949.9206	578.9984	24.0624
76	0.1548	-0.2507	0.005	30986.272	619.7254	24.8943
77	0.1612	-0.2636	0.0053	33457.8203	669.1564	25.8681
78	0.1642	-0.2865	0.0057	40134.7141	802.6943	28.3319
79	0.1548	-0.2712	0.0054	42655.8276	853.1166	29.2082
80	0.1565	-0.2689	0.0054	43194.2236	863.8845	29.3919
81	0.1606	-0.2967	0.0059	52291.5748	1045.8315	32.3393
82	0.1671	-0.3043	0.0061	53137.2003	1062.744	32.5998
83	0.1746	-0.3189	0.0064	57005.8066	1140.1161	33.7656
84	0.1796	-0.3174	0.0063	57107.9351	1142.1587	33.7958

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
35 & 0.0111 & -0.0164 & 3e-04 & 83.1804 & 1.6636 & 1.2898 \tabularnewline
36 & 0.0178 & -0.0094 & 2e-04 & 27.6674 & 0.5533 & 0.7439 \tabularnewline
37 & 0.0227 & -0.0114 & 2e-04 & 41.6892 & 0.8338 & 0.9131 \tabularnewline
38 & 0.0245 & -0.0063 & 1e-04 & 12.5448 & 0.2509 & 0.5009 \tabularnewline
39 & 0.0273 & -0.0048 & 1e-04 & 7.0417 & 0.1408 & 0.3753 \tabularnewline
40 & 0.0304 & -0.0243 & 5e-04 & 182.9748 & 3.6595 & 1.913 \tabularnewline
41 & 0.0332 & -0.0227 & 5e-04 & 154.9557 & 3.0991 & 1.7604 \tabularnewline
42 & 0.0347 & -0.0214 & 4e-04 & 140.8867 & 2.8177 & 1.6786 \tabularnewline
43 & 0.033 & -0.0373 & 7e-04 & 531.0488 & 10.621 & 3.259 \tabularnewline
44 & 0.0343 & -0.0276 & 6e-04 & 300.9353 & 6.0187 & 2.4533 \tabularnewline
45 & 0.0359 & -0.0211 & 4e-04 & 174.2692 & 3.4854 & 1.8669 \tabularnewline
46 & 0.0382 & -0.0033 & 1e-04 & 4.1553 & 0.0831 & 0.2883 \tabularnewline
47 & 0.0442 & -0.017 & 3e-04 & 105.5869 & 2.1117 & 1.4532 \tabularnewline
48 & 0.05 & -0.0222 & 4e-04 & 182.06 & 3.6412 & 1.9082 \tabularnewline
49 & 0.0549 & -0.0415 & 8e-04 & 655.7319 & 13.1146 & 3.6214 \tabularnewline
50 & 0.0583 & -0.0386 & 8e-04 & 559.7388 & 11.1948 & 3.3459 \tabularnewline
51 & 0.0628 & -0.036 & 7e-04 & 476.3651 & 9.5273 & 3.0866 \tabularnewline
52 & 0.067 & -0.054 & 0.0011 & 1069.9525 & 21.3991 & 4.6259 \tabularnewline
53 & 0.0715 & -0.0512 & 0.001 & 936.1402 & 18.7228 & 4.327 \tabularnewline
54 & 0.0739 & -0.0564 & 0.0011 & 1157.1464 & 23.1429 & 4.8107 \tabularnewline
55 & 0.07 & -0.0665 & 0.0013 & 1954.792 & 39.0958 & 6.2527 \tabularnewline
56 & 0.0717 & -0.0702 & 0.0014 & 2257.4995 & 45.15 & 6.7194 \tabularnewline
57 & 0.0745 & -0.0687 & 0.0014 & 2148.7351 & 42.9747 & 6.5555 \tabularnewline
58 & 0.0785 & -0.0744 & 0.0015 & 2420.2011 & 48.404 & 6.9573 \tabularnewline
59 & 0.085 & -0.088 & 0.0018 & 3299.2888 & 65.9858 & 8.1232 \tabularnewline
60 & 0.0905 & -0.0908 & 0.0018 & 3558.5384 & 71.1708 & 8.4363 \tabularnewline
