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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_arimaforecasting.wasp
Title produced by softwareARIMA Forecasting
Date of computationFri, 21 Dec 2012 16:00:35 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/21/t1356123668scn99lsiteq6t57.htm/, Retrieved Fri, 29 Mar 2024 12:31:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=204289, Retrieved Fri, 29 Mar 2024 12:31:31 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact53
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [ARIMA Forecasting] [] [2012-11-26 16:57:24] [15cbcfe738d59edaf37329746b028204]
- R P     [ARIMA Forecasting] [] [2012-12-21 21:00:35] [da7c95f16c64cb676a9e51b1b6b79fc0] [Current]
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Dataseries X:
235.1
280.7
264.6
240.7
201.4
240.8
241.1
223.8
206.1
174.7
203.3
220.5
299.5
347.4
338.3
327.7
351.6
396.6
438.8
395.6
363.5
378.8
357
369
464.8
479.1
431.3
366.5
326.3
355.1
331.6
261.3
249
205.5
235.6
240.9
264.9
253.8
232.3
193.8
177
213.2
207.2
180.6
188.6
175.4
199
179.6
225.8
234
200.2
183.6
178.2
203.2
208.5
191.8
172.8
148
159.4
154.5
213.2
196.4
182.8
176.4
153.6
173.2
171
151.2
161.9
157.2
201.7
236.4
356.1
398.3
403.7
384.6
365.8
368.1
367.9
347
343.3
292.9
311.5
300.9
366.9
356.9
329.7
316.2
269
289.3
266.2
253.6
233.8
228.4
253.6
260.1
306.6
309.2
309.5
271
279.9
317.9
298.4
246.7
227.3
209.1
259.9
266
320.6
308.5
282.2
262.7
263.5
313.1
284.3
252.6
250.3
246.5
312.7
333.2
446.4
511.6
515.5
506.4
483.2
522.3
509.8
460.7
405.8
375
378.5
406.8
467.8
469.8
429.8
355.8
332.7
378
360.5
334.7
319.5
323.1
363.6
352.1
411.9
388.6
416.4
360.7
338
417.2
388.4
371.1
331.5
353.7
396.7
447
533.5
565.4
542.3
488.7
467.1
531.3
496.1
444
403.4
386.3
394.1
404.1
462.1
448.1
432.3
386.3
395.2
421.9
382.9
384.2
345.5
323.4
372.6
376
462.7
487
444.2
399.3
394.9
455.4
414
375.5
347
339.4
385.8
378.8
451.8
446.1
422.5
383.1
352.8
445.3
367.5
355.1
326.2
319.8
331.8
340.9
394.1
417.2
369.9
349.2
321.4
405.7
342.9
316.5
284.2
270.9
288.8
278.8
324.4
310.9
299
273
279.3
359.2
305
282.1
250.3
246.5
257.9
266.5
315.9
318.4
295.4
266.4
245.8
362.8
324.9
294.2
289.5
295.2
290.3
272
307.4
328.7
292.9
249.1
230.4
361.5
321.7
277.2
260.7
251
257.6
241.8
287.5
292.3
274.7
254.2
230
339
318.2
287
295.8
284
271
262.7
340.6
379.4
373.3
355.2
338.4
466.9
451
422
429.2
425.9
460.7
463.6
541.4
544.2
517.5
469.4
439.4
549
533
506.1
484
457
481.5
469.5
544.7
541.2
521.5
469.7
434.4
542.6
517.3
485.7
465.8
447
426.6
411.6
467.5
484.5
451.2
417.4
379.9
484.7
455
420.8
416.5
376.3
405.6
405.8
500.8
514
475.5
430.1
414.4
538
526
488.5
520.2
504.4
568.5
610.6
818
830.9
835.9
782
762.3
856.9
820.9
769.6
752.2
724.4
723.1
719.5
817.4
803.3
752.5
689
630.4
765.5
757.7
732.2
702.6
683.3
709.5
702.2
784.8
810.9
755.6
656.8
615.1
745.3
694.1
675.7
643.7
622.1
634.6
588
689.7
673.9
647.9
568.8
545.7
632.6
643.8
593.1
579.7
546
562.9
572.5




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 6 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204289&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204289&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204289&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







