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Author

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R Software Module

rwasp_arimaforecasting.wasp

Title produced by software

ARIMA Forecasting

Date of computation

Tue, 06 Dec 2011 11:08:05 -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/06/t1323187696yl4fxtlttoowso3.htm/, Retrieved Mon, 29 Apr 2024 06:47:54 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=151719, Retrieved Mon, 29 Apr 2024 06:47:54 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

106

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

-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Standard Deviation-Mean Plot] [Unemployment] [2010-11-29 10:34:47] [b98453cac15ba1066b407e146608df68]
- RMP     [ARIMA Forecasting] [Unemployment] [2010-11-29 20:46:45] [b98453cac15ba1066b407e146608df68]
- R P         [ARIMA Forecasting] [] [2011-12-06 16:08:05] [5fd8c857995b7937a45335fd5ccccdde] [Current]
- R  D          [ARIMA Forecasting] [] [2011-12-06 16:09:17] [9b13650c94c5192ca5135ec8a1fa39f7] 
-   P             [ARIMA Forecasting] [] [2011-12-22 14:08:39] [9b13650c94c5192ca5135ec8a1fa39f7] 

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Dataseries X:

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Boxplots

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Herman Ole Andreas Wold' @ wold.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 & 3 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=151719&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=151719&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=151719&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	3 seconds
R Server	'Herman Ole Andreas Wold' @ wold.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[348])
336	719.5	-	-	-	-	-	-	-
337	817.4	-	-	-	-	-	-	-
338	803.3	-	-	-	-	-	-	-
339	752.5	-	-	-	-	-	-	-
340	689	-	-	-	-	-	-	-
341	630.4	-	-	-	-	-	-	-
342	765.5	-	-	-	-	-	-	-
343	757.7	-	-	-	-	-	-	-
344	732.2	-	-	-	-	-	-	-
345	702.6	-	-	-	-	-	-	-
346	683.3	-	-	-	-	-	-	-
347	709.5	-	-	-	-	-	-	-
348	702.2	-	-	-	-	-	-	-
349	784.8	834.2599	790.0403	878.4795	0.0142	1	0.7726	1
350	810.9	838.9147	776.3788	901.4507	0.19	0.9551	0.8678	1
351	755.6	810.6034	734.0128	887.194	0.0796	0.497	0.9315	0.9972
352	656.8	756.8672	668.4281	845.3064	0.0133	0.5112	0.9337	0.8872
353	615.1	726.6039	627.7259	825.4819	0.0135	0.9168	0.9717	0.6857
354	745.3	844.4327	736.1172	952.7481	0.0364	1	0.9234	0.995
355	694.1	826.0109	709.0169	943.0049	0.0136	0.9118	0.8738	0.981
356	675.7	787.7847	662.7129	912.8566	0.0395	0.929	0.8081	0.9101
357	643.7	784.7171	652.0583	917.3758	0.0186	0.9464	0.8875	0.8886
358	622.1	763.9731	624.1386	903.8077	0.0234	0.9541	0.8709	0.8067
359	634.6	795.303	648.6432	941.9627	0.0159	0.9897	0.8742	0.8933
360	588	807.4119	654.2308	960.593	0.0025	0.9865	0.9109	0.9109
361	689.7	956.2668	792.5067	1120.0268	7e-04	1	0.9799	0.9988
362	673.9	957.9284	784.2325	1131.6243	7e-04	0.9988	0.9515	0.998
363	647.9	934.6669	751.5736	1117.7603	0.0011	0.9974	0.9724	0.9936
364	568.8	877.729	685.6975	1069.7605	8e-04	0.9905	0.9879	0.9634
365	545.7	842.2446	641.6729	1042.8163	0.0019	0.9962	0.9868	0.9144
366	632.6	957.0633	748.3005	1165.8261	0.0012	0.9999	0.9766	0.9916
367	643.8	935.5761	718.9317	1152.2205	0.0041	0.9969	0.9855	0.9826
368	593.1	896.6227	672.3735	1120.8719	0.004	0.9864	0.9733	0.9554
369	579.7	879.7331	648.1286	1111.3375	0.0056	0.9924	0.9771	0.9335
370	546	856.8514	618.1183	1095.5846	0.0054	0.9886	0.973	0.8979
371	562.9	874.497	628.8419	1120.1521	0.0065	0.9956	0.9722	0.9154
372	572.5	874.5681	622.1809	1126.9554	0.0095	0.9922	0.987	0.9096