61 & 0.095 & -0.108 & 0.0022 & 5150.1066 & 103.0021 & 10.149 \tabularnewline
62 & 0.0994 & -0.1072 & 0.0021 & 5015.2432 & 100.3049 & 10.0152 \tabularnewline
63 & 0.1049 & -0.1131 & 0.0023 & 5474.0881 & 109.4818 & 10.4634 \tabularnewline
64 & 0.1096 & -0.1221 & 0.0024 & 6379.1491 & 127.583 & 11.2953 \tabularnewline
65 & 0.1152 & -0.1127 & 0.0023 & 5293.3086 & 105.8662 & 10.2891 \tabularnewline
66 & 0.118 & -0.1201 & 0.0024 & 6111.6125 & 122.2323 & 11.0559 \tabularnewline
67 & 0.1113 & -0.1309 & 0.0026 & 8718.5572 & 174.3711 & 13.205 \tabularnewline
68 & 0.1131 & -0.1362 & 0.0027 & 9736.2737 & 194.7255 & 13.9544 \tabularnewline
69 & 0.1167 & -0.1419 & 0.0028 & 10509.0877 & 210.1818 & 14.4976 \tabularnewline
70 & 0.1221 & -0.1711 & 0.0034 & 14727.1334 & 294.5427 & 17.1622 \tabularnewline
71 & 0.1291 & -0.1921 & 0.0038 & 18116.9528 & 362.3391 & 19.0352 \tabularnewline
72 & 0.1343 & -0.2097 & 0.0042 & 21848.6855 & 436.9737 & 20.9039 \tabularnewline
73 & 0.1385 & -0.2131 & 0.0043 & 23080.8563 & 461.6171 & 21.4853 \tabularnewline
74 & 0.1436 & -0.2256 & 0.0045 & 25593.005 & 511.8601 & 22.6243 \tabularnewline
75 & 0.1497 & -0.2423 & 0.0048 & 28949.9206 & 578.9984 & 24.0624 \tabularnewline
76 & 0.1548 & -0.2507 & 0.005 & 30986.272 & 619.7254 & 24.8943 \tabularnewline
77 & 0.1612 & -0.2636 & 0.0053 & 33457.8203 & 669.1564 & 25.8681 \tabularnewline
78 & 0.1642 & -0.2865 & 0.0057 & 40134.7141 & 802.6943 & 28.3319 \tabularnewline
79 & 0.1548 & -0.2712 & 0.0054 & 42655.8276 & 853.1166 & 29.2082 \tabularnewline
80 & 0.1565 & -0.2689 & 0.0054 & 43194.2236 & 863.8845 & 29.3919 \tabularnewline
81 & 0.1606 & -0.2967 & 0.0059 & 52291.5748 & 1045.8315 & 32.3393 \tabularnewline
82 & 0.1671 & -0.3043 & 0.0061 & 53137.2003 & 1062.744 & 32.5998 \tabularnewline
83 & 0.1746 & -0.3189 & 0.0064 & 57005.8066 & 1140.1161 & 33.7656 \tabularnewline
84 & 0.1796 & -0.3174 & 0.0063 & 57107.9351 & 1142.1587 & 33.7958 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33821&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]35[/C][C]0.0111[/C][C]-0.0164[/C][C]3e-04[/C][C]83.1804[/C][C]1.6636[/C][C]1.2898[/C][/ROW]
[ROW][C]36[/C][C]0.0178[/C][C]-0.0094[/C][C]2e-04[/C][C]27.6674[/C][C]0.5533[/C][C]0.7439[/C][/ROW]
[ROW][C]37[/C][C]0.0227[/C][C]-0.0114[/C][C]2e-04[/C][C]41.6892[/C][C]0.8338[/C][C]0.9131[/C][/ROW]
[ROW][C]38[/C][C]0.0245[/C][C]-0.0063[/C][C]1e-04[/C][C]12.5448[/C][C]0.2509[/C][C]0.5009[/C][/ROW]
[ROW][C]39[/C][C]0.0273[/C][C]-0.0048[/C][C]1e-04[/C][C]7.0417[/C][C]0.1408[/C][C]0.3753[/C][/ROW]
[ROW][C]40[/C][C]0.0304[/C][C]-0.0243[/C][C]5e-04[/C][C]182.9748[/C][C]3.6595[/C][C]1.913[/C][/ROW]