Univariate ARIMA Extrapolation Forecast
timeY[t]F[t]95% LB95% UBp-value(H0: Y[t] = F[t])P(F[t]>Y[t-1])P(F[t]>Y[t-s])P(F[t]>Y[348])
336719.5-------
337817.4-------
338803.3-------
339752.5-------
340689-------
341630.4-------
342765.5-------
343757.7-------
344732.2-------
345702.6-------
346683.3-------
347709.5-------
348702.2-------
349784.8806.3003763.249849.35160.163810.30671
350810.9808.4126748.1824868.64270.46770.77890.56610.9997
351755.6778.9295705.4986852.36040.26670.19670.75970.9797
352656.8727.6443643.4858811.80290.04950.25750.81590.7233
353615.1692.939599.3313786.54670.05160.77540.90480.4231
354745.3805.1705703.3216907.01940.12460.99990.77740.9762
355694.1781.7204672.3022891.13860.05830.74290.66650.9228
356675.7744.835628.615861.05510.12180.80390.58440.7639
357643.7731.8649609.2699854.460.07930.81540.68010.6823
358622.1708.4593580.0388836.87980.09370.83850.64950.5381
359634.6727.8368593.8917861.78180.08620.93910.60580.6462
360588726.6476587.5991865.69610.02530.90280.63480.6348
361689.7830.4186683.0483977.78880.03060.99940.7280.9559
362673.9832.2706677.3255987.21570.02260.96430.60650.9501
363647.9802.4676640.3651964.57020.03080.940.71450.8873
364568.8750.927582.229919.62510.01720.88430.86290.7143
365545.7715.9109540.9265890.89530.02830.95030.87060.561
366632.6827.8918647.06531008.71830.01710.99890.81470.9135
367643.8804.1397617.7122990.56720.04590.96430.87630.8581
368593.1767.0085575.3431958.67390.03770.89620.82480.7463
369579.7753.7448557.0366950.4530.04140.94530.86360.6962
370546730.0981528.6512931.5450.03660.92830.85330.607
371562.9749.1902543.167955.21350.03820.97340.86220.6726
372572.5747.7647537.4244958.10510.05120.95750.93170.6644

\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 & 806.3003 & 763.249 & 849.3516 & 0.1638 & 1 & 0.3067 & 1 \tabularnewline
350 & 810.9 & 808.4126 & 748.1824 & 868.6427 & 0.4677 & 0.7789 & 0.5661 & 0.9997 \tabularnewline
351 & 755.6 & 778.9295 & 705.4986 & 852.3604 & 0.2667 & 0.1967 & 0.7597 & 0.9797 \tabularnewline
352 & 656.8 & 727.6443 & 643.4858 & 811.8029 & 0.0495 & 0.2575 & 0.8159 & 0.7233 \tabularnewline
353 & 615.1 & 692.939 & 599.3313 & 786.5467 & 0.0516 & 0.7754 & 0.9048 & 0.4231 \tabularnewline
354 & 745.3 & 805.1705 & 703.3216 & 907.0194 & 0.1246 & 0.9999 & 0.7774 & 0.9762 \tabularnewline
355 & 694.1 & 781.7204 & 672.3022 & 891.1386 & 0.0583 & 0.7429 & 0.6665 & 0.9228 \tabularnewline
356 & 675.7 & 744.835 & 628.615 & 861.0551 & 0.1218 & 0.8039 & 0.5844 & 0.7639 \tabularnewline
357 & 643.7 & 731.8649 & 609.2699 & 854.46 & 0.0793 & 0.8154 & 0.6801 & 0.6823 \tabularnewline
358 & 622.1 & 708.4593 & 580.0388 & 836.8798 & 0.0937 & 0.8385 & 0.6495 & 0.5381 \tabularnewline
359 & 634.6 & 727.8368 & 593.8917 & 861.7818 & 0.0862 & 0.9391 & 0.6058 & 0.6462 \tabularnewline
360 & 588 & 726.6476 & 587.5991 & 865.6961 & 0.0253 & 0.9028 & 0.6348 & 0.6348 \tabularnewline
361 & 689.7 & 830.4186 & 683.0483 & 977.7888 & 0.0306 & 0.9994 & 0.728 & 0.9559 \tabularnewline
