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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 11:54:37 -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/t12604713211ajhhm3t8qw3g0f.htm/, Retrieved Fri, 29 Mar 2024 06:58:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65724, Retrieved Fri, 29 Mar 2024 06:58:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact103
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]
-   PD    [ARIMA Forecasting] [] [2009-12-10 18:54:37] [5858ea01c9bd81debbf921a11363ad90] [Current]
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Dataseries X:
280.2
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=65724&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=65724&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65724&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[331])
319292.1-------
320291.9-------
321282.5-------
322277.9-------
323287.5-------
324289.2-------
325285.6-------
326293.2-------
327290.8-------
328283.1-------
329275-------
330287.8-------
331287.8-------
332287.4284.296264.0989304.49310.38160.36690.23030.3669
333284285.5797248.6627322.49670.46660.46150.56490.4531
334277.8286.5592237.159335.95930.36410.54040.63440.4804
335277.6287.0835226.3871347.77980.37970.61780.49460.4908
336304.9286.9713216.2354357.70720.30970.60240.47540.4908
337294286.4031207.1182365.6880.42550.32370.50790.4862
338300.9285.7415199.3633372.11970.36540.42570.43280.4814
339324285.3225192.9746377.67040.20590.37050.45370.479
340332.9285.2997187.6436382.95570.16970.21870.51760.48
341341.6285.606182.8853388.32670.14270.18340.58020.4833
342333.4286.0296178.2251393.83410.19460.15620.48720.4872
343348.2286.344173.3735399.31450.14160.20710.48990.4899
344344.7286.4196168.3044404.53480.16670.15260.49350.4909
345344.7286.2672163.1975409.33680.1760.1760.51440.4903
346329.3286.0047158.294413.71550.25320.18380.55010.489
347323.5285.78153.762417.7980.28770.25910.54830.488
348323.2285.6935149.6317421.75520.29450.2930.3910.4879
349317.4285.7595145.8066425.71240.32880.30.45410.4886
350330.1285.9164142.1282429.70460.27350.33390.41910.4898
351329.2286.0709138.4549433.68690.28340.27940.30730.4908
352334.9286.1493134.7206437.57790.2640.28870.27260.4915
353315.8286.1289130.9469441.31090.35390.26890.24180.4916
354315.4286.0389127.2108444.86710.35860.35670.27950.4913
355319.6285.9363123.594448.27870.34220.3610.22610.491
356317.3285.8728120.1412451.60440.35510.3450.24330.4909
357313.8285.8712116.8452454.89720.3730.35780.24760.4911
358315.8285.9201113.6588458.18140.36690.37550.31080.4915
359311.3285.9861110.5233461.44890.38870.36960.33760.4919

\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[331]) \tabularnewline
319 & 292.1 & - & - & - & - & - & - & - \tabularnewline
320 & 291.9 & - & - & - & - & - & - & - \tabularnewline
321 & 282.5 & - & - & - & - & - & - & - \tabularnewline
322 & 277.9 & - & - & - & - & - & - & - \tabularnewline
323 & 287.5 & - & - & - & - & - & - & - \tabularnewline