[ROW][C]41[/C][C]0.0332[/C][C]-0.0227[/C][C]5e-04[/C][C]154.9557[/C][C]3.0991[/C][C]1.7604[/C][/ROW]
[ROW][C]42[/C][C]0.0347[/C][C]-0.0214[/C][C]4e-04[/C][C]140.8867[/C][C]2.8177[/C][C]1.6786[/C][/ROW]
[ROW][C]43[/C][C]0.033[/C][C]-0.0373[/C][C]7e-04[/C][C]531.0488[/C][C]10.621[/C][C]3.259[/C][/ROW]
[ROW][C]44[/C][C]0.0343[/C][C]-0.0276[/C][C]6e-04[/C][C]300.9353[/C][C]6.0187[/C][C]2.4533[/C][/ROW]
[ROW][C]45[/C][C]0.0359[/C][C]-0.0211[/C][C]4e-04[/C][C]174.2692[/C][C]3.4854[/C][C]1.8669[/C][/ROW]
[ROW][C]46[/C][C]0.0382[/C][C]-0.0033[/C][C]1e-04[/C][C]4.1553[/C][C]0.0831[/C][C]0.2883[/C][/ROW]
[ROW][C]47[/C][C]0.0442[/C][C]-0.017[/C][C]3e-04[/C][C]105.5869[/C][C]2.1117[/C][C]1.4532[/C][/ROW]
[ROW][C]48[/C][C]0.05[/C][C]-0.0222[/C][C]4e-04[/C][C]182.06[/C][C]3.6412[/C][C]1.9082[/C][/ROW]
[ROW][C]49[/C][C]0.0549[/C][C]-0.0415[/C][C]8e-04[/C][C]655.7319[/C][C]13.1146[/C][C]3.6214[/C][/ROW]
[ROW][C]50[/C][C]0.0583[/C][C]-0.0386[/C][C]8e-04[/C][C]559.7388[/C][C]11.1948[/C][C]3.3459[/C][/ROW]
[ROW][C]51[/C][C]0.0628[/C][C]-0.036[/C][C]7e-04[/C][C]476.3651[/C][C]9.5273[/C][C]3.0866[/C][/ROW]
[ROW][C]52[/C][C]0.067[/C][C]-0.054[/C][C]0.0011[/C][C]1069.9525[/C][C]21.3991[/C][C]4.6259[/C][/ROW]
[ROW][C]53[/C][C]0.0715[/C][C]-0.0512[/C][C]0.001[/C][C]936.1402[/C][C]18.7228[/C][C]4.327[/C][/ROW]
[ROW][C]54[/C][C]0.0739[/C][C]-0.0564[/C][C]0.0011[/C][C]1157.1464[/C][C]23.1429[/C][C]4.8107[/C][/ROW]
[ROW][C]55[/C][C]0.07[/C][C]-0.0665[/C][C]0.0013[/C][C]1954.792[/C][C]39.0958[/C][C]6.2527[/C][/ROW]
[ROW][C]56[/C][C]0.0717[/C][C]-0.0702[/C][C]0.0014[/C][C]2257.4995[/C][C]45.15[/C][C]6.7194[/C][/ROW]
[ROW][C]57[/C][C]0.0745[/C][C]-0.0687[/C][C]0.0014[/C][C]2148.7351[/C][C]42.9747[/C][C]6.5555[/C][/ROW]
[ROW][C]58[/C][C]0.0785[/C][C]-0.0744[/C][C]0.0015[/C][C]2420.2011[/C][C]48.404[/C][C]6.9573[/C][/ROW]
[ROW][C]59[/C][C]0.085[/C][C]-0.088[/C][C]0.0018[/C][C]3299.2888[/C][C]65.9858[/C][C]8.1232[/C][/ROW]
[ROW][C]60[/C][C]0.0905[/C][C]-0.0908[/C][C]0.0018[/C][C]3558.5384[/C][C]71.1708[/C][C]8.4363[/C][/ROW]
[ROW][C]61[/C][C]0.095[/C][C]-0.108[/C][C]0.0022[/C][C]5150.1066[/C][C]103.0021[/C][C]10.149[/C][/ROW]
[ROW][C]62[/C][C]0.0994[/C][C]-0.1072[/C][C]0.0021[/C][C]5015.2432[/C][C]100.3049[/C][C]10.0152[/C][/ROW]
[ROW][C]63[/C][C]0.1049[/C][C]-0.1131[/C][C]0.0023[/C][C]5474.0881[/C][C]109.4818[/C][C]10.4634[/C][/ROW]
[ROW][C]64[/C][C]0.1096[/C][C]-0.1221[/C][C]0.0024[/C][C]6379.1491[/C][C]127.583[/C][C]11.2953[/C][/ROW]