362 & 673.9 & 832.2706 & 677.3255 & 987.2157 & 0.0226 & 0.9643 & 0.6065 & 0.9501 \tabularnewline
363 & 647.9 & 802.4676 & 640.3651 & 964.5702 & 0.0308 & 0.94 & 0.7145 & 0.8873 \tabularnewline
364 & 568.8 & 750.927 & 582.229 & 919.6251 & 0.0172 & 0.8843 & 0.8629 & 0.7143 \tabularnewline
365 & 545.7 & 715.9109 & 540.9265 & 890.8953 & 0.0283 & 0.9503 & 0.8706 & 0.561 \tabularnewline
366 & 632.6 & 827.8918 & 647.0653 & 1008.7183 & 0.0171 & 0.9989 & 0.8147 & 0.9135 \tabularnewline
367 & 643.8 & 804.1397 & 617.7122 & 990.5672 & 0.0459 & 0.9643 & 0.8763 & 0.8581 \tabularnewline
368 & 593.1 & 767.0085 & 575.3431 & 958.6739 & 0.0377 & 0.8962 & 0.8248 & 0.7463 \tabularnewline
369 & 579.7 & 753.7448 & 557.0366 & 950.453 & 0.0414 & 0.9453 & 0.8636 & 0.6962 \tabularnewline
370 & 546 & 730.0981 & 528.6512 & 931.545 & 0.0366 & 0.9283 & 0.8533 & 0.607 \tabularnewline
371 & 562.9 & 749.1902 & 543.167 & 955.2135 & 0.0382 & 0.9734 & 0.8622 & 0.6726 \tabularnewline
372 & 572.5 & 747.7647 & 537.4244 & 958.1051 & 0.0512 & 0.9575 & 0.9317 & 0.6644 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204289&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]806.3003[/C][C]763.249[/C][C]849.3516[/C][C]0.1638[/C][C]1[/C][C]0.3067[/C][C]1[/C][/ROW]
[ROW][C]350[/C][C]810.9[/C][C]808.4126[/C][C]748.1824[/C][C]868.6427[/C][C]0.4677[/C][C]0.7789[/C][C]0.5661[/C][C]0.9997[/C][/ROW]
[ROW][C]351[/C][C]755.6[/C][C]778.9295[/C][C]705.4986[/C][C]852.3604[/C][C]0.2667[/C][C]0.1967[/C][C]0.7597[/C][C]0.9797[/C][/ROW]
[ROW][C]352[/C][C]656.8[/C][C]727.6443[/C][C]643.4858[/C][C]811.8029[/C][C]0.0495[/C][C]0.2575[/C][C]0.8159[/C][C]0.7233[/C][/ROW]
[ROW][C]353[/C][C]615.1[/C][C]692.939[/C][C]599.3313[/C][C]786.5467[/C][C]0.0516[/C][C]0.7754[/C][C]0.9048[/C][C]0.4231[/C][/ROW]
[ROW][C]354[/C][C]745.3[/C][C]805.1705[/C][C]703.3216[/C][C]907.0194[/C][C]0.1246[/C][C]0.9999[/C][C]0.7774[/C][C]0.9762[/C][/ROW]
[ROW][C]355[/C][C]694.1[/C][C]781.7204[/C][C]672.3022[/C][C]891.1386[/C][C]0.0583[/C][C]0.7429[/C][C]0.6665[/C][C]0.9228[/C][/ROW]
[ROW][C]356[/C][C]675.7[/C][C]744.835[/C][C]628.615[/C][C]861.0551[/C][C]0.1218[/C][C]0.8039[/C][C]0.5844[/C][C]0.7639[/C][/ROW]
[ROW][C]357[/C][C]643.7[/C][C]731.8649[/C][C]609.2699[/C][C]854.46[/C][C]0.0793[/C][C]0.8154[/C][C]0.6801[/C][C]0.6823[/C][/ROW]
[ROW][C]358[/C][C]622.1[/C][C]708.4593[/C][C]580.0388[/C][C]836.8798[/C][C]0.0937[/C][C]0.8385[/C][C]0.6495[/C][C]0.5381[/C][/ROW]
[ROW][C]359[/C][C]634.6[/C][C]727.8368[/C][C]593.8917[/C][C]861.7818[/C][C]0.0862[/C][C]0.9391[/C][C]0.6058[/C][C]0.6462[/C][/ROW]
[ROW][C]360[/C][C]588[/C][C]726.6476[/C][C]587.5991[/C][C]865.6961[/C][C]0.0253[/C][C]0.9028[/C][C]0.6348[/C][C]0.6348[/C][/ROW]
[ROW][C]361[/C][C]689.7[/C][C]830.4186[/C][C]683.0483[/C][C]977.7888[/C][C]0.0306[/C][C]0.9994[/C][C]0.728[/C][C]0.9559[/C][/ROW]
[ROW][C]362[/C][C]673.9[/C][C]832.2706[/C][C]677.3255[/C][C]987.2157[/C][C]0.0226[/C][C]0.9643[/C][C]0.6065[/C][C]0.9501[/C][/ROW]
[ROW][C]363[/C][C]647.9[/C][C]802.4676[/C][C]640.3651[/C][C]964.5702[/C][C]0.0308[/C][C]0.94[/C][C]0.7145[/C][C]0.8873[/C][/ROW]
