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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 computationThu, 10 Dec 2009 12:47:52 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/10/t1260474508uyiu7eluwyo6qg1.htm/, Retrieved Thu, 28 Mar 2024 20:56:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65759, Retrieved Thu, 28 Mar 2024 20:56:38 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact99
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [ARIMA Forecasting] [] [2009-12-07 09:54:52] [b98453cac15ba1066b407e146608df68]
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Dataseries X:
299.9
339.2
374.2
393.5
389.2
381.7
375.2
369
357.4
352.1
346.5
342.9
340.3
328.3
322.9
314.3
308.9
294
285.6
281.2
280.3
278.8
274.5
270.4
263.4
259.9
258
262.7
284.7
311.3
322.1
327
331.3
333.3
321.4
327
320
314.7
316.7
314.4
321.3
318.2
307.2
301.3
287.5
277.7
274.4
258.8
253.3
251
248.4
249.5
246.1
244.5
243.6
244
240.8
249.8
248
259.4
260.5
260.8
261.3
259.5
256.6
257.9
256.5
254.2
253.3
253.8
255.5
257.1
257.3
253.2
252.8
252
250.7
252.2
250
251
253.4
251.2
255.6
261.1
258.9
259.9
261.2
264.7
267.1
266.4
267.7
268.6
267.5
268.5
268.5
270.5
270.9
270.1
269.3
269.8
270.1
264.9
263.7
264.8
263.7
255.9
276.2
360.1
380.5
373.7
369.8
366.6
359.3
345.8
326.2
324.5
328.1
327.5
324.4
316.5
310.9
301.5
291.7
290.4
287.4
277.7
281.6
288
276
272.9
283
283.3
276.8
284.5
282.7
281.2
287.4
283.1
284
285.5
289.2
292.5
296.4
305.2
303.9
311.5
316.3
316.7
322.5
317.1
309.8
303.8
290.3
293.7
291.7
296.5
289.1
288.5
293.8
297.7
305.4
302.7
302.5
303
294.5
294.1
294.5
297.1
289.4
292.4
287.9
286.6
280.5
272.4
269.2
270.6
267.3
262.5
266.8
268.8
263.1
261.2
266
262.5
265.2
261.3
253.7
249.2
239.1
236.4
235.2
245.2
246.2
247.7
251.4
253.3
254.8
250
249.3
241.5
243.3
248
253
252.9
251.5
251.6
253.5
259.8
334.1
448
445.8
445
448.2
438.2
439.8
423.4
410.8
408.4
406.7
405.9
402.7
405.1
399.6
386.5
381.4
375.2
357.7
359
355
352.7
344.4
343.8
338
339
333.3
334.4
328.3
330.7
330
331.6
351.2
389.4
410.9
442.8
462.8
466.9
461.7
439.2
430.3
416.1
402.5
397.3
403.3
395.9
387.8
378.6
377.1
370.4
362
350.3
348.2
344.6
343.5
342.8
347.6
346.6
349.5
342.1
342
342.8
339.3
348.2
333.7
334.7
354
367.7
363.3
358.4
353.1
343.1
344.6
344.4
333.9
331.7
324.3
321.2
322.4
321.7
320.5
312.8
309.7
315.6
309.7
304.6
302.5
301.5
298.8
291.3
293.6
294.6
285.9
297.6
301.1
293.8
297.7
292.9
292.1
287.2
288.2
283.8
299.9
292.4
293.3
300.8
293.7
293.1
294.4
292.1
291.9
282.5
277.9
287.5
289.2
285.6
293.2
290.8
283.1
275
287.8
287.8
287.4
284
277.8
277.6
304.9
294
300.9
324
332.9
341.6
333.4
348.2
344.7
344.7
329.3
323.5
323.2
317.4
330.1
329.2
334.9
315.8
315.4
319.6
317.3
313.8
315.8
311.3




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65759&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65759&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65759&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 time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