[ROW][C]65[/C][C]0.1152[/C][C]-0.1127[/C][C]0.0023[/C][C]5293.3086[/C][C]105.8662[/C][C]10.2891[/C][/ROW]
[ROW][C]66[/C][C]0.118[/C][C]-0.1201[/C][C]0.0024[/C][C]6111.6125[/C][C]122.2323[/C][C]11.0559[/C][/ROW]
[ROW][C]67[/C][C]0.1113[/C][C]-0.1309[/C][C]0.0026[/C][C]8718.5572[/C][C]174.3711[/C][C]13.205[/C][/ROW]
[ROW][C]68[/C][C]0.1131[/C][C]-0.1362[/C][C]0.0027[/C][C]9736.2737[/C][C]194.7255[/C][C]13.9544[/C][/ROW]
[ROW][C]69[/C][C]0.1167[/C][C]-0.1419[/C][C]0.0028[/C][C]10509.0877[/C][C]210.1818[/C][C]14.4976[/C][/ROW]
[ROW][C]70[/C][C]0.1221[/C][C]-0.1711[/C][C]0.0034[/C][C]14727.1334[/C][C]294.5427[/C][C]17.1622[/C][/ROW]
[ROW][C]71[/C][C]0.1291[/C][C]-0.1921[/C][C]0.0038[/C][C]18116.9528[/C][C]362.3391[/C][C]19.0352[/C][/ROW]
[ROW][C]72[/C][C]0.1343[/C][C]-0.2097[/C][C]0.0042[/C][C]21848.6855[/C][C]436.9737[/C][C]20.9039[/C][/ROW]
[ROW][C]73[/C][C]0.1385[/C][C]-0.2131[/C][C]0.0043[/C][C]23080.8563[/C][C]461.6171[/C][C]21.4853[/C][/ROW]
[ROW][C]74[/C][C]0.1436[/C][C]-0.2256[/C][C]0.0045[/C][C]25593.005[/C][C]511.8601[/C][C]22.6243[/C][/ROW]
[ROW][C]75[/C][C]0.1497[/C][C]-0.2423[/C][C]0.0048[/C][C]28949.9206[/C][C]578.9984[/C][C]24.0624[/C][/ROW]
[ROW][C]76[/C][C]0.1548[/C][C]-0.2507[/C][C]0.005[/C][C]30986.272[/C][C]619.7254[/C][C]24.8943[/C][/ROW]
[ROW][C]77[/C][C]0.1612[/C][C]-0.2636[/C][C]0.0053[/C][C]33457.8203[/C][C]669.1564[/C][C]25.8681[/C][/ROW]
[ROW][C]78[/C][C]0.1642[/C][C]-0.2865[/C][C]0.0057[/C][C]40134.7141[/C][C]802.6943[/C][C]28.3319[/C][/ROW]
[ROW][C]79[/C][C]0.1548[/C][C]-0.2712[/C][C]0.0054[/C][C]42655.8276[/C][C]853.1166[/C][C]29.2082[/C][/ROW]
[ROW][C]80[/C][C]0.1565[/C][C]-0.2689[/C][C]0.0054[/C][C]43194.2236[/C][C]863.8845[/C][C]29.3919[/C][/ROW]
[ROW][C]81[/C][C]0.1606[/C][C]-0.2967[/C][C]0.0059[/C][C]52291.5748[/C][C]1045.8315[/C][C]32.3393[/C][/ROW]
[ROW][C]82[/C][C]0.1671[/C][C]-0.3043[/C][C]0.0061[/C][C]53137.2003[/C][C]1062.744[/C][C]32.5998[/C][/ROW]
[ROW][C]83[/C][C]0.1746[/C][C]-0.3189[/C][C]0.0064[/C][C]57005.8066[/C][C]1140.1161[/C][C]33.7656[/C][/ROW]
[ROW][C]84[/C][C]0.1796[/C][C]-0.3174[/C][C]0.0063[/C][C]57107.9351[/C][C]1142.1587[/C][C]33.7958[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33821&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33821&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
35	0.0111	-0.0164	3e-04	83.1804	1.6636	1.2898
36	0.0178	-0.0094	2e-04	27.6674	0.5533	0.7439
37	0.0227	-0.0114	2e-04	41.6892	0.8338	0.9131
38	0.0245	-0.0063	1e-04	12.5448	0.2509	0.5009
39	0.0273	-0.0048	1e-04	7.0417	0.1408	0.3753
40	0.0304	-0.0243	5e-04	182.9748	3.6595	1.913