324 & 289.2 & - & - & - & - & - & - & - \tabularnewline
325 & 285.6 & - & - & - & - & - & - & - \tabularnewline
326 & 293.2 & - & - & - & - & - & - & - \tabularnewline
327 & 290.8 & - & - & - & - & - & - & - \tabularnewline
328 & 283.1 & - & - & - & - & - & - & - \tabularnewline
329 & 275 & - & - & - & - & - & - & - \tabularnewline
330 & 287.8 & - & - & - & - & - & - & - \tabularnewline
331 & 287.8 & - & - & - & - & - & - & - \tabularnewline
332 & 287.4 & 284.296 & 264.0989 & 304.4931 & 0.3816 & 0.3669 & 0.2303 & 0.3669 \tabularnewline
333 & 284 & 285.5797 & 248.6627 & 322.4967 & 0.4666 & 0.4615 & 0.5649 & 0.4531 \tabularnewline
334 & 277.8 & 286.5592 & 237.159 & 335.9593 & 0.3641 & 0.5404 & 0.6344 & 0.4804 \tabularnewline
335 & 277.6 & 287.0835 & 226.3871 & 347.7798 & 0.3797 & 0.6178 & 0.4946 & 0.4908 \tabularnewline
336 & 304.9 & 286.9713 & 216.2354 & 357.7072 & 0.3097 & 0.6024 & 0.4754 & 0.4908 \tabularnewline
337 & 294 & 286.4031 & 207.1182 & 365.688 & 0.4255 & 0.3237 & 0.5079 & 0.4862 \tabularnewline
338 & 300.9 & 285.7415 & 199.3633 & 372.1197 & 0.3654 & 0.4257 & 0.4328 & 0.4814 \tabularnewline
339 & 324 & 285.3225 & 192.9746 & 377.6704 & 0.2059 & 0.3705 & 0.4537 & 0.479 \tabularnewline
340 & 332.9 & 285.2997 & 187.6436 & 382.9557 & 0.1697 & 0.2187 & 0.5176 & 0.48 \tabularnewline
341 & 341.6 & 285.606 & 182.8853 & 388.3267 & 0.1427 & 0.1834 & 0.5802 & 0.4833 \tabularnewline
342 & 333.4 & 286.0296 & 178.2251 & 393.8341 & 0.1946 & 0.1562 & 0.4872 & 0.4872 \tabularnewline
343 & 348.2 & 286.344 & 173.3735 & 399.3145 & 0.1416 & 0.2071 & 0.4899 & 0.4899 \tabularnewline
344 & 344.7 & 286.4196 & 168.3044 & 404.5348 & 0.1667 & 0.1526 & 0.4935 & 0.4909 \tabularnewline
345 & 344.7 & 286.2672 & 163.1975 & 409.3368 & 0.176 & 0.176 & 0.5144 & 0.4903 \tabularnewline
346 & 329.3 & 286.0047 & 158.294 & 413.7155 & 0.2532 & 0.1838 & 0.5501 & 0.489 \tabularnewline
347 & 323.5 & 285.78 & 153.762 & 417.798 & 0.2877 & 0.2591 & 0.5483 & 0.488 \tabularnewline
348 & 323.2 & 285.6935 & 149.6317 & 421.7552 & 0.2945 & 0.293 & 0.391 & 0.4879 \tabularnewline
349 & 317.4 & 285.7595 & 145.8066 & 425.7124 & 0.3288 & 0.3 & 0.4541 & 0.4886 \tabularnewline
350 & 330.1 & 285.9164 & 142.1282 & 429.7046 & 0.2735 & 0.3339 & 0.4191 & 0.4898 \tabularnewline
351 & 329.2 & 286.0709 & 138.4549 & 433.6869 & 0.2834 & 0.2794 & 0.3073 & 0.4908 \tabularnewline
352 & 334.9 & 286.1493 & 134.7206 & 437.5779 & 0.264 & 0.2887 & 0.2726 & 0.4915 \tabularnewline
353 & 315.8 & 286.1289 & 130.9469 & 441.3109 & 0.3539 & 0.2689 & 0.2418 & 0.4916 \tabularnewline
354 & 315.4 & 286.0389 & 127.2108 & 444.8671 & 0.3586 & 0.3567 & 0.2795 & 0.4913 \tabularnewline
355 & 319.6 & 285.9363 & 123.594 & 448.2787 & 0.3422 & 0.361 & 0.2261 & 0.491 \tabularnewline
356 & 317.3 & 285.8728 & 120.1412 & 451.6044 & 0.3551 & 0.345 & 0.2433 & 0.4909 \tabularnewline
357 & 313.8 & 285.8712 & 116.8452 & 454.8972 & 0.373 & 0.3578 & 0.2476 & 0.4911 \tabularnewline