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[330])
318292.1-------
319291.9-------
320282.5-------
321277.9-------
322287.5-------
323289.2-------
324285.6-------
325293.2-------
326290.8-------
327283.1-------
328275-------
329287.8-------
330287.8-------
331287.4284.2825264.0557304.50930.38130.36660.23020.3666
332284285.5554248.592322.51880.46710.4610.56440.4526
333277.8286.5343237.0922335.97650.36460.540.63390.48
334277.6287.0582226.3338347.78260.38010.61750.49430.4904
335304.9286.9438216.1932357.69450.30940.60210.47510.4905
336294286.3724207.0825365.66230.42520.32350.50760.4859
337300.9285.7092199.3316372.08680.36520.42540.43250.4811
338324285.293192.9468377.63920.20570.37020.45350.4788
339332.9285.277187.6187382.93520.16960.21850.51740.4798
340341.6285.5908182.8596388.32210.14260.18340.58010.4832
341333.4286.0185178.1923393.84460.19450.15620.48710.4871
342348.2286.3308173.3285399.33320.14160.20710.48980.4898
343344.7286.3993168.2464404.55230.16670.15260.49340.4907
344344.7286.2383163.1307409.3460.1760.1760.51420.4901
345329.3285.9705158.2259413.71510.25310.18380.54990.4888
346323.5285.7461153.6996417.79260.28760.2590.54810.4878
347323.2285.6654149.5782421.75260.29440.29290.39090.4877
348317.4285.7392145.76425.71850.32880.30.4540.4885
349330.1285.9019142.0823429.72140.27350.33390.4190.4897
350329.2286.0573138.4031433.71150.28340.27940.30720.4908
351334.9286.1317134.6589437.60460.2640.28870.27250.4914
352315.8286.105130.8757441.33430.35390.26890.24170.4915
353315.4286.0097127.1345444.88490.35850.35660.27940.4912
354319.6285.9053123.5182448.29240.34210.36090.22610.4909
355317.3285.8441120.0701451.61810.3550.34490.24330.4908
356313.8285.8473116.7795454.91510.37290.35770.24750.491
357315.8285.9007113.5959458.20550.36690.37550.31080.4914
358311.3285.9688110.459461.47860.38860.36950.33760.4918

\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[330]) \tabularnewline
318 & 292.1 & - & - & - & - & - & - & - \tabularnewline
319 & 291.9 & - & - & - & - & - & - & - \tabularnewline
320 & 282.5 & - & - & - & - & - & - & - \tabularnewline
321 & 277.9 & - & - & - & - & - & - & - \tabularnewline
322 & 287.5 & - & - & - & - & - & - & - \tabularnewline
323 & 289.2 & - & - & - & - & - & - & - \tabularnewline
324 & 285.6 & - & - & - & - & - & - & - \tabularnewline
325 & 293.2 & - & - & - & - & - & - & - \tabularnewline
326 & 290.8 & - & - & - & - & - & - & - \tabularnewline
327 & 283.1 & - & - & - & - & - & - & - \tabularnewline
328 & 275 & - & - & - & - & - & - & - \tabularnewline
329 & 287.8 & - & - & - & - & - & - & - \tabularnewline
330 & 287.8 & - & - & - & - & - & - & - \tabularnewline
331 & 287.4 & 284.2825 & 264.0557 & 304.5093 & 0.3813 & 0.3666 & 0.2302 & 0.3666 \tabularnewline
332 & 284 & 285.5554 & 248.592 & 322.5188 & 0.4671 & 0.461 & 0.5644 & 0.4526 \tabularnewline