[ROW][C]364[/C][C]568.8[/C][C]750.927[/C][C]582.229[/C][C]919.6251[/C][C]0.0172[/C][C]0.8843[/C][C]0.8629[/C][C]0.7143[/C][/ROW]
[ROW][C]365[/C][C]545.7[/C][C]715.9109[/C][C]540.9265[/C][C]890.8953[/C][C]0.0283[/C][C]0.9503[/C][C]0.8706[/C][C]0.561[/C][/ROW]
[ROW][C]366[/C][C]632.6[/C][C]827.8918[/C][C]647.0653[/C][C]1008.7183[/C][C]0.0171[/C][C]0.9989[/C][C]0.8147[/C][C]0.9135[/C][/ROW]
[ROW][C]367[/C][C]643.8[/C][C]804.1397[/C][C]617.7122[/C][C]990.5672[/C][C]0.0459[/C][C]0.9643[/C][C]0.8763[/C][C]0.8581[/C][/ROW]
[ROW][C]368[/C][C]593.1[/C][C]767.0085[/C][C]575.3431[/C][C]958.6739[/C][C]0.0377[/C][C]0.8962[/C][C]0.8248[/C][C]0.7463[/C][/ROW]
[ROW][C]369[/C][C]579.7[/C][C]753.7448[/C][C]557.0366[/C][C]950.453[/C][C]0.0414[/C][C]0.9453[/C][C]0.8636[/C][C]0.6962[/C][/ROW]
[ROW][C]370[/C][C]546[/C][C]730.0981[/C][C]528.6512[/C][C]931.545[/C][C]0.0366[/C][C]0.9283[/C][C]0.8533[/C][C]0.607[/C][/ROW]
[ROW][C]371[/C][C]562.9[/C][C]749.1902[/C][C]543.167[/C][C]955.2135[/C][C]0.0382[/C][C]0.9734[/C][C]0.8622[/C][C]0.6726[/C][/ROW]
[ROW][C]372[/C][C]572.5[/C][C]747.7647[/C][C]537.4244[/C][C]958.1051[/C][C]0.0512[/C][C]0.9575[/C][C]0.9317[/C][C]0.6644[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204289&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204289&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
timeY[t]F[t]95% LB95% UBp-value(H0: Y[t] = F[t])P(F[t]>Y[t-1])P(F[t]>Y[t-s])P(F[t]>Y[348])
336719.5-------
337817.4-------
338803.3-------
339752.5-------
340689-------
341630.4-------
342765.5-------
343757.7-------
344732.2-------
345702.6-------
346683.3-------
347709.5-------
348702.2-------
349784.8806.3003763.249849.35160.163810.30671
350810.9808.4126748.1824868.64270.46770.77890.56610.9997
351755.6778.9295705.4986852.36040.26670.19670.75970.9797
352656.8727.6443643.4858811.80290.04950.25750.81590.7233
353615.1692.939599.3313786.54670.05160.77540.90480.4231
354745.3805.1705703.3216907.01940.12460.99990.77740.9762
355694.1781.7204672.3022891.13860.05830.74290.66650.9228
356675.7744.835628.615861.05510.12180.80390.58440.7639
357643.7731.8649609.2699854.460.07930.81540.68010.6823
358622.1708.4593580.0388836.87980.09370.83850.64950.5381
359634.6727.8368593.8917861.78180.08620.93910.60580.6462
360588726.6476587.5991865.69610.02530.90280.63480.6348
361689.7830.4186683.0483977.78880.03060.99940.7280.9559
362673.9832.2706677.3255987.21570.02260.96430.60650.9501
363647.9802.4676640.3651964.57020.03080.940.71450.8873
364568.8750.927582.229919.62510.01720.88430.86290.7143
365545.7715.9109540.9265890.89530.02830.95030.87060.561
366632.6827.8918647.06531008.71830.01710.99890.81470.9135
367643.8804.1397617.7122990.56720.04590.96430.87630.8581
368593.1767.0085575.3431958.67390.03770.89620.82480.7463
369579.7753.7448557.0366950.4530.04140.94530.86360.6962
370546730.0981528.6512931.5450.03660.92830.85330.607
371562.9749.1902543.167955.21350.03820.97340.86220.6726
372572.5747.7647537.4244958.10510.05120.95750.93170.6644







Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
3490.0272-0.02670462.263400
3500.0380.00310.01496.1873234.225415.3044