358 & 315.8 & 285.9201 & 113.6588 & 458.1814 & 0.3669 & 0.3755 & 0.3108 & 0.4915 \tabularnewline
359 & 311.3 & 285.9861 & 110.5233 & 461.4489 & 0.3887 & 0.3696 & 0.3376 & 0.4919 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65724&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[331])[/C][/ROW]
[ROW][C]319[/C][C]292.1[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]320[/C][C]291.9[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]321[/C][C]282.5[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]322[/C][C]277.9[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]323[/C][C]287.5[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]324[/C][C]289.2[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]325[/C][C]285.6[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]326[/C][C]293.2[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]327[/C][C]290.8[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]328[/C][C]283.1[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]329[/C][C]275[/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]287.8[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]332[/C][C]287.4[/C][C]284.296[/C][C]264.0989[/C][C]304.4931[/C][C]0.3816[/C][C]0.3669[/C][C]0.2303[/C][C]0.3669[/C][/ROW]
[ROW][C]333[/C][C]284[/C][C]285.5797[/C][C]248.6627[/C][C]322.4967[/C][C]0.4666[/C][C]0.4615[/C][C]0.5649[/C][C]0.4531[/C][/ROW]
[ROW][C]334[/C][C]277.8[/C][C]286.5592[/C][C]237.159[/C][C]335.9593[/C][C]0.3641[/C][C]0.5404[/C][C]0.6344[/C][C]0.4804[/C][/ROW]
[ROW][C]335[/C][C]277.6[/C][C]287.0835[/C][C]226.3871[/C][C]347.7798[/C][C]0.3797[/C][C]0.6178[/C][C]0.4946[/C][C]0.4908[/C][/ROW]
[ROW][C]336[/C][C]304.9[/C][C]286.9713[/C][C]216.2354[/C][C]357.7072[/C][C]0.3097[/C][C]0.6024[/C][C]0.4754[/C][C]0.4908[/C][/ROW]
[ROW][C]337[/C][C]294[/C][C]286.4031[/C][C]207.1182[/C][C]365.688[/C][C]0.4255[/C][C]0.3237[/C][C]0.5079[/C][C]0.4862[/C][/ROW]
[ROW][C]338[/C][C]300.9[/C][C]285.7415[/C][C]199.3633[/C][C]372.1197[/C][C]0.3654[/C][C]0.4257[/C][C]0.4328[/C][C]0.4814[/C][/ROW]
[ROW][C]339[/C][C]324[/C][C]285.3225[/C][C]192.9746[/C][C]377.6704[/C][C]0.2059[/C][C]0.3705[/C][C]0.4537[/C][C]0.479[/C][/ROW]
[ROW][C]340[/C][C]332.9[/C][C]285.2997[/C][C]187.6436[/C][C]382.9557[/C][C]0.1697[/C][C]0.2187[/C][C]0.5176[/C][C]0.48[/C][/ROW]
[ROW][C]341[/C][C]341.6[/C][C]285.606[/C][C]182.8853[/C][C]388.3267[/C][C]0.1427[/C][C]0.1834[/C][C]0.5802[/C][C]0.4833[/C][/ROW]
[ROW][C]342[/C][C]333.4[/C][C]286.0296[/C][C]178.2251[/C][C]393.8341[/C][C]0.1946[/C][C]0.1562[/C][C]0.4872[/C][C]0.4872[/C][/ROW]
[ROW][C]343[/C][C]348.2[/C][C]286.344[/C][C]173.3735[/C][C]399.3145[/C][C]0.1416[/C][C]0.2071[/C][C]0.4899[/C][C]0.4899[/C][/ROW]
[ROW][C]344[/C][C]344.7[/C][C]286.4196[/C][C]168.3044[/C][C]404.5348[/C][C]0.1667[/C][C]0.1526[/C][C]0.4935[/C][C]0.4909[/C][/ROW]
[ROW][C]345[/C][C]344.7[/C][C]286.2672[/C][C]163.1975[/C][C]409.3368[/C][C]0.176[/C][C]0.176[/C][C]0.5144[/C][C]0.4903[/C][/ROW]