41	0.0332	-0.0227	5e-04	154.9557	3.0991	1.7604
42	0.0347	-0.0214	4e-04	140.8867	2.8177	1.6786
43	0.033	-0.0373	7e-04	531.0488	10.621	3.259
44	0.0343	-0.0276	6e-04	300.9353	6.0187	2.4533
45	0.0359	-0.0211	4e-04	174.2692	3.4854	1.8669
46	0.0382	-0.0033	1e-04	4.1553	0.0831	0.2883
47	0.0442	-0.017	3e-04	105.5869	2.1117	1.4532
48	0.05	-0.0222	4e-04	182.06	3.6412	1.9082
49	0.0549	-0.0415	8e-04	655.7319	13.1146	3.6214
50	0.0583	-0.0386	8e-04	559.7388	11.1948	3.3459
51	0.0628	-0.036	7e-04	476.3651	9.5273	3.0866
52	0.067	-0.054	0.0011	1069.9525	21.3991	4.6259
53	0.0715	-0.0512	0.001	936.1402	18.7228	4.327
54	0.0739	-0.0564	0.0011	1157.1464	23.1429	4.8107
55	0.07	-0.0665	0.0013	1954.792	39.0958	6.2527
56	0.0717	-0.0702	0.0014	2257.4995	45.15	6.7194
57	0.0745	-0.0687	0.0014	2148.7351	42.9747	6.5555
58	0.0785	-0.0744	0.0015	2420.2011	48.404	6.9573
59	0.085	-0.088	0.0018	3299.2888	65.9858	8.1232
60	0.0905	-0.0908	0.0018	3558.5384	71.1708	8.4363
61	0.095	-0.108	0.0022	5150.1066	103.0021	10.149
62	0.0994	-0.1072	0.0021	5015.2432	100.3049	10.0152
63	0.1049	-0.1131	0.0023	5474.0881	109.4818	10.4634
64	0.1096	-0.1221	0.0024	6379.1491	127.583	11.2953
65	0.1152	-0.1127	0.0023	5293.3086	105.8662	10.2891
66	0.118	-0.1201	0.0024	6111.6125	122.2323	11.0559
67	0.1113	-0.1309	0.0026	8718.5572	174.3711	13.205
68	0.1131	-0.1362	0.0027	9736.2737	194.7255	13.9544
69	0.1167	-0.1419	0.0028	10509.0877	210.1818	14.4976
70	0.1221	-0.1711	0.0034	14727.1334	294.5427	17.1622
71	0.1291	-0.1921	0.0038	18116.9528	362.3391	19.0352
72	0.1343	-0.2097	0.0042	21848.6855	436.9737	20.9039
73	0.1385	-0.2131	0.0043	23080.8563	461.6171	21.4853
74	0.1436	-0.2256	0.0045	25593.005	511.8601	22.6243
75	0.1497	-0.2423	0.0048	28949.9206	578.9984	24.0624
76	0.1548	-0.2507	0.005	30986.272	619.7254	24.8943
77	0.1612	-0.2636	0.0053	33457.8203	669.1564	25.8681
78	0.1642	-0.2865	0.0057	40134.7141	802.6943	28.3319
79	0.1548	-0.2712	0.0054	42655.8276	853.1166	29.2082
80	0.1565	-0.2689	0.0054	43194.2236	863.8845	29.3919
81	0.1606	-0.2967	0.0059	52291.5748	1045.8315	32.3393
82	0.1671	-0.3043	0.0061	53137.2003	1062.744	32.5998
83	0.1746	-0.3189	0.0064	57005.8066	1140.1161	33.7656
84	0.1796	-0.3174	0.0063	57107.9351	1142.1587	33.7958

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 50 ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 2 ; par8 = 0 ; par9 = 0 ; par10 = FALSE ;

Parameters (R input):

par1 = 50 ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 2 ; 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,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