3510.0481-0.030.0199544.2656337.572118.3731
3520.059-0.09740.03935018.91771507.908538.8318
3530.0689-0.11230.05396058.90332418.107549.1743
3540.0645-0.07440.05733584.47922612.502851.1126
3550.0714-0.11210.06517677.3373336.050557.7586
3560.0796-0.09280.06864779.65093516.500659.3001
3570.0855-0.12050.07437773.05053989.450663.1621
3580.0925-0.12190.07917457.92274336.297865.8506
3590.0939-0.12810.08368693.09334732.370168.7922
3600.0976-0.19080.092519223.16185939.936177.071
3610.0905-0.16950.098419801.72037006.227283.7032
3620.095-0.19030.10525081.25038297.300291.0895
3630.1031-0.19260.110823891.15219336.890496.6276
3640.1146-0.24250.119133170.25610826.4757104.0504
3650.1247-0.23780.12628971.738311893.8441109.0589
3660.1114-0.23590.132138138.89213351.9023115.5504
3670.1183-0.19940.135725708.815914002.2662118.3312
3680.1275-0.22670.140230244.151914814.3605121.7143
3690.1332-0.23090.144530291.597115551.3717124.7051
3700.1408-0.25220.149433892.106316385.0415128.0041
3710.1403-0.24870.153834704.057217181.5204131.0783
3720.1435-0.23440.157130717.729617745.5291133.2123

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
349 & 0.0272 & -0.0267 & 0 & 462.2634 & 0 & 0 \tabularnewline
350 & 0.038 & 0.0031 & 0.0149 & 6.1873 & 234.2254 & 15.3044 \tabularnewline
351 & 0.0481 & -0.03 & 0.0199 & 544.2656 & 337.5721 & 18.3731 \tabularnewline
352 & 0.059 & -0.0974 & 0.0393 & 5018.9177 & 1507.9085 & 38.8318 \tabularnewline
353 & 0.0689 & -0.1123 & 0.0539 & 6058.9033 & 2418.1075 & 49.1743 \tabularnewline
354 & 0.0645 & -0.0744 & 0.0573 & 3584.4792 & 2612.5028 & 51.1126 \tabularnewline
355 & 0.0714 & -0.1121 & 0.0651 & 7677.337 & 3336.0505 & 57.7586 \tabularnewline
356 & 0.0796 & -0.0928 & 0.0686 & 4779.6509 & 3516.5006 & 59.3001 \tabularnewline
357 & 0.0855 & -0.1205 & 0.0743 & 7773.0505 & 3989.4506 & 63.1621 \tabularnewline
358 & 0.0925 & -0.1219 & 0.0791 & 7457.9227 & 4336.2978 & 65.8506 \tabularnewline
359 & 0.0939 & -0.1281 & 0.0836 & 8693.0933 & 4732.3701 & 68.7922 \tabularnewline
360 & 0.0976 & -0.1908 & 0.0925 & 19223.1618 & 5939.9361 & 77.071 \tabularnewline
361 & 0.0905 & -0.1695 & 0.0984 & 19801.7203 & 7006.2272 & 83.7032 \tabularnewline
362 & 0.095 & -0.1903 & 0.105 & 25081.2503 & 8297.3002 & 91.0895 \tabularnewline
363 & 0.1031 & -0.1926 & 0.1108 & 23891.1521 & 9336.8904 & 96.6276 \tabularnewline
364 & 0.1146 & -0.2425 & 0.1191 & 33170.256 & 10826.4757 & 104.0504 \tabularnewline
365 & 0.1247 & -0.2378 & 0.126 & 28971.7383 & 11893.8441 & 109.0589 \tabularnewline
366 & 0.1114 & -0.2359 & 0.1321 & 38138.892 & 13351.9023 & 115.5504 \tabularnewline
367 & 0.1183 & -0.1994 & 0.1357 & 25708.8159 & 14002.2662 & 118.3312 \tabularnewline
368 & 0.1275 & -0.2267 & 0.1402 & 30244.1519 & 14814.3605 & 121.7143 \tabularnewline
369 & 0.1332 & -0.2309 & 0.1445 & 30291.5971 & 15551.3717 & 124.7051 \tabularnewline
370 & 0.1408 & -0.2522 & 0.1494 & 33892.1063 & 16385.0415 & 128.0041 \tabularnewline
371 & 0.1403 & -0.2487 & 0.1538 & 34704.0572 & 17181.5204 & 131.0783 \tabularnewline