[ROW][C]346[/C][C]329.3[/C][C]286.0047[/C][C]158.294[/C][C]413.7155[/C][C]0.2532[/C][C]0.1838[/C][C]0.5501[/C][C]0.489[/C][/ROW]
[ROW][C]347[/C][C]323.5[/C][C]285.78[/C][C]153.762[/C][C]417.798[/C][C]0.2877[/C][C]0.2591[/C][C]0.5483[/C][C]0.488[/C][/ROW]
[ROW][C]348[/C][C]323.2[/C][C]285.6935[/C][C]149.6317[/C][C]421.7552[/C][C]0.2945[/C][C]0.293[/C][C]0.391[/C][C]0.4879[/C][/ROW]
[ROW][C]349[/C][C]317.4[/C][C]285.7595[/C][C]145.8066[/C][C]425.7124[/C][C]0.3288[/C][C]0.3[/C][C]0.4541[/C][C]0.4886[/C][/ROW]
[ROW][C]350[/C][C]330.1[/C][C]285.9164[/C][C]142.1282[/C][C]429.7046[/C][C]0.2735[/C][C]0.3339[/C][C]0.4191[/C][C]0.4898[/C][/ROW]
[ROW][C]351[/C][C]329.2[/C][C]286.0709[/C][C]138.4549[/C][C]433.6869[/C][C]0.2834[/C][C]0.2794[/C][C]0.3073[/C][C]0.4908[/C][/ROW]
[ROW][C]352[/C][C]334.9[/C][C]286.1493[/C][C]134.7206[/C][C]437.5779[/C][C]0.264[/C][C]0.2887[/C][C]0.2726[/C][C]0.4915[/C][/ROW]
[ROW][C]353[/C][C]315.8[/C][C]286.1289[/C][C]130.9469[/C][C]441.3109[/C][C]0.3539[/C][C]0.2689[/C][C]0.2418[/C][C]0.4916[/C][/ROW]
[ROW][C]354[/C][C]315.4[/C][C]286.0389[/C][C]127.2108[/C][C]444.8671[/C][C]0.3586[/C][C]0.3567[/C][C]0.2795[/C][C]0.4913[/C][/ROW]
[ROW][C]355[/C][C]319.6[/C][C]285.9363[/C][C]123.594[/C][C]448.2787[/C][C]0.3422[/C][C]0.361[/C][C]0.2261[/C][C]0.491[/C][/ROW]
[ROW][C]356[/C][C]317.3[/C][C]285.8728[/C][C]120.1412[/C][C]451.6044[/C][C]0.3551[/C][C]0.345[/C][C]0.2433[/C][C]0.4909[/C][/ROW]
[ROW][C]357[/C][C]313.8[/C][C]285.8712[/C][C]116.8452[/C][C]454.8972[/C][C]0.373[/C][C]0.3578[/C][C]0.2476[/C][C]0.4911[/C][/ROW]
[ROW][C]358[/C][C]315.8[/C][C]285.9201[/C][C]113.6588[/C][C]458.1814[/C][C]0.3669[/C][C]0.3755[/C][C]0.3108[/C][C]0.4915[/C][/ROW]
[ROW][C]359[/C][C]311.3[/C][C]285.9861[/C][C]110.5233[/C][C]461.4489[/C][C]0.3887[/C][C]0.3696[/C][C]0.3376[/C][C]0.4919[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65724&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65724&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[331])
319292.1-------
320291.9-------
321282.5-------
322277.9-------
323287.5-------
324289.2-------
325285.6-------
326293.2-------
327290.8-------
328283.1-------
329275-------
330287.8-------
331287.8-------
332287.4284.296264.0989304.49310.38160.36690.23030.3669
333284285.5797248.6627322.49670.46660.46150.56490.4531
334277.8286.5592237.159335.95930.36410.54040.63440.4804
335277.6287.0835226.3871347.77980.37970.61780.49460.4908
336304.9286.9713216.2354357.70720.30970.60240.47540.4908
337294286.4031207.1182365.6880.42550.32370.50790.4862
338300.9285.7415199.3633372.11970.36540.42570.43280.4814
339324285.3225192.9746377.67040.20590.37050.45370.479
340332.9285.2997187.6436382.95570.16970.21870.51760.48
341341.6285.606182.8853388.32670.14270.18340.58020.4833
342333.4286.0296178.2251393.83410.19460.15620.48720.4872
343348.2286.344173.3735399.31450.14160.20710.48990.4899
344344.7286.4196168.3044404.53480.16670.15260.49350.4909
345344.7286.2672163.1975409.33680.1760.1760.51440.4903