333 & 277.8 & 286.5343 & 237.0922 & 335.9765 & 0.3646 & 0.54 & 0.6339 & 0.48 \tabularnewline
334 & 277.6 & 287.0582 & 226.3338 & 347.7826 & 0.3801 & 0.6175 & 0.4943 & 0.4904 \tabularnewline
335 & 304.9 & 286.9438 & 216.1932 & 357.6945 & 0.3094 & 0.6021 & 0.4751 & 0.4905 \tabularnewline
336 & 294 & 286.3724 & 207.0825 & 365.6623 & 0.4252 & 0.3235 & 0.5076 & 0.4859 \tabularnewline
337 & 300.9 & 285.7092 & 199.3316 & 372.0868 & 0.3652 & 0.4254 & 0.4325 & 0.4811 \tabularnewline
338 & 324 & 285.293 & 192.9468 & 377.6392 & 0.2057 & 0.3702 & 0.4535 & 0.4788 \tabularnewline
339 & 332.9 & 285.277 & 187.6187 & 382.9352 & 0.1696 & 0.2185 & 0.5174 & 0.4798 \tabularnewline
340 & 341.6 & 285.5908 & 182.8596 & 388.3221 & 0.1426 & 0.1834 & 0.5801 & 0.4832 \tabularnewline
341 & 333.4 & 286.0185 & 178.1923 & 393.8446 & 0.1945 & 0.1562 & 0.4871 & 0.4871 \tabularnewline
342 & 348.2 & 286.3308 & 173.3285 & 399.3332 & 0.1416 & 0.2071 & 0.4898 & 0.4898 \tabularnewline
343 & 344.7 & 286.3993 & 168.2464 & 404.5523 & 0.1667 & 0.1526 & 0.4934 & 0.4907 \tabularnewline
344 & 344.7 & 286.2383 & 163.1307 & 409.346 & 0.176 & 0.176 & 0.5142 & 0.4901 \tabularnewline
345 & 329.3 & 285.9705 & 158.2259 & 413.7151 & 0.2531 & 0.1838 & 0.5499 & 0.4888 \tabularnewline
346 & 323.5 & 285.7461 & 153.6996 & 417.7926 & 0.2876 & 0.259 & 0.5481 & 0.4878 \tabularnewline
347 & 323.2 & 285.6654 & 149.5782 & 421.7526 & 0.2944 & 0.2929 & 0.3909 & 0.4877 \tabularnewline
348 & 317.4 & 285.7392 & 145.76 & 425.7185 & 0.3288 & 0.3 & 0.454 & 0.4885 \tabularnewline
349 & 330.1 & 285.9019 & 142.0823 & 429.7214 & 0.2735 & 0.3339 & 0.419 & 0.4897 \tabularnewline
350 & 329.2 & 286.0573 & 138.4031 & 433.7115 & 0.2834 & 0.2794 & 0.3072 & 0.4908 \tabularnewline
351 & 334.9 & 286.1317 & 134.6589 & 437.6046 & 0.264 & 0.2887 & 0.2725 & 0.4914 \tabularnewline
352 & 315.8 & 286.105 & 130.8757 & 441.3343 & 0.3539 & 0.2689 & 0.2417 & 0.4915 \tabularnewline
353 & 315.4 & 286.0097 & 127.1345 & 444.8849 & 0.3585 & 0.3566 & 0.2794 & 0.4912 \tabularnewline
354 & 319.6 & 285.9053 & 123.5182 & 448.2924 & 0.3421 & 0.3609 & 0.2261 & 0.4909 \tabularnewline
355 & 317.3 & 285.8441 & 120.0701 & 451.6181 & 0.355 & 0.3449 & 0.2433 & 0.4908 \tabularnewline
356 & 313.8 & 285.8473 & 116.7795 & 454.9151 & 0.3729 & 0.3577 & 0.2475 & 0.491 \tabularnewline
357 & 315.8 & 285.9007 & 113.5959 & 458.2055 & 0.3669 & 0.3755 & 0.3108 & 0.4914 \tabularnewline
358 & 311.3 & 285.9688 & 110.459 & 461.4786 & 0.3886 & 0.3695 & 0.3376 & 0.4918 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65759&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[330])[/C][/ROW]