\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[348]) \tabularnewline
336 & 719.5 & - & - & - & - & - & - & - \tabularnewline
337 & 817.4 & - & - & - & - & - & - & - \tabularnewline
338 & 803.3 & - & - & - & - & - & - & - \tabularnewline
339 & 752.5 & - & - & - & - & - & - & - \tabularnewline
340 & 689 & - & - & - & - & - & - & - \tabularnewline
341 & 630.4 & - & - & - & - & - & - & - \tabularnewline
342 & 765.5 & - & - & - & - & - & - & - \tabularnewline
343 & 757.7 & - & - & - & - & - & - & - \tabularnewline
344 & 732.2 & - & - & - & - & - & - & - \tabularnewline
345 & 702.6 & - & - & - & - & - & - & - \tabularnewline
346 & 683.3 & - & - & - & - & - & - & - \tabularnewline
347 & 709.5 & - & - & - & - & - & - & - \tabularnewline
348 & 702.2 & - & - & - & - & - & - & - \tabularnewline
349 & 784.8 & 834.2599 & 790.0403 & 878.4795 & 0.0142 & 1 & 0.7726 & 1 \tabularnewline
350 & 810.9 & 838.9147 & 776.3788 & 901.4507 & 0.19 & 0.9551 & 0.8678 & 1 \tabularnewline
351 & 755.6 & 810.6034 & 734.0128 & 887.194 & 0.0796 & 0.497 & 0.9315 & 0.9972 \tabularnewline
352 & 656.8 & 756.8672 & 668.4281 & 845.3064 & 0.0133 & 0.5112 & 0.9337 & 0.8872 \tabularnewline
353 & 615.1 & 726.6039 & 627.7259 & 825.4819 & 0.0135 & 0.9168 & 0.9717 & 0.6857 \tabularnewline
354 & 745.3 & 844.4327 & 736.1172 & 952.7481 & 0.0364 & 1 & 0.9234 & 0.995 \tabularnewline
355 & 694.1 & 826.0109 & 709.0169 & 943.0049 & 0.0136 & 0.9118 & 0.8738 & 0.981 \tabularnewline
356 & 675.7 & 787.7847 & 662.7129 & 912.8566 & 0.0395 & 0.929 & 0.8081 & 0.9101 \tabularnewline
357 & 643.7 & 784.7171 & 652.0583 & 917.3758 & 0.0186 & 0.9464 & 0.8875 & 0.8886 \tabularnewline
358 & 622.1 & 763.9731 & 624.1386 & 903.8077 & 0.0234 & 0.9541 & 0.8709 & 0.8067 \tabularnewline
359 & 634.6 & 795.303 & 648.6432 & 941.9627 & 0.0159 & 0.9897 & 0.8742 & 0.8933 \tabularnewline
360 & 588 & 807.4119 & 654.2308 & 960.593 & 0.0025 & 0.9865 & 0.9109 & 0.9109 \tabularnewline
361 & 689.7 & 956.2668 & 792.5067 & 1120.0268 & 7e-04 & 1 & 0.9799 & 0.9988 \tabularnewline
362 & 673.9 & 957.9284 & 784.2325 & 1131.6243 & 7e-04 & 0.9988 & 0.9515 & 0.998 \tabularnewline
363 & 647.9 & 934.6669 & 751.5736 & 1117.7603 & 0.0011 & 0.9974 & 0.9724 & 0.9936 \tabularnewline
364 & 568.8 & 877.729 & 685.6975 & 1069.7605 & 8e-04 & 0.9905 & 0.9879 & 0.9634 \tabularnewline
365 & 545.7 & 842.2446 & 641.6729 & 1042.8163 & 0.0019 & 0.9962 & 0.9868 & 0.9144 \tabularnewline
366 & 632.6 & 957.0633 & 748.3005 & 1165.8261 & 0.0012 & 0.9999 & 0.9766 & 0.9916 \tabularnewline
367 & 643.8 & 935.5761 & 718.9317 & 1152.2205 & 0.0041 & 0.9969 & 0.9855 & 0.9826 \tabularnewline
368 & 593.1 & 896.6227 & 672.3735 & 1120.8719 & 0.004 & 0.9864 & 0.9733 & 0.9554 \tabularnewline
369 & 579.7 & 879.7331 & 648.1286 & 1111.3375 & 0.0056 & 0.9924 & 0.9771 & 0.9335 \tabularnewline
370 & 546 & 856.8514 & 618.1183 & 1095.5846 & 0.0054 & 0.9886 & 0.973 & 0.8979 \tabularnewline
371 & 562.9 & 874.497 & 628.8419 & 1120.1521 & 0.0065 & 0.9956 & 0.9722 & 0.9154 \tabularnewline