346329.3286.0047158.294413.71550.25320.18380.55010.489
347323.5285.78153.762417.7980.28770.25910.54830.488
348323.2285.6935149.6317421.75520.29450.2930.3910.4879
349317.4285.7595145.8066425.71240.32880.30.45410.4886
350330.1285.9164142.1282429.70460.27350.33390.41910.4898
351329.2286.0709138.4549433.68690.28340.27940.30730.4908
352334.9286.1493134.7206437.57790.2640.28870.27260.4915
353315.8286.1289130.9469441.31090.35390.26890.24180.4916
354315.4286.0389127.2108444.86710.35860.35670.27950.4913
355319.6285.9363123.594448.27870.34220.3610.22610.491
356317.3285.8728120.1412451.60440.35510.3450.24330.4909
357313.8285.8712116.8452454.89720.3730.35780.24760.4911
358315.8285.9201113.6588458.18140.36690.37550.31080.4915
359311.3285.9861110.5233461.44890.38870.36960.33760.4919







Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
3320.03620.010909.635100
3330.066-0.00550.00822.49556.06532.4628
3340.088-0.03060.015776.723129.61795.4422
3350.1079-0.0330.0289.936144.69756.6856
3360.12580.06250.0285321.4393100.045810.0023
3370.14120.02650.028257.712692.99039.6431
3380.15420.0530.0317229.781112.531810.6081
3390.16510.13560.04471495.9489285.45916.8955
3400.17460.16680.05832265.7915505.495922.4832
3410.18350.19610.07213135.3285768.479227.7215
3420.19230.16560.08062243.9548902.613330.0435
3430.20130.2160.09183826.16881146.242933.8562
3440.21040.20350.10043396.60361319.347636.3228
3450.21930.20410.10783414.39461468.993838.3275
3460.22780.15140.11071874.47871496.026138.6785
3470.23570.1320.11211422.79821491.449438.6193
3480.2430.13130.11321406.74051486.466538.5547
3490.24990.11070.11311001.12181459.502938.2034
3500.25660.15450.11521952.19381485.43438.5413
3510.26330.15080.1171860.12251504.168438.7836
3520.270.17040.11962376.63491545.714539.3156
3530.27670.10370.1188880.37581515.471838.9291
3540.28330.10260.1181862.07231487.063138.5625
3550.28970.11770.11811133.24341472.320638.3708
3560.29580.10990.1178987.66851452.934638.1174
3570.30170.09770.117780.01771427.053137.7764
3580.30740.10450.1166892.80861407.266337.5135
3590.3130.08850.1156640.79491379.892337.1469

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
332 & 0.0362 & 0.0109 & 0 & 9.6351 & 0 & 0 \tabularnewline
333 & 0.066 & -0.0055 & 0.0082 & 2.4955 & 6.0653 & 2.4628 \tabularnewline
334 & 0.088 & -0.0306 & 0.0157 & 76.7231 & 29.6179 & 5.4422 \tabularnewline
335 & 0.1079 & -0.033 & 0.02 & 89.9361 & 44.6975 & 6.6856 \tabularnewline
336 & 0.1258 & 0.0625 & 0.0285 & 321.4393 & 100.0458 & 10.0023 \tabularnewline
337 & 0.1412 & 0.0265 & 0.0282 & 57.7126 & 92.9903 & 9.6431 \tabularnewline
338 & 0.1542 & 0.053 & 0.0317 & 229.781 & 112.5318 & 10.6081 \tabularnewline
339 & 0.1651 & 0.1356 & 0.0447 & 1495.9489 & 285.459 & 16.8955 \tabularnewline
340 & 0.1746 & 0.1668 & 0.0583 & 2265.7915 & 505.4959 & 22.4832 \tabularnewline
341 & 0.1835 & 0.1961 & 0.0721 & 3135.3285 & 768.4792 & 27.7215 \tabularnewline
342 & 0.1923 & 0.1656 & 0.0806 & 2243.9548 & 902.6133 & 30.0435 \tabularnewline