372 & 0.1435 & -0.2344 & 0.1571 & 30717.7296 & 17745.5291 & 133.2123 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204289&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]0.0272[/C][C]-0.0267[/C][C]0[/C][C]462.2634[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]350[/C][C]0.038[/C][C]0.0031[/C][C]0.0149[/C][C]6.1873[/C][C]234.2254[/C][C]15.3044[/C][/ROW]
[ROW][C]351[/C][C]0.0481[/C][C]-0.03[/C][C]0.0199[/C][C]544.2656[/C][C]337.5721[/C][C]18.3731[/C][/ROW]
[ROW][C]352[/C][C]0.059[/C][C]-0.0974[/C][C]0.0393[/C][C]5018.9177[/C][C]1507.9085[/C][C]38.8318[/C][/ROW]
[ROW][C]353[/C][C]0.0689[/C][C]-0.1123[/C][C]0.0539[/C][C]6058.9033[/C][C]2418.1075[/C][C]49.1743[/C][/ROW]
[ROW][C]354[/C][C]0.0645[/C][C]-0.0744[/C][C]0.0573[/C][C]3584.4792[/C][C]2612.5028[/C][C]51.1126[/C][/ROW]
[ROW][C]355[/C][C]0.0714[/C][C]-0.1121[/C][C]0.0651[/C][C]7677.337[/C][C]3336.0505[/C][C]57.7586[/C][/ROW]
[ROW][C]356[/C][C]0.0796[/C][C]-0.0928[/C][C]0.0686[/C][C]4779.6509[/C][C]3516.5006[/C][C]59.3001[/C][/ROW]
[ROW][C]357[/C][C]0.0855[/C][C]-0.1205[/C][C]0.0743[/C][C]7773.0505[/C][C]3989.4506[/C][C]63.1621[/C][/ROW]
[ROW][C]358[/C][C]0.0925[/C][C]-0.1219[/C][C]0.0791[/C][C]7457.9227[/C][C]4336.2978[/C][C]65.8506[/C][/ROW]
[ROW][C]359[/C][C]0.0939[/C][C]-0.1281[/C][C]0.0836[/C][C]8693.0933[/C][C]4732.3701[/C][C]68.7922[/C][/ROW]
[ROW][C]360[/C][C]0.0976[/C][C]-0.1908[/C][C]0.0925[/C][C]19223.1618[/C][C]5939.9361[/C][C]77.071[/C][/ROW]
[ROW][C]361[/C][C]0.0905[/C][C]-0.1695[/C][C]0.0984[/C][C]19801.7203[/C][C]7006.2272[/C][C]83.7032[/C][/ROW]
[ROW][C]362[/C][C]0.095[/C][C]-0.1903[/C][C]0.105[/C][C]25081.2503[/C][C]8297.3002[/C][C]91.0895[/C][/ROW]
[ROW][C]363[/C][C]0.1031[/C][C]-0.1926[/C][C]0.1108[/C][C]23891.1521[/C][C]9336.8904[/C][C]96.6276[/C][/ROW]
[ROW][C]364[/C][C]0.1146[/C][C]-0.2425[/C][C]0.1191[/C][C]33170.256[/C][C]10826.4757[/C][C]104.0504[/C][/ROW]
[ROW][C]365[/C][C]0.1247[/C][C]-0.2378[/C][C]0.126[/C][C]28971.7383[/C][C]11893.8441[/C][C]109.0589[/C][/ROW]
[ROW][C]366[/C][C]0.1114[/C][C]-0.2359[/C][C]0.1321[/C][C]38138.892[/C][C]13351.9023[/C][C]115.5504[/C][/ROW]
[ROW][C]367[/C][C]0.1183[/C][C]-0.1994[/C][C]0.1357[/C][C]25708.8159[/C][C]14002.2662[/C][C]118.3312[/C][/ROW]
[ROW][C]368[/C][C]0.1275[/C][C]-0.2267[/C][C]0.1402[/C][C]30244.1519[/C][C]14814.3605[/C][C]121.7143[/C][/ROW]
[ROW][C]369[/C][C]0.1332[/C][C]-0.2309[/C][C]0.1445[/C][C]30291.5971[/C][C]15551.3717[/C][C]124.7051[/C][/ROW]
[ROW][C]370[/C][C]0.1408[/C][C]-0.2522[/C][C]0.1494[/C][C]33892.1063[/C][C]16385.0415[/C][C]128.0041[/C][/ROW]
[ROW][C]371[/C][C]0.1403[/C][C]-0.2487[/C][C]0.1538[/C][C]34704.0572[/C][C]17181.5204[/C][C]131.0783[/C][/ROW]
[ROW][C]372[/C][C]0.1435[/C][C]-0.2344[/C][C]0.1571[/C][C]30717.7296[/C][C]17745.5291[/C][C]133.2123[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204289&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204289&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.PEMAPESq.EMSERMSE
3490.0272-0.02670462.263400
3500.0380.00310.01496.1873234.225415.3044
3510.0481-0.030.0199544.2656337.572118.3731
3520.059-0.09740.03935018.91771507.908538.8318
3530.0689-0.11230.05396058.90332418.107549.1743
3540.0645-0.07440.05733584.47922612.502851.1126