372 & 572.5 & 874.5681 & 622.1809 & 1126.9554 & 0.0095 & 0.9922 & 0.987 & 0.9096 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=151719&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[348])[/C][/ROW]
[ROW][C]336[/C][C]719.5[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]337[/C][C]817.4[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]338[/C][C]803.3[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]339[/C][C]752.5[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]340[/C][C]689[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]341[/C][C]630.4[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]342[/C][C]765.5[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]343[/C][C]757.7[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]344[/C][C]732.2[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]345[/C][C]702.6[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]346[/C][C]683.3[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]347[/C][C]709.5[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]348[/C][C]702.2[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]349[/C][C]784.8[/C][C]834.2599[/C][C]790.0403[/C][C]878.4795[/C][C]0.0142[/C][C]1[/C][C]0.7726[/C][C]1[/C][/ROW]
[ROW][C]350[/C][C]810.9[/C][C]838.9147[/C][C]776.3788[/C][C]901.4507[/C][C]0.19[/C][C]0.9551[/C][C]0.8678[/C][C]1[/C][/ROW]
[ROW][C]351[/C][C]755.6[/C][C]810.6034[/C][C]734.0128[/C][C]887.194[/C][C]0.0796[/C][C]0.497[/C][C]0.9315[/C][C]0.9972[/C][/ROW]
[ROW][C]352[/C][C]656.8[/C][C]756.8672[/C][C]668.4281[/C][C]845.3064[/C][C]0.0133[/C][C]0.5112[/C][C]0.9337[/C][C]0.8872[/C][/ROW]
[ROW][C]353[/C][C]615.1[/C][C]726.6039[/C][C]627.7259[/C][C]825.4819[/C][C]0.0135[/C][C]0.9168[/C][C]0.9717[/C][C]0.6857[/C][/ROW]
[ROW][C]354[/C][C]745.3[/C][C]844.4327[/C][C]736.1172[/C][C]952.7481[/C][C]0.0364[/C][C]1[/C][C]0.9234[/C][C]0.995[/C][/ROW]
[ROW][C]355[/C][C]694.1[/C][C]826.0109[/C][C]709.0169[/C][C]943.0049[/C][C]0.0136[/C][C]0.9118[/C][C]0.8738[/C][C]0.981[/C][/ROW]
[ROW][C]356[/C][C]675.7[/C][C]787.7847[/C][C]662.7129[/C][C]912.8566[/C][C]0.0395[/C][C]0.929[/C][C]0.8081[/C][C]0.9101[/C][/ROW]
[ROW][C]357[/C][C]643.7[/C][C]784.7171[/C][C]652.0583[/C][C]917.3758[/C][C]0.0186[/C][C]0.9464[/C][C]0.8875[/C][C]0.8886[/C][/ROW]
[ROW][C]358[/C][C]622.1[/C][C]763.9731[/C][C]624.1386[/C][C]903.8077[/C][C]0.0234[/C][C]0.9541[/C][C]0.8709[/C][C]0.8067[/C][/ROW]
[ROW][C]359[/C][C]634.6[/C][C]795.303[/C][C]648.6432[/C][C]941.9627[/C][C]0.0159[/C][C]0.9897[/C][C]0.8742[/C][C]0.8933[/C][/ROW]
[ROW][C]360[/C][C]588[/C][C]807.4119[/C][C]654.2308[/C][C]960.593[/C][C]0.0025[/C][C]0.9865[/C][C]0.9109[/C][C]0.9109[/C][/ROW]
[ROW][C]361[/C][C]689.7[/C][C]956.2668[/C][C]792.5067[/C][C]1120.0268[/C][C]7e-04[/C][C]1[/C][C]0.9799[/C][C]0.9988[/C][/ROW]
[ROW][C]362[/C][C]673.9[/C][C]957.9284[/C][C]784.2325[/C][C]1131.6243[/C][C]7e-04[/C][C]0.9988[/C][C]0.9515[/C][C]0.998[/C][/ROW]
[ROW][C]363[/C][C]647.9[/C][C]934.6669[/C][C]751.5736[/C][C]1117.7603[/C][C]0.0011[/C][C]0.9974[/C][C]0.9724[/C][C]0.9936[/C][/ROW]
[ROW][C]364[/C][C]568.8[/C][C]877.729[/C][C]685.6975[/C][C]1069.7605[/C][C]8e-04[/C][C]0.9905[/C][C]0.9879[/C][C]0.9634[/C][/ROW]