343 & 0.2013 & 0.216 & 0.0918 & 3826.1688 & 1146.2429 & 33.8562 \tabularnewline
344 & 0.2104 & 0.2035 & 0.1004 & 3396.6036 & 1319.3476 & 36.3228 \tabularnewline
345 & 0.2193 & 0.2041 & 0.1078 & 3414.3946 & 1468.9938 & 38.3275 \tabularnewline
346 & 0.2278 & 0.1514 & 0.1107 & 1874.4787 & 1496.0261 & 38.6785 \tabularnewline
347 & 0.2357 & 0.132 & 0.1121 & 1422.7982 & 1491.4494 & 38.6193 \tabularnewline
348 & 0.243 & 0.1313 & 0.1132 & 1406.7405 & 1486.4665 & 38.5547 \tabularnewline
349 & 0.2499 & 0.1107 & 0.1131 & 1001.1218 & 1459.5029 & 38.2034 \tabularnewline
350 & 0.2566 & 0.1545 & 0.1152 & 1952.1938 & 1485.434 & 38.5413 \tabularnewline
351 & 0.2633 & 0.1508 & 0.117 & 1860.1225 & 1504.1684 & 38.7836 \tabularnewline
352 & 0.27 & 0.1704 & 0.1196 & 2376.6349 & 1545.7145 & 39.3156 \tabularnewline
353 & 0.2767 & 0.1037 & 0.1188 & 880.3758 & 1515.4718 & 38.9291 \tabularnewline
354 & 0.2833 & 0.1026 & 0.1181 & 862.0723 & 1487.0631 & 38.5625 \tabularnewline
355 & 0.2897 & 0.1177 & 0.1181 & 1133.2434 & 1472.3206 & 38.3708 \tabularnewline
356 & 0.2958 & 0.1099 & 0.1178 & 987.6685 & 1452.9346 & 38.1174 \tabularnewline
357 & 0.3017 & 0.0977 & 0.117 & 780.0177 & 1427.0531 & 37.7764 \tabularnewline
358 & 0.3074 & 0.1045 & 0.1166 & 892.8086 & 1407.2663 & 37.5135 \tabularnewline
359 & 0.313 & 0.0885 & 0.1156 & 640.7949 & 1379.8923 & 37.1469 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65724&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]332[/C][C]0.0362[/C][C]0.0109[/C][C]0[/C][C]9.6351[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]333[/C][C]0.066[/C][C]-0.0055[/C][C]0.0082[/C][C]2.4955[/C][C]6.0653[/C][C]2.4628[/C][/ROW]
[ROW][C]334[/C][C]0.088[/C][C]-0.0306[/C][C]0.0157[/C][C]76.7231[/C][C]29.6179[/C][C]5.4422[/C][/ROW]
[ROW][C]335[/C][C]0.1079[/C][C]-0.033[/C][C]0.02[/C][C]89.9361[/C][C]44.6975[/C][C]6.6856[/C][/ROW]
[ROW][C]336[/C][C]0.1258[/C][C]0.0625[/C][C]0.0285[/C][C]321.4393[/C][C]100.0458[/C][C]10.0023[/C][/ROW]
[ROW][C]337[/C][C]0.1412[/C][C]0.0265[/C][C]0.0282[/C][C]57.7126[/C][C]92.9903[/C][C]9.6431[/C][/ROW]
[ROW][C]338[/C][C]0.1542[/C][C]0.053[/C][C]0.0317[/C][C]229.781[/C][C]112.5318[/C][C]10.6081[/C][/ROW]
[ROW][C]339[/C][C]0.1651[/C][C]0.1356[/C][C]0.0447[/C][C]1495.9489[/C][C]285.459[/C][C]16.8955[/C][/ROW]
[ROW][C]340[/C][C]0.1746[/C][C]0.1668[/C][C]0.0583[/C][C]2265.7915[/C][C]505.4959[/C][C]22.4832[/C][/ROW]
[ROW][C]341[/C][C]0.1835[/C][C]0.1961[/C][C]0.0721[/C][C]3135.3285[/C][C]768.4792[/C][C]27.7215[/C][/ROW]
[ROW][C]342[/C][C]0.1923[/C][C]0.1656[/C][C]0.0806[/C][C]2243.9548[/C][C]902.6133[/C][C]30.0435[/C][/ROW]
[ROW][C]343[/C][C]0.2013[/C][C]0.216[/C][C]0.0918[/C][C]3826.1688[/C][C]1146.2429[/C][C]33.8562[/C][/ROW]
[ROW][C]344[/C][C]0.2104[/C][C]0.2035[/C][C]0.1004[/C][C]3396.6036[/C][C]1319.3476[/C][C]36.3228[/C][/ROW]
[ROW][C]345[/C][C]0.2193[/C][C]0.2041[/C][C]0.1078[/C][C]3414.3946[/C][C]1468.9938[/C][C]38.3275[/C][/ROW]