[ROW][C]318[/C][C]292.1[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]319[/C][C]291.9[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]320[/C][C]282.5[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]321[/C][C]277.9[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]322[/C][C]287.5[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]323[/C][C]289.2[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]324[/C][C]285.6[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]325[/C][C]293.2[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]326[/C][C]290.8[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]327[/C][C]283.1[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]328[/C][C]275[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]329[/C][C]287.8[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]330[/C][C]287.8[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]331[/C][C]287.4[/C][C]284.2825[/C][C]264.0557[/C][C]304.5093[/C][C]0.3813[/C][C]0.3666[/C][C]0.2302[/C][C]0.3666[/C][/ROW]
[ROW][C]332[/C][C]284[/C][C]285.5554[/C][C]248.592[/C][C]322.5188[/C][C]0.4671[/C][C]0.461[/C][C]0.5644[/C][C]0.4526[/C][/ROW]
[ROW][C]333[/C][C]277.8[/C][C]286.5343[/C][C]237.0922[/C][C]335.9765[/C][C]0.3646[/C][C]0.54[/C][C]0.6339[/C][C]0.48[/C][/ROW]
[ROW][C]334[/C][C]277.6[/C][C]287.0582[/C][C]226.3338[/C][C]347.7826[/C][C]0.3801[/C][C]0.6175[/C][C]0.4943[/C][C]0.4904[/C][/ROW]
[ROW][C]335[/C][C]304.9[/C][C]286.9438[/C][C]216.1932[/C][C]357.6945[/C][C]0.3094[/C][C]0.6021[/C][C]0.4751[/C][C]0.4905[/C][/ROW]
[ROW][C]336[/C][C]294[/C][C]286.3724[/C][C]207.0825[/C][C]365.6623[/C][C]0.4252[/C][C]0.3235[/C][C]0.5076[/C][C]0.4859[/C][/ROW]
[ROW][C]337[/C][C]300.9[/C][C]285.7092[/C][C]199.3316[/C][C]372.0868[/C][C]0.3652[/C][C]0.4254[/C][C]0.4325[/C][C]0.4811[/C][/ROW]
[ROW][C]338[/C][C]324[/C][C]285.293[/C][C]192.9468[/C][C]377.6392[/C][C]0.2057[/C][C]0.3702[/C][C]0.4535[/C][C]0.4788[/C][/ROW]
[ROW][C]339[/C][C]332.9[/C][C]285.277[/C][C]187.6187[/C][C]382.9352[/C][C]0.1696[/C][C]0.2185[/C][C]0.5174[/C][C]0.4798[/C][/ROW]
[ROW][C]340[/C][C]341.6[/C][C]285.5908[/C][C]182.8596[/C][C]388.3221[/C][C]0.1426[/C][C]0.1834[/C][C]0.5801[/C][C]0.4832[/C][/ROW]
[ROW][C]341[/C][C]333.4[/C][C]286.0185[/C][C]178.1923[/C][C]393.8446[/C][C]0.1945[/C][C]0.1562[/C][C]0.4871[/C][C]0.4871[/C][/ROW]
[ROW][C]342[/C][C]348.2[/C][C]286.3308[/C][C]173.3285[/C][C]399.3332[/C][C]0.1416[/C][C]0.2071[/C][C]0.4898[/C][C]0.4898[/C][/ROW]
[ROW][C]343[/C][C]344.7[/C][C]286.3993[/C][C]168.2464[/C][C]404.5523[/C][C]0.1667[/C][C]0.1526[/C][C]0.4934[/C][C]0.4907[/C][/ROW]
[ROW][C]344[/C][C]344.7[/C][C]286.2383[/C][C]163.1307[/C][C]409.346[/C][C]0.176[/C][C]0.176[/C][C]0.5142[/C][C]0.4901[/C][/ROW]
[ROW][C]345[/C][C]329.3[/C][C]285.9705[/C][C]158.2259[/C][C]413.7151[/C][C]0.2531[/C][C]0.1838[/C][C]0.5499[/C][C]0.4888[/C][/ROW]
[ROW][C]346[/C][C]323.5[/C][C]285.7461[/C][C]153.6996[/C][C]417.7926[/C][C]0.2876[/C][C]0.259[/C][C]0.5481[/C][C]0.4878[/C][/ROW]
[ROW][C]347[/C][C]323.2[/C][C]285.6654[/C][C]149.5782[/C][C]421.7526[/C][C]0.2944[/C][C]0.2929[/C][C]0.3909[/C][C]0.4877[/C][/ROW]
[ROW][C]348[/C][C]317.4[/C][C]285.7392[/C][C]145.76[/C][C]425.7185[/C][C]0.3288[/C][C]0.3[/C][C]0.454[/C][C]0.4885[/C][/ROW]