3550.0714-0.11210.06517677.3373336.050557.7586
3560.0796-0.09280.06864779.65093516.500659.3001
3570.0855-0.12050.07437773.05053989.450663.1621
3580.0925-0.12190.07917457.92274336.297865.8506
3590.0939-0.12810.08368693.09334732.370168.7922
3600.0976-0.19080.092519223.16185939.936177.071
3610.0905-0.16950.098419801.72037006.227283.7032
3620.095-0.19030.10525081.25038297.300291.0895
3630.1031-0.19260.110823891.15219336.890496.6276
3640.1146-0.24250.119133170.25610826.4757104.0504
3650.1247-0.23780.12628971.738311893.8441109.0589
3660.1114-0.23590.132138138.89213351.9023115.5504
3670.1183-0.19940.135725708.815914002.2662118.3312
3680.1275-0.22670.140230244.151914814.3605121.7143
3690.1332-0.23090.144530291.597115551.3717124.7051
3700.1408-0.25220.149433892.106316385.0415128.0041
3710.1403-0.24870.153834704.057217181.5204131.0783
3720.1435-0.23440.157130717.729617745.5291133.2123



Parameters (Session):
par1 = 24 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 2 ; par7 = 1 ; par8 = 0 ; par9 = 1 ; par10 = FALSE ;
Parameters (R input):
par1 = 24 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 2 ; par7 = 1 ; par8 = 0 ; par9 = 1 ; par10 = FALSE ;
R code (references can be found in the software module):
par1 <- as.numeric(par1) #cut off periods
par2 <- as.numeric(par2) #lambda
par3 <- as.numeric(par3) #degree of non-seasonal differencing
par4 <- as.numeric(par4) #degree of seasonal differencing
par5 <- as.numeric(par5) #seasonal period
par6 <- as.numeric(par6) #p
par7 <- as.numeric(par7) #q
par8 <- as.numeric(par8) #P
par9 <- as.numeric(par9) #Q
if (par10 == 'TRUE') par10 <- TRUE
if (par10 == 'FALSE') par10 <- FALSE
if (par2 == 0) x <- log(x)
if (par2 != 0) x <- x^par2
lx <- length(x)
first <- lx - 2*par1
nx <- lx - par1
nx1 <- nx + 1
fx <- lx - nx
if (fx < 1) {
fx <- par5
nx1 <- lx + fx - 1
first <- lx - 2*fx
}
first <- 1
if (fx < 3) fx <- round(lx/10,0)
(arima.out <- arima(x[1:nx], order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5), include.mean=par10, method='ML'))
(forecast <- predict(arima.out,par1))
(lb <- forecast$pred - 1.96 * forecast$se)
(ub <- forecast$pred + 1.96 * forecast$se)
if (par2 == 0) {
x <- exp(x)
forecast$pred <- exp(forecast$pred)
lb <- exp(lb)
ub <- exp(ub)
}
if (par2 != 0) {
x <- x^(1/par2)
forecast$pred <- forecast$pred^(1/par2)
lb <- lb^(1/par2)
ub <- ub^(1/par2)
}
if (par2 < 0) {
olb <- lb
lb <- ub
ub <- olb
}
(actandfor <- c(x[1:nx], forecast$pred))
(perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
bitmap(file='test1.png')
opar <- par(mar=c(4,4,2,2),las=1)
ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
plot(x,ylim=ylim,type='n',xlim=c(first,lx))
usr <- par('usr')
rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
abline(h= (-3:3)*2 , col ='gray', lty =3)
polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
lines(nx1:lx, lb , lty=2)
lines(nx1:lx, ub , lty=2)
lines(x, lwd=2)
lines(nx1:lx, forecast$pred , lwd=2 , col ='white')
box()
par(opar)
dev.off()
prob.dec <- array(NA, dim=fx)
prob.sdec <- array(NA, dim=fx)
prob.ldec <- array(NA, dim=fx)
prob.pval <- array(NA, dim=fx)