[ROW][C]365[/C][C]545.7[/C][C]842.2446[/C][C]641.6729[/C][C]1042.8163[/C][C]0.0019[/C][C]0.9962[/C][C]0.9868[/C][C]0.9144[/C][/ROW]
[ROW][C]366[/C][C]632.6[/C][C]957.0633[/C][C]748.3005[/C][C]1165.8261[/C][C]0.0012[/C][C]0.9999[/C][C]0.9766[/C][C]0.9916[/C][/ROW]
[ROW][C]367[/C][C]643.8[/C][C]935.5761[/C][C]718.9317[/C][C]1152.2205[/C][C]0.0041[/C][C]0.9969[/C][C]0.9855[/C][C]0.9826[/C][/ROW]
[ROW][C]368[/C][C]593.1[/C][C]896.6227[/C][C]672.3735[/C][C]1120.8719[/C][C]0.004[/C][C]0.9864[/C][C]0.9733[/C][C]0.9554[/C][/ROW]
[ROW][C]369[/C][C]579.7[/C][C]879.7331[/C][C]648.1286[/C][C]1111.3375[/C][C]0.0056[/C][C]0.9924[/C][C]0.9771[/C][C]0.9335[/C][/ROW]
[ROW][C]370[/C][C]546[/C][C]856.8514[/C][C]618.1183[/C][C]1095.5846[/C][C]0.0054[/C][C]0.9886[/C][C]0.973[/C][C]0.8979[/C][/ROW]
[ROW][C]371[/C][C]562.9[/C][C]874.497[/C][C]628.8419[/C][C]1120.1521[/C][C]0.0065[/C][C]0.9956[/C][C]0.9722[/C][C]0.9154[/C][/ROW]
[ROW][C]372[/C][C]572.5[/C][C]874.5681[/C][C]622.1809[/C][C]1126.9554[/C][C]0.0095[/C][C]0.9922[/C][C]0.987[/C][C]0.9096[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=151719&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=151719&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[348])
336	719.5	-	-	-	-	-	-	-
337	817.4	-	-	-	-	-	-	-
338	803.3	-	-	-	-	-	-	-
339	752.5	-	-	-	-	-	-	-
340	689	-	-	-	-	-	-	-
341	630.4	-	-	-	-	-	-	-
342	765.5	-	-	-	-	-	-	-
343	757.7	-	-	-	-	-	-	-
344	732.2	-	-	-	-	-	-	-
345	702.6	-	-	-	-	-	-	-
346	683.3	-	-	-	-	-	-	-
347	709.5	-	-	-	-	-	-	-
348	702.2	-	-	-	-	-	-	-
349	784.8	834.2599	790.0403	878.4795	0.0142	1	0.7726	1
350	810.9	838.9147	776.3788	901.4507	0.19	0.9551	0.8678	1
351	755.6	810.6034	734.0128	887.194	0.0796	0.497	0.9315	0.9972
352	656.8	756.8672	668.4281	845.3064	0.0133	0.5112	0.9337	0.8872
353	615.1	726.6039	627.7259	825.4819	0.0135	0.9168	0.9717	0.6857
354	745.3	844.4327	736.1172	952.7481	0.0364	1	0.9234	0.995
355	694.1	826.0109	709.0169	943.0049	0.0136	0.9118	0.8738	0.981
356	675.7	787.7847	662.7129	912.8566	0.0395	0.929	0.8081	0.9101
357	643.7	784.7171	652.0583	917.3758	0.0186	0.9464	0.8875	0.8886
358	622.1	763.9731	624.1386	903.8077	0.0234	0.9541	0.8709	0.8067
359	634.6	795.303	648.6432	941.9627	0.0159	0.9897	0.8742	0.8933
360	588	807.4119	654.2308	960.593	0.0025	0.9865	0.9109	0.9109
361	689.7	956.2668	792.5067	1120.0268	7e-04	1	0.9799	0.9988
362	673.9	957.9284	784.2325	1131.6243	7e-04	0.9988	0.9515	0.998
363	647.9	934.6669	751.5736	1117.7603	0.0011	0.9974	0.9724	0.9936
364	568.8	877.729	685.6975	1069.7605	8e-04	0.9905	0.9879	0.9634
365	545.7	842.2446	641.6729	1042.8163	0.0019	0.9962	0.9868	0.9144
366	632.6	957.0633	748.3005	1165.8261	0.0012	0.9999	0.9766	0.9916
367	643.8	935.5761	718.9317	1152.2205	0.0041	0.9969	0.9855	0.9826
368	593.1	896.6227	672.3735	1120.8719	0.004	0.9864	0.9733	0.9554
369	579.7	879.7331	648.1286	1111.3375	0.0056	0.9924	0.9771	0.9335
370	546	856.8514	618.1183	1095.5846	0.0054	0.9886	0.973	0.8979
371	562.9	874.497	628.8419	1120.1521	0.0065	0.9956	0.9722	0.9154
372	572.5	874.5681	622.1809	1126.9554	0.0095	0.9922	0.987	0.9096