[ROW][C]349[/C][C]330.1[/C][C]285.9019[/C][C]142.0823[/C][C]429.7214[/C][C]0.2735[/C][C]0.3339[/C][C]0.419[/C][C]0.4897[/C][/ROW]
[ROW][C]350[/C][C]329.2[/C][C]286.0573[/C][C]138.4031[/C][C]433.7115[/C][C]0.2834[/C][C]0.2794[/C][C]0.3072[/C][C]0.4908[/C][/ROW]
[ROW][C]351[/C][C]334.9[/C][C]286.1317[/C][C]134.6589[/C][C]437.6046[/C][C]0.264[/C][C]0.2887[/C][C]0.2725[/C][C]0.4914[/C][/ROW]
[ROW][C]352[/C][C]315.8[/C][C]286.105[/C][C]130.8757[/C][C]441.3343[/C][C]0.3539[/C][C]0.2689[/C][C]0.2417[/C][C]0.4915[/C][/ROW]
[ROW][C]353[/C][C]315.4[/C][C]286.0097[/C][C]127.1345[/C][C]444.8849[/C][C]0.3585[/C][C]0.3566[/C][C]0.2794[/C][C]0.4912[/C][/ROW]
[ROW][C]354[/C][C]319.6[/C][C]285.9053[/C][C]123.5182[/C][C]448.2924[/C][C]0.3421[/C][C]0.3609[/C][C]0.2261[/C][C]0.4909[/C][/ROW]
[ROW][C]355[/C][C]317.3[/C][C]285.8441[/C][C]120.0701[/C][C]451.6181[/C][C]0.355[/C][C]0.3449[/C][C]0.2433[/C][C]0.4908[/C][/ROW]
[ROW][C]356[/C][C]313.8[/C][C]285.8473[/C][C]116.7795[/C][C]454.9151[/C][C]0.3729[/C][C]0.3577[/C][C]0.2475[/C][C]0.491[/C][/ROW]
[ROW][C]357[/C][C]315.8[/C][C]285.9007[/C][C]113.5959[/C][C]458.2055[/C][C]0.3669[/C][C]0.3755[/C][C]0.3108[/C][C]0.4914[/C][/ROW]
[ROW][C]358[/C][C]311.3[/C][C]285.9688[/C][C]110.459[/C][C]461.4786[/C][C]0.3886[/C][C]0.3695[/C][C]0.3376[/C][C]0.4918[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65759&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65759&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[330])
318292.1-------
319291.9-------
320282.5-------
321277.9-------
322287.5-------
323289.2-------
324285.6-------
325293.2-------
326290.8-------
327283.1-------
328275-------
329287.8-------
330287.8-------
331287.4284.2825264.0557304.50930.38130.36660.23020.3666
332284285.5554248.592322.51880.46710.4610.56440.4526
333277.8286.5343237.0922335.97650.36460.540.63390.48
334277.6287.0582226.3338347.78260.38010.61750.49430.4904
335304.9286.9438216.1932357.69450.30940.60210.47510.4905
336294286.3724207.0825365.66230.42520.32350.50760.4859
337300.9285.7092199.3316372.08680.36520.42540.43250.4811
338324285.293192.9468377.63920.20570.37020.45350.4788
339332.9285.277187.6187382.93520.16960.21850.51740.4798
340341.6285.5908182.8596388.32210.14260.18340.58010.4832
341333.4286.0185178.1923393.84460.19450.15620.48710.4871
342348.2286.3308173.3285399.33320.14160.20710.48980.4898
343344.7286.3993168.2464404.55230.16670.15260.49340.4907
344344.7286.2383163.1307409.3460.1760.1760.51420.4901
345329.3285.9705158.2259413.71510.25310.18380.54990.4888
346323.5285.7461153.6996417.79260.28760.2590.54810.4878
347323.2285.6654149.5782421.75260.29440.29290.39090.4877
348317.4285.7392145.76425.71850.32880.30.4540.4885
349330.1285.9019142.0823429.72140.27350.33390.4190.4897
350329.2286.0573138.4031433.71150.28340.27940.30720.4908
351334.9286.1317134.6589437.60460.2640.28870.27250.4914
352315.8286.105130.8757441.33430.35390.26890.24170.4915
353315.4286.0097127.1345444.88490.35850.35660.27940.4912