[ROW][C]346[/C][C]0.2278[/C][C]0.1514[/C][C]0.1107[/C][C]1874.4787[/C][C]1496.0261[/C][C]38.6785[/C][/ROW]
[ROW][C]347[/C][C]0.2357[/C][C]0.132[/C][C]0.1121[/C][C]1422.7982[/C][C]1491.4494[/C][C]38.6193[/C][/ROW]
[ROW][C]348[/C][C]0.243[/C][C]0.1313[/C][C]0.1132[/C][C]1406.7405[/C][C]1486.4665[/C][C]38.5547[/C][/ROW]
[ROW][C]349[/C][C]0.2499[/C][C]0.1107[/C][C]0.1131[/C][C]1001.1218[/C][C]1459.5029[/C][C]38.2034[/C][/ROW]
[ROW][C]350[/C][C]0.2566[/C][C]0.1545[/C][C]0.1152[/C][C]1952.1938[/C][C]1485.434[/C][C]38.5413[/C][/ROW]
[ROW][C]351[/C][C]0.2633[/C][C]0.1508[/C][C]0.117[/C][C]1860.1225[/C][C]1504.1684[/C][C]38.7836[/C][/ROW]
[ROW][C]352[/C][C]0.27[/C][C]0.1704[/C][C]0.1196[/C][C]2376.6349[/C][C]1545.7145[/C][C]39.3156[/C][/ROW]
[ROW][C]353[/C][C]0.2767[/C][C]0.1037[/C][C]0.1188[/C][C]880.3758[/C][C]1515.4718[/C][C]38.9291[/C][/ROW]
[ROW][C]354[/C][C]0.2833[/C][C]0.1026[/C][C]0.1181[/C][C]862.0723[/C][C]1487.0631[/C][C]38.5625[/C][/ROW]
[ROW][C]355[/C][C]0.2897[/C][C]0.1177[/C][C]0.1181[/C][C]1133.2434[/C][C]1472.3206[/C][C]38.3708[/C][/ROW]
[ROW][C]356[/C][C]0.2958[/C][C]0.1099[/C][C]0.1178[/C][C]987.6685[/C][C]1452.9346[/C][C]38.1174[/C][/ROW]
[ROW][C]357[/C][C]0.3017[/C][C]0.0977[/C][C]0.117[/C][C]780.0177[/C][C]1427.0531[/C][C]37.7764[/C][/ROW]
[ROW][C]358[/C][C]0.3074[/C][C]0.1045[/C][C]0.1166[/C][C]892.8086[/C][C]1407.2663[/C][C]37.5135[/C][/ROW]
[ROW][C]359[/C][C]0.313[/C][C]0.0885[/C][C]0.1156[/C][C]640.7949[/C][C]1379.8923[/C][C]37.1469[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65724&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65724&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
3320.03620.010909.635100
3330.066-0.00550.00822.49556.06532.4628
3340.088-0.03060.015776.723129.61795.4422
3350.1079-0.0330.0289.936144.69756.6856
3360.12580.06250.0285321.4393100.045810.0023
3370.14120.02650.028257.712692.99039.6431
3380.15420.0530.0317229.781112.531810.6081
3390.16510.13560.04471495.9489285.45916.8955
3400.17460.16680.05832265.7915505.495922.4832
3410.18350.19610.07213135.3285768.479227.7215
3420.19230.16560.08062243.9548902.613330.0435
3430.20130.2160.09183826.16881146.242933.8562
3440.21040.20350.10043396.60361319.347636.3228
3450.21930.20410.10783414.39461468.993838.3275
3460.22780.15140.11071874.47871496.026138.6785
3470.23570.1320.11211422.79821491.449438.6193
3480.2430.13130.11321406.74051486.466538.5547
3490.24990.11070.11311001.12181459.502938.2034
3500.25660.15450.11521952.19381485.43438.5413
3510.26330.15080.1171860.12251504.168438.7836
3520.270.17040.11962376.63491545.714539.3156
3530.27670.10370.1188880.37581515.471838.9291
3540.28330.10260.1181862.07231487.063138.5625
3550.28970.11770.11811133.24341472.320638.3708
3560.29580.10990.1178987.66851452.934638.1174
3570.30170.09770.117780.01771427.053137.7764
3580.30740.10450.1166892.80861407.266337.5135
3590.3130.08850.1156640.79491379.892337.1469



Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; 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')