Univariate ARIMA Extrapolation Forecast Performance
time	% S.E.	PE	MAPE	Sq.E	MSE	RMSE
349	0.027	-0.0593	0	2446.2785	0	0
350	0.038	-0.0334	0.0463	784.8241	1615.5513	40.1939
351	0.0482	-0.0679	0.0535	3025.3757	2085.4928	45.6672
352	0.0596	-0.1322	0.0732	10013.4509	4067.4823	63.7768
353	0.0694	-0.1535	0.0892	12433.1182	5740.6095	75.7668
354	0.0654	-0.1174	0.0939	9827.2849	6421.722	80.1356
355	0.0723	-0.1597	0.1033	17400.4924	7990.1178	89.3875
356	0.081	-0.1423	0.1082	12562.9852	8561.7262	92.5296
357	0.0863	-0.1797	0.1161	19885.8139	9819.9582	99.0957
358	0.0934	-0.1857	0.1231	20127.9887	10850.7612	104.167
359	0.0941	-0.2021	0.1303	25825.4454	12212.0962	110.5084
360	0.0968	-0.2717	0.1421	48141.596	15206.2212	123.3135
361	0.0874	-0.2788	0.1526	71057.8392	19502.4995	139.6513
362	0.0925	-0.2965	0.1629	80672.1284	23871.7587	154.5049
363	0.0999	-0.3068	0.1725	82235.2597	27762.6587	166.6213
364	0.1116	-0.352	0.1837	95437.1188	31992.3125	178.8639
365	0.1215	-0.3521	0.1936	87938.7109	35283.2771	187.8384
366	0.1113	-0.339	0.2017	105276.4583	39171.7872	197.9186
367	0.1181	-0.3119	0.2075	85133.2946	41590.8139	203.9383
368	0.1276	-0.3385	0.214	92126.0324	44117.5748	210.0418
369	0.1343	-0.3411	0.2201	90019.8421	46303.3971	215.1822
370	0.1422	-0.3628	0.2266	96628.6069	48590.9066	220.4335
371	0.1433	-0.3563	0.2322	97092.7121	50699.6808	225.1659
372	0.1472	-0.3454	0.2369	91245.1521	52389.0754	228.8866