354319.6285.9053123.5182448.29240.34210.36090.22610.4909
355317.3285.8441120.0701451.61810.3550.34490.24330.4908
356313.8285.8473116.7795454.91510.37290.35770.24750.491
357315.8285.9007113.5959458.20550.36690.37550.31080.4914
358311.3285.9688110.459461.47860.38860.36950.33760.4918







Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
3310.03630.01109.718600
3320.066-0.00540.00822.41926.06892.4635
3330.088-0.03050.015676.288429.47545.4291
3340.1079-0.03290.0289.457144.47086.6686
3350.12580.06260.0285322.4235100.061410.0031
3360.14130.02660.028258.180793.08139.6479
3370.15420.05320.0317230.7605112.749710.6184
3380.16510.13570.04471498.2319285.93516.9096
3390.17470.16690.05832267.9548506.159422.498
3400.18350.19610.07213137.029769.246427.7353
3410.19230.16570.08062245.0109903.406830.0567
3420.20140.21610.09193827.79331147.105633.8689
3430.21050.20360.10053398.96861320.325936.3363
3440.21940.20420.10793417.76631470.14338.3424
3450.22790.15150.11081877.44811497.296738.6949
3460.23580.13210.11211425.35411492.800338.6368
3470.24310.13140.11331408.84891487.86238.5728
3480.24990.11080.11311002.4041460.892138.2216
3490.25670.15460.11531953.47431486.817538.5593
3500.26340.15080.11711861.29211505.541238.8013
3510.27010.17040.11962378.34231547.103239.3332
3520.27680.10380.1189881.7941516.861838.9469
3530.28340.10280.1182863.79071488.467438.5807
3540.28980.11790.11821135.33221473.753538.3895
3550.29590.110.1179989.47271454.382238.1364
3560.30180.09780.1171781.35421428.496537.7955
3570.30750.10460.1166893.96581408.699137.5326
3580.31310.08860.1156641.66871381.305237.1659

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
331 & 0.0363 & 0.011 & 0 & 9.7186 & 0 & 0 \tabularnewline
332 & 0.066 & -0.0054 & 0.0082 & 2.4192 & 6.0689 & 2.4635 \tabularnewline
333 & 0.088 & -0.0305 & 0.0156 & 76.2884 & 29.4754 & 5.4291 \tabularnewline
334 & 0.1079 & -0.0329 & 0.02 & 89.4571 & 44.4708 & 6.6686 \tabularnewline
335 & 0.1258 & 0.0626 & 0.0285 & 322.4235 & 100.0614 & 10.0031 \tabularnewline
336 & 0.1413 & 0.0266 & 0.0282 & 58.1807 & 93.0813 & 9.6479 \tabularnewline
337 & 0.1542 & 0.0532 & 0.0317 & 230.7605 & 112.7497 & 10.6184 \tabularnewline
338 & 0.1651 & 0.1357 & 0.0447 & 1498.2319 & 285.935 & 16.9096 \tabularnewline
339 & 0.1747 & 0.1669 & 0.0583 & 2267.9548 & 506.1594 & 22.498 \tabularnewline
340 & 0.1835 & 0.1961 & 0.0721 & 3137.029 & 769.2464 & 27.7353 \tabularnewline
341 & 0.1923 & 0.1657 & 0.0806 & 2245.0109 & 903.4068 & 30.0567 \tabularnewline
342 & 0.2014 & 0.2161 & 0.0919 & 3827.7933 & 1147.1056 & 33.8689 \tabularnewline
343 & 0.2105 & 0.2036 & 0.1005 & 3398.9686 & 1320.3259 & 36.3363 \tabularnewline
344 & 0.2194 & 0.2042 & 0.1079 & 3417.7663 & 1470.143 & 38.3424 \tabularnewline
345 & 0.2279 & 0.1515 & 0.1108 & 1877.4481 & 1497.2967 & 38.6949 \tabularnewline
346 & 0.2358 & 0.1321 & 0.1121 & 1425.3541 & 1492.8003 & 38.6368 \tabularnewline
347 & 0.2431 & 0.1314 & 0.1133 & 1408.8489 & 1487.862 & 38.5728 \tabularnewline