perf.pe <- array(0, dim=fx)
perf.mape <- array(0, dim=fx)
perf.mape1 <- array(0, dim=fx)
perf.se <- array(0, dim=fx)
perf.mse <- array(0, dim=fx)
perf.mse1 <- array(0, dim=fx)
perf.rmse <- array(0, dim=fx)
for (i in 1:fx) {
locSD <- (ub[i] - forecast$pred[i]) / 1.96
perf.pe[i] = (x[nx+i] - forecast$pred[i]) / forecast$pred[i]
perf.se[i] = (x[nx+i] - forecast$pred[i])^2
prob.dec[i] = pnorm((x[nx+i-1] - forecast$pred[i]) / locSD)
prob.sdec[i] = pnorm((x[nx+i-par5] - forecast$pred[i]) / locSD)
prob.ldec[i] = pnorm((x[nx] - forecast$pred[i]) / locSD)
prob.pval[i] = pnorm(abs(x[nx+i] - forecast$pred[i]) / locSD)
}
perf.mape[1] = abs(perf.pe[1])
perf.mse[1] = abs(perf.se[1])
for (i in 2:fx) {
perf.mape[i] = perf.mape[i-1] + abs(perf.pe[i])
perf.mape1[i] = perf.mape[i] / i
perf.mse[i] = perf.mse[i-1] + perf.se[i]
perf.mse1[i] = perf.mse[i] / i
}
perf.rmse = sqrt(perf.mse1)
bitmap(file='test2.png')
plot(forecast$pred, pch=19, type='b',main='ARIMA Extrapolation Forecast', ylab='Forecast and 95% CI', xlab='time',ylim=c(min(lb),max(ub)))
dum <- forecast$pred
dum[1:par1] <- x[(nx+1):lx]
lines(dum, lty=1)
lines(ub,lty=3)
lines(lb,lty=3)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Univariate ARIMA Extrapolation Forecast',9,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'time',1,header=TRUE)
a<-table.element(a,'Y[t]',1,header=TRUE)
a<-table.element(a,'F[t]',1,header=TRUE)
a<-table.element(a,'95% LB',1,header=TRUE)
a<-table.element(a,'95% UB',1,header=TRUE)
a<-table.element(a,'p-value
(H0: Y[t] = F[t])',1,header=TRUE)
a<-table.element(a,'P(F[t]>Y[t-1])',1,header=TRUE)
a<-table.element(a,'P(F[t]>Y[t-s])',1,header=TRUE)
mylab <- paste('P(F[t]>Y[',nx,sep='')
mylab <- paste(mylab,'])',sep='')
a<-table.element(a,mylab,1,header=TRUE)
a<-table.row.end(a)
for (i in (nx-par5):nx) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.row.end(a)
}
for (i in 1:fx) {
a<-table.row.start(a)
a<-table.element(a,nx+i,header=TRUE)
a<-table.element(a,round(x[nx+i],4))
a<-table.element(a,round(forecast$pred[i],4))
a<-table.element(a,round(lb[i],4))
a<-table.element(a,round(ub[i],4))
a<-table.element(a,round((1-prob.pval[i]),4))
a<-table.element(a,round((1-prob.dec[i]),4))
a<-table.element(a,round((1-prob.sdec[i]),4))
a<-table.element(a,round((1-prob.ldec[i]),4))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Univariate ARIMA Extrapolation Forecast Performance',7,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'time',1,header=TRUE)
a<-table.element(a,'% S.E.',1,header=TRUE)
a<-table.element(a,'PE',1,header=TRUE)
a<-table.element(a,'MAPE',1,header=TRUE)
a<-table.element(a,'Sq.E',1,header=TRUE)
a<-table.element(a,'MSE',1,header=TRUE)
a<-table.element(a,'RMSE',1,header=TRUE)
a<-table.row.end(a)
for (i in 1:fx) {
a<-table.row.start(a)
a<-table.element(a,nx+i,header=TRUE)
a<-table.element(a,round(perc.se[i],4))
a<-table.element(a,round(perf.pe[i],4))
a<-table.element(a,round(perf.mape1[i],4))
a<-table.element(a,round(perf.se[i],4))
a<-table.element(a,round(perf.mse1[i],4))
a<-table.element(a,round(perf.rmse[i],4))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')