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
349 & 0.027 & -0.0593 & 0 & 2446.2785 & 0 & 0 \tabularnewline
350 & 0.038 & -0.0334 & 0.0463 & 784.8241 & 1615.5513 & 40.1939 \tabularnewline
351 & 0.0482 & -0.0679 & 0.0535 & 3025.3757 & 2085.4928 & 45.6672 \tabularnewline
352 & 0.0596 & -0.1322 & 0.0732 & 10013.4509 & 4067.4823 & 63.7768 \tabularnewline
353 & 0.0694 & -0.1535 & 0.0892 & 12433.1182 & 5740.6095 & 75.7668 \tabularnewline
354 & 0.0654 & -0.1174 & 0.0939 & 9827.2849 & 6421.722 & 80.1356 \tabularnewline
355 & 0.0723 & -0.1597 & 0.1033 & 17400.4924 & 7990.1178 & 89.3875 \tabularnewline
356 & 0.081 & -0.1423 & 0.1082 & 12562.9852 & 8561.7262 & 92.5296 \tabularnewline
357 & 0.0863 & -0.1797 & 0.1161 & 19885.8139 & 9819.9582 & 99.0957 \tabularnewline
358 & 0.0934 & -0.1857 & 0.1231 & 20127.9887 & 10850.7612 & 104.167 \tabularnewline
359 & 0.0941 & -0.2021 & 0.1303 & 25825.4454 & 12212.0962 & 110.5084 \tabularnewline
360 & 0.0968 & -0.2717 & 0.1421 & 48141.596 & 15206.2212 & 123.3135 \tabularnewline
361 & 0.0874 & -0.2788 & 0.1526 & 71057.8392 & 19502.4995 & 139.6513 \tabularnewline
362 & 0.0925 & -0.2965 & 0.1629 & 80672.1284 & 23871.7587 & 154.5049 \tabularnewline
363 & 0.0999 & -0.3068 & 0.1725 & 82235.2597 & 27762.6587 & 166.6213 \tabularnewline
364 & 0.1116 & -0.352 & 0.1837 & 95437.1188 & 31992.3125 & 178.8639 \tabularnewline
365 & 0.1215 & -0.3521 & 0.1936 & 87938.7109 & 35283.2771 & 187.8384 \tabularnewline
366 & 0.1113 & -0.339 & 0.2017 & 105276.4583 & 39171.7872 & 197.9186 \tabularnewline
367 & 0.1181 & -0.3119 & 0.2075 & 85133.2946 & 41590.8139 & 203.9383 \tabularnewline
368 & 0.1276 & -0.3385 & 0.214 & 92126.0324 & 44117.5748 & 210.0418 \tabularnewline
369 & 0.1343 & -0.3411 & 0.2201 & 90019.8421 & 46303.3971 & 215.1822 \tabularnewline
370 & 0.1422 & -0.3628 & 0.2266 & 96628.6069 & 48590.9066 & 220.4335 \tabularnewline
371 & 0.1433 & -0.3563 & 0.2322 & 97092.7121 & 50699.6808 & 225.1659 \tabularnewline
372 & 0.1472 & -0.3454 & 0.2369 & 91245.1521 & 52389.0754 & 228.8866 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=151719&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]349[/C][C]0.027[/C][C]-0.0593[/C][C]0[/C][C]2446.2785[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]350[/C][C]0.038[/C][C]-0.0334[/C][C]0.0463[/C][C]784.8241[/C][C]1615.5513[/C][C]40.1939[/C][/ROW]
[ROW][C]351[/C][C]0.0482[/C][C]-0.0679[/C][C]0.0535[/C][C]3025.3757[/C][C]2085.4928[/C][C]45.6672[/C][/ROW]
[ROW][C]352[/C][C]0.0596[/C][C]-0.1322[/C][C]0.0732[/C][C]10013.4509[/C][C]4067.4823[/C][C]63.7768[/C][/ROW]
[ROW][C]353[/C][C]0.0694[/C][C]-0.1535[/C][C]0.0892[/C][C]12433.1182[/C][C]5740.6095[/C][C]75.7668[/C][/ROW]
[ROW][C]354[/C][C]0.0654[/C][C]-0.1174[/C][C]0.0939[/C][C]9827.2849[/C][C]6421.722[/C][C]80.1356[/C][/ROW]
[ROW][C]355[/C][C]0.0723[/C][C]-0.1597[/C][C]0.1033[/C][C]17400.4924[/C][C]7990.1178[/C][C]89.3875[/C][/ROW]
[ROW][C]356[/C][C]0.081[/C][C]-0.1423[/C][C]0.1082[/C][C]12562.9852[/C][C]8561.7262[/C][C]92.5296[/C][/ROW]
[ROW][C]357[/C][C]0.0863[/C][C]-0.1797[/C][C]0.1161[/C][C]19885.8139[/C][C]9819.9582[/C][C]99.0957[/C][/ROW]