348 & 0.2499 & 0.1108 & 0.1131 & 1002.404 & 1460.8921 & 38.2216 \tabularnewline
349 & 0.2567 & 0.1546 & 0.1153 & 1953.4743 & 1486.8175 & 38.5593 \tabularnewline
350 & 0.2634 & 0.1508 & 0.1171 & 1861.2921 & 1505.5412 & 38.8013 \tabularnewline
351 & 0.2701 & 0.1704 & 0.1196 & 2378.3423 & 1547.1032 & 39.3332 \tabularnewline
352 & 0.2768 & 0.1038 & 0.1189 & 881.794 & 1516.8618 & 38.9469 \tabularnewline
353 & 0.2834 & 0.1028 & 0.1182 & 863.7907 & 1488.4674 & 38.5807 \tabularnewline
354 & 0.2898 & 0.1179 & 0.1182 & 1135.3322 & 1473.7535 & 38.3895 \tabularnewline
355 & 0.2959 & 0.11 & 0.1179 & 989.4727 & 1454.3822 & 38.1364 \tabularnewline
356 & 0.3018 & 0.0978 & 0.1171 & 781.3542 & 1428.4965 & 37.7955 \tabularnewline
357 & 0.3075 & 0.1046 & 0.1166 & 893.9658 & 1408.6991 & 37.5326 \tabularnewline
358 & 0.3131 & 0.0886 & 0.1156 & 641.6687 & 1381.3052 & 37.1659 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65759&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]331[/C][C]0.0363[/C][C]0.011[/C][C]0[/C][C]9.7186[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]332[/C][C]0.066[/C][C]-0.0054[/C][C]0.0082[/C][C]2.4192[/C][C]6.0689[/C][C]2.4635[/C][/ROW]
[ROW][C]333[/C][C]0.088[/C][C]-0.0305[/C][C]0.0156[/C][C]76.2884[/C][C]29.4754[/C][C]5.4291[/C][/ROW]
[ROW][C]334[/C][C]0.1079[/C][C]-0.0329[/C][C]0.02[/C][C]89.4571[/C][C]44.4708[/C][C]6.6686[/C][/ROW]
[ROW][C]335[/C][C]0.1258[/C][C]0.0626[/C][C]0.0285[/C][C]322.4235[/C][C]100.0614[/C][C]10.0031[/C][/ROW]
[ROW][C]336[/C][C]0.1413[/C][C]0.0266[/C][C]0.0282[/C][C]58.1807[/C][C]93.0813[/C][C]9.6479[/C][/ROW]
[ROW][C]337[/C][C]0.1542[/C][C]0.0532[/C][C]0.0317[/C][C]230.7605[/C][C]112.7497[/C][C]10.6184[/C][/ROW]
[ROW][C]338[/C][C]0.1651[/C][C]0.1357[/C][C]0.0447[/C][C]1498.2319[/C][C]285.935[/C][C]16.9096[/C][/ROW]
[ROW][C]339[/C][C]0.1747[/C][C]0.1669[/C][C]0.0583[/C][C]2267.9548[/C][C]506.1594[/C][C]22.498[/C][/ROW]
[ROW][C]340[/C][C]0.1835[/C][C]0.1961[/C][C]0.0721[/C][C]3137.029[/C][C]769.2464[/C][C]27.7353[/C][/ROW]
[ROW][C]341[/C][C]0.1923[/C][C]0.1657[/C][C]0.0806[/C][C]2245.0109[/C][C]903.4068[/C][C]30.0567[/C][/ROW]
[ROW][C]342[/C][C]0.2014[/C][C]0.2161[/C][C]0.0919[/C][C]3827.7933[/C][C]1147.1056[/C][C]33.8689[/C][/ROW]
[ROW][C]343[/C][C]0.2105[/C][C]0.2036[/C][C]0.1005[/C][C]3398.9686[/C][C]1320.3259[/C][C]36.3363[/C][/ROW]
[ROW][C]344[/C][C]0.2194[/C][C]0.2042[/C][C]0.1079[/C][C]3417.7663[/C][C]1470.143[/C][C]38.3424[/C][/ROW]
[ROW][C]345[/C][C]0.2279[/C][C]0.1515[/C][C]0.1108[/C][C]1877.4481[/C][C]1497.2967[/C][C]38.6949[/C][/ROW]
[ROW][C]346[/C][C]0.2358[/C][C]0.1321[/C][C]0.1121[/C][C]1425.3541[/C][C]1492.8003[/C][C]38.6368[/C][/ROW]
[ROW][C]347[/C][C]0.2431[/C][C]0.1314[/C][C]0.1133[/C][C]1408.8489[/C][C]1487.862[/C][C]38.5728[/C][/ROW]
[ROW][C]348[/C][C]0.2499[/C][C]0.1108[/C][C]0.1131[/C][C]1002.404[/C][C]1460.8921[/C][C]38.2216[/C][/ROW]