[ROW][C]358[/C][C]0.0934[/C][C]-0.1857[/C][C]0.1231[/C][C]20127.9887[/C][C]10850.7612[/C][C]104.167[/C][/ROW]
[ROW][C]359[/C][C]0.0941[/C][C]-0.2021[/C][C]0.1303[/C][C]25825.4454[/C][C]12212.0962[/C][C]110.5084[/C][/ROW]
[ROW][C]360[/C][C]0.0968[/C][C]-0.2717[/C][C]0.1421[/C][C]48141.596[/C][C]15206.2212[/C][C]123.3135[/C][/ROW]
[ROW][C]361[/C][C]0.0874[/C][C]-0.2788[/C][C]0.1526[/C][C]71057.8392[/C][C]19502.4995[/C][C]139.6513[/C][/ROW]
[ROW][C]362[/C][C]0.0925[/C][C]-0.2965[/C][C]0.1629[/C][C]80672.1284[/C][C]23871.7587[/C][C]154.5049[/C][/ROW]
[ROW][C]363[/C][C]0.0999[/C][C]-0.3068[/C][C]0.1725[/C][C]82235.2597[/C][C]27762.6587[/C][C]166.6213[/C][/ROW]
[ROW][C]364[/C][C]0.1116[/C][C]-0.352[/C][C]0.1837[/C][C]95437.1188[/C][C]31992.3125[/C][C]178.8639[/C][/ROW]
[ROW][C]365[/C][C]0.1215[/C][C]-0.3521[/C][C]0.1936[/C][C]87938.7109[/C][C]35283.2771[/C][C]187.8384[/C][/ROW]
[ROW][C]366[/C][C]0.1113[/C][C]-0.339[/C][C]0.2017[/C][C]105276.4583[/C][C]39171.7872[/C][C]197.9186[/C][/ROW]
[ROW][C]367[/C][C]0.1181[/C][C]-0.3119[/C][C]0.2075[/C][C]85133.2946[/C][C]41590.8139[/C][C]203.9383[/C][/ROW]
[ROW][C]368[/C][C]0.1276[/C][C]-0.3385[/C][C]0.214[/C][C]92126.0324[/C][C]44117.5748[/C][C]210.0418[/C][/ROW]
[ROW][C]369[/C][C]0.1343[/C][C]-0.3411[/C][C]0.2201[/C][C]90019.8421[/C][C]46303.3971[/C][C]215.1822[/C][/ROW]
[ROW][C]370[/C][C]0.1422[/C][C]-0.3628[/C][C]0.2266[/C][C]96628.6069[/C][C]48590.9066[/C][C]220.4335[/C][/ROW]
[ROW][C]371[/C][C]0.1433[/C][C]-0.3563[/C][C]0.2322[/C][C]97092.7121[/C][C]50699.6808[/C][C]225.1659[/C][/ROW]
[ROW][C]372[/C][C]0.1472[/C][C]-0.3454[/C][C]0.2369[/C][C]91245.1521[/C][C]52389.0754[/C][C]228.8866[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=151719&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=151719&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
349	0.027	-0.0593	0	2446.2785	0	0
350	0.038	-0.0334	0.0463	784.8241	1615.5513	40.1939
351	0.0482	-0.0679	0.0535	3025.3757	2085.4928	45.6672
352	0.0596	-0.1322	0.0732	10013.4509	4067.4823	63.7768
353	0.0694	-0.1535	0.0892	12433.1182	5740.6095	75.7668
354	0.0654	-0.1174	0.0939	9827.2849	6421.722	80.1356
355	0.0723	-0.1597	0.1033	17400.4924	7990.1178	89.3875
356	0.081	-0.1423	0.1082	12562.9852	8561.7262	92.5296
357	0.0863	-0.1797	0.1161	19885.8139	9819.9582	99.0957
358	0.0934	-0.1857	0.1231	20127.9887	10850.7612	104.167
359	0.0941	-0.2021	0.1303	25825.4454	12212.0962	110.5084
360	0.0968	-0.2717	0.1421	48141.596	15206.2212	123.3135
361	0.0874	-0.2788	0.1526	71057.8392	19502.4995	139.6513
362	0.0925	-0.2965	0.1629	80672.1284	23871.7587	154.5049
363	0.0999	-0.3068	0.1725	82235.2597	27762.6587	166.6213
364	0.1116	-0.352	0.1837	95437.1188	31992.3125	178.8639
365	0.1215	-0.3521	0.1936	87938.7109	35283.2771	187.8384
366	0.1113	-0.339	0.2017	105276.4583	39171.7872	197.9186
367	0.1181	-0.3119	0.2075	85133.2946	41590.8139	203.9383
368	0.1276	-0.3385	0.214	92126.0324	44117.5748	210.0418
369	0.1343	-0.3411	0.2201	90019.8421	46303.3971	215.1822
370	0.1422	-0.3628	0.2266	96628.6069	48590.9066	220.4335
371	0.1433	-0.3563	0.2322	97092.7121	50699.6808	225.1659
372	0.1472	-0.3454	0.2369	91245.1521	52389.0754	228.8866

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 12 ;

Parameters (R input):

par1 = 24 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 1 ; par7 = 0 ; par8 = 2 ; par9 = 0 ; par10 = FALSE ;

R code (references can be found in the software module):

par1 <- as.numeric(par1) #cut off periods
par2 <- as.numeric(par2) #lambda
par3 <- as.numeric(par3) #degree of non-seasonal differencing
par4 <- as.numeric(par4) #degree of seasonal differencing
par5 <- as.numeric(par5) #seasonal period
par6 <- as.numeric(par6) #p
par7 <- as.numeric(par7) #q
par8 <- as.numeric(par8) #P
par9 <- as.numeric(par9) #Q
if (par10 == 'TRUE') par10 <- TRUE
if (par10 == 'FALSE') par10 <- FALSE
if (par2 == 0) x <- log(x)
if (par2 != 0) x <- x^par2
lx <- length(x)
first <- lx - 2*par1
nx <- lx - par1
nx1 <- nx + 1
fx <- lx - nx
if (fx < 1) {
fx <- par5
nx1 <- lx + fx - 1
first <- lx - 2*fx
}
first <- 1
if (fx < 3) fx <- round(lx/10,0)
(arima.out <- arima(x[1:nx], order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5), include.mean=par10, method='ML'))
(forecast <- predict(arima.out,par1))
(lb <- forecast$pred - 1.96 * forecast$se)
(ub <- forecast$pred + 1.96 * forecast$se)
if (par2 == 0) {
x <- exp(x)
forecast$pred <- exp(forecast$pred)
lb <- exp(lb)
ub <- exp(ub)
}
if (par2 != 0) {
x <- x^(1/par2)
forecast$pred <- forecast$pred^(1/par2)
lb <- lb^(1/par2)
ub <- ub^(1/par2)
}
if (par2 < 0) {
olb <- lb
lb <- ub
ub <- olb
}
(actandfor <- c(x[1:nx], forecast$pred))
(perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
bitmap(file='test1.png')
opar <- par(mar=c(4,4,2,2),las=1)
ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
plot(x,ylim=ylim,type='n',xlim=c(first,lx))
usr <- par('usr')
rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
abline(h= (-3:3)*2 , col ='gray', lty =3)
polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
lines(nx1:lx, lb , lty=2)
lines(nx1:lx, ub , lty=2)
lines(x, lwd=2)
lines(nx1:lx, forecast$pred , lwd=2 , col ='white')
box()
par(opar)
dev.off()
prob.dec <- array(NA, dim=fx)
prob.sdec <- array(NA, dim=fx)
prob.ldec <- array(NA, dim=fx)
prob.pval <- array(NA, dim=fx)
perf.pe <- array(0, dim=fx)
perf.mape <- array(0, dim=fx)
perf.mape1 <- array(0, dim=fx)
perf.se <- array(0, dim=fx)
perf.mse <- array(0, dim=fx)
perf.mse1 <- array(0, dim=fx)
perf.rmse <- array(0, dim=fx)
for (i in 1:fx) {
locSD <- (ub[i] - forecast$pred[i]) / 1.96
perf.pe[i] = (x[nx+i] - forecast$pred[i]) / forecast$pred[i]
perf.se[i] = (x[nx+i] - forecast$pred[i])^2
prob.dec[i] = pnorm((x[nx+i-1] - forecast$pred[i]) / locSD)
prob.sdec[i] = pnorm((x[nx+i-par5] - forecast$pred[i]) / locSD)
prob.ldec[i] = pnorm((x[nx] - forecast$pred[i]) / locSD)
prob.pval[i] = pnorm(abs(x[nx+i] - forecast$pred[i]) / locSD)
}
perf.mape[1] = abs(perf.pe[1])
perf.mse[1] = abs(perf.se[1])
for (i in 2:fx) {
perf.mape[i] = perf.mape[i-1] + abs(perf.pe[i])
perf.mape1[i] = perf.mape[i] / i
perf.mse[i] = perf.mse[i-1] + perf.se[i]
perf.mse1[i] = perf.mse[i] / i
}
perf.rmse = sqrt(perf.mse1)
bitmap(file='test2.png')
plot(forecast$pred, pch=19, type='b',main='ARIMA Extrapolation Forecast', ylab='Forecast and 95% CI', xlab='time',ylim=c(min(lb),max(ub)))
dum <- forecast$pred
dum[1:par1] <- x[(nx+1):lx]
lines(dum, lty=1)
lines(ub,lty=3)
lines(lb,lty=3)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Univariate ARIMA Extrapolation Forecast',9,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'time',1,header=TRUE)
a<-table.element(a,'Y[t]',1,header=TRUE)
a<-table.element(a,'F[t]',1,header=TRUE)
a<-table.element(a,'95% LB',1,header=TRUE)
a<-table.element(a,'95% UB',1,header=TRUE)
a<-table.element(a,'p-value
(H0: Y[t] = F[t])',1,header=TRUE)
a<-table.element(a,'P(F[t]>Y[t-1])',1,header=TRUE)
a<-table.element(a,'P(F[t]>Y[t-s])',1,header=TRUE)
mylab <- paste('P(F[t]>Y[',nx,sep='')
mylab <- paste(mylab,'])',sep='')
a<-table.element(a,mylab,1,header=TRUE)
a<-table.row.end(a)
for (i in (nx-par5):nx) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.row.end(a)
}
for (i in 1:fx) {
a<-table.row.start(a)
a<-table.element(a,nx+i,header=TRUE)
a<-table.element(a,round(x[nx+i],4))
a<-table.element(a,round(forecast$pred[i],4))
a<-table.element(a,round(lb[i],4))
a<-table.element(a,round(ub[i],4))
a<-table.element(a,round((1-prob.pval[i]),4))
a<-table.element(a,round((1-prob.dec[i]),4))
a<-table.element(a,round((1-prob.sdec[i]),4))
a<-table.element(a,round((1-prob.ldec[i]),4))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Univariate ARIMA Extrapolation Forecast Performance',7,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'time',1,header=TRUE)
a<-table.element(a,'% S.E.',1,header=TRUE)
a<-table.element(a,'PE',1,header=TRUE)
a<-table.element(a,'MAPE',1,header=TRUE)
a<-table.element(a,'Sq.E',1,header=TRUE)
a<-table.element(a,'MSE',1,header=TRUE)
a<-table.element(a,'RMSE',1,header=TRUE)
a<-table.row.end(a)
for (i in 1:fx) {
a<-table.row.start(a)
a<-table.element(a,nx+i,header=TRUE)
a<-table.element(a,round(perc.se[i],4))
a<-table.element(a,round(perf.pe[i],4))
a<-table.element(a,round(perf.mape1[i],4))
a<-table.element(a,round(perf.se[i],4))
a<-table.element(a,round(perf.mse1[i],4))
a<-table.element(a,round(perf.rmse[i],4))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code