[ROW][C]349[/C][C]0.2567[/C][C]0.1546[/C][C]0.1153[/C][C]1953.4743[/C][C]1486.8175[/C][C]38.5593[/C][/ROW]
[ROW][C]350[/C][C]0.2634[/C][C]0.1508[/C][C]0.1171[/C][C]1861.2921[/C][C]1505.5412[/C][C]38.8013[/C][/ROW]
[ROW][C]351[/C][C]0.2701[/C][C]0.1704[/C][C]0.1196[/C][C]2378.3423[/C][C]1547.1032[/C][C]39.3332[/C][/ROW]
[ROW][C]352[/C][C]0.2768[/C][C]0.1038[/C][C]0.1189[/C][C]881.794[/C][C]1516.8618[/C][C]38.9469[/C][/ROW]
[ROW][C]353[/C][C]0.2834[/C][C]0.1028[/C][C]0.1182[/C][C]863.7907[/C][C]1488.4674[/C][C]38.5807[/C][/ROW]
[ROW][C]354[/C][C]0.2898[/C][C]0.1179[/C][C]0.1182[/C][C]1135.3322[/C][C]1473.7535[/C][C]38.3895[/C][/ROW]
[ROW][C]355[/C][C]0.2959[/C][C]0.11[/C][C]0.1179[/C][C]989.4727[/C][C]1454.3822[/C][C]38.1364[/C][/ROW]
[ROW][C]356[/C][C]0.3018[/C][C]0.0978[/C][C]0.1171[/C][C]781.3542[/C][C]1428.4965[/C][C]37.7955[/C][/ROW]
[ROW][C]357[/C][C]0.3075[/C][C]0.1046[/C][C]0.1166[/C][C]893.9658[/C][C]1408.6991[/C][C]37.5326[/C][/ROW]
[ROW][C]358[/C][C]0.3131[/C][C]0.0886[/C][C]0.1156[/C][C]641.6687[/C][C]1381.3052[/C][C]37.1659[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65759&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65759&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
3310.03630.01109.718600
3320.066-0.00540.00822.41926.06892.4635
3330.088-0.03050.015676.288429.47545.4291
3340.1079-0.03290.0289.457144.47086.6686
3350.12580.06260.0285322.4235100.061410.0031
3360.14130.02660.028258.180793.08139.6479
3370.15420.05320.0317230.7605112.749710.6184
3380.16510.13570.04471498.2319285.93516.9096
3390.17470.16690.05832267.9548506.159422.498
3400.18350.19610.07213137.029769.246427.7353
3410.19230.16570.08062245.0109903.406830.0567
3420.20140.21610.09193827.79331147.105633.8689
3430.21050.20360.10053398.96861320.325936.3363
3440.21940.20420.10793417.76631470.14338.3424
3450.22790.15150.11081877.44811497.296738.6949
3460.23580.13210.11211425.35411492.800338.6368
3470.24310.13140.11331408.84891487.86238.5728
3480.24990.11080.11311002.4041460.892138.2216
3490.25670.15460.11531953.47431486.817538.5593
3500.26340.15080.11711861.29211505.541238.8013
3510.27010.17040.11962378.34231547.103239.3332
3520.27680.10380.1189881.7941516.861838.9469
3530.28340.10280.1182863.79071488.467438.5807
3540.28980.11790.11821135.33221473.753538.3895
3550.29590.110.1179989.47271454.382238.1364
3560.30180.09780.1171781.35421428.496537.7955
3570.30750.10460.1166893.96581408.699137.5326
3580.31310.08860.1156641.66871381.305237.1659



Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 0 ; par8 = 0 ; par9 = 0 ;
Parameters (R input):
par1 = 24 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 0 ; par7 = 0 ; par8 = 0 ; par9 = 0 ; par10 = FALSE ;
R code (references can be found in the software module):
par1 <- as.numeric(par1) #cut off periods
par1 <- 28
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
par6 <- 3
par7 <- as.numeric(par7) #q
par7 <- 3
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')