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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 11:00:37 -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/t1260468127fnu0gyz5z3lktcr.htm/, Retrieved Thu, 28 Mar 2024 20:01:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65661, Retrieved Thu, 28 Mar 2024 20:01:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact117
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 18:00:37] [6e025b5370bdd3143fbe248190b38274] [Current]
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Dataseries X:
255
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




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=65661&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=65661&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65661&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])
319294.4-------
320292.1-------
321291.9-------
322282.5-------
323277.9-------
324287.5-------
325289.2-------
326285.6-------
327293.2-------
328290.8-------
329283.1-------
330275-------
331287.8-------
332287.8294.9056274.6344315.17670.2460.7540.60690.754
333287.4295.4533258.3102332.59650.33540.65680.57440.6568
334284297.8645247.8549347.87410.29340.65910.72650.6534
335277.8299.7465237.8695361.62360.24350.6910.75550.6474
336277.6300.5624228.1601372.96460.26710.73110.63820.6351
337304.9300.2448218.8999381.58970.45530.70730.60490.6179
338294299.2374210.4635388.01140.4540.45030.61830.5997
339300.9298.1514203.1152393.18760.47740.53410.54070.5845
340324297.4648196.8889398.04070.30250.47330.55170.5747
341332.9297.3579191.5745403.14140.25510.31080.60420.5703
342341.6297.718186.8061408.62980.2190.26710.6560.5696
343333.4298.2665182.2128414.32030.27650.23210.57020.5702
344348.2298.7216177.552419.89130.21180.28740.57010.5701
345344.7298.9172172.7609425.07350.23850.22190.5710.5686
346344.7298.8416167.9214429.76190.24620.24620.58790.5656
347329.3298.6004163.1777434.02320.32840.25230.61830.5621
348323.5298.3401158.6557438.02450.3620.3320.61450.5588
349323.2298.1753154.413441.93770.36650.36490.46350.5562
350317.4298.1495150.4293445.86960.39920.36980.5220.5546
351330.1298.2356146.6311449.840.34020.40220.48630.5537
352329.2298.367142.9318453.80220.34870.34450.37330.553
353334.9298.4761139.2674457.68490.32690.35260.33590.5523
354315.8298.5231135.6139461.43240.41770.33080.30210.5513
355315.4298.5051131.9837465.02660.42120.41930.34060.5501
356319.6298.4474128.4082468.48660.40370.42250.28320.5488
357317.3298.385124.9179471.85210.41540.40530.30040.5476
358313.8298.3454121.5281475.16280.4320.41680.30370.5465
359315.8298.3392118.2354478.44290.42460.43320.36810.5457

\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 & 294.4 & - & - & - & - & - & - & - \tabularnewline
320 & 292.1 & - & - & - & - & - & - & - \tabularnewline
321 & 291.9 & - & - & - & - & - & - & - \tabularnewline
322 & 282.5 & - & - & - & - & - & - & - \tabularnewline
323 & 277.9 & - & - & - & - & - & - & - \tabularnewline
324 & 287.5 & - & - & - & - & - & - & - \tabularnewline
325 & 289.2 & - & - & - & - & - & - & - \tabularnewline
326 & 285.6 & - & - & - & - & - & - & - \tabularnewline
327 & 293.2 & - & - & - & - & - & - & - \tabularnewline
328 & 290.8 & - & - & - & - & - & - & - \tabularnewline
329 & 283.1 & - & - & - & - & - & - & - \tabularnewline
330 & 275 & - & - & - & - & - & - & - \tabularnewline
331 & 287.8 & - & - & - & - & - & - & - \tabularnewline
332 & 287.8 & 294.9056 & 274.6344 & 315.1767 & 0.246 & 0.754 & 0.6069 & 0.754 \tabularnewline
333 & 287.4 & 295.4533 & 258.3102 & 332.5965 & 0.3354 & 0.6568 & 0.5744 & 0.6568 \tabularnewline
334 & 284 & 297.8645 & 247.8549 & 347.8741 & 0.2934 & 0.6591 & 0.7265 & 0.6534 \tabularnewline
335 & 277.8 & 299.7465 & 237.8695 & 361.6236 & 0.2435 & 0.691 & 0.7555 & 0.6474 \tabularnewline
336 & 277.6 & 300.5624 & 228.1601 & 372.9646 & 0.2671 & 0.7311 & 0.6382 & 0.6351 \tabularnewline
337 & 304.9 & 300.2448 & 218.8999 & 381.5897 & 0.4553 & 0.7073 & 0.6049 & 0.6179 \tabularnewline
338 & 294 & 299.2374 & 210.4635 & 388.0114 & 0.454 & 0.4503 & 0.6183 & 0.5997 \tabularnewline
339 & 300.9 & 298.1514 & 203.1152 & 393.1876 & 0.4774 & 0.5341 & 0.5407 & 0.5845 \tabularnewline
340 & 324 & 297.4648 & 196.8889 & 398.0407 & 0.3025 & 0.4733 & 0.5517 & 0.5747 \tabularnewline
341 & 332.9 & 297.3579 & 191.5745 & 403.1414 & 0.2551 & 0.3108 & 0.6042 & 0.5703 \tabularnewline
342 & 341.6 & 297.718 & 186.8061 & 408.6298 & 0.219 & 0.2671 & 0.656 & 0.5696 \tabularnewline
343 & 333.4 & 298.2665 & 182.2128 & 414.3203 & 0.2765 & 0.2321 & 0.5702 & 0.5702 \tabularnewline
344 & 348.2 & 298.7216 & 177.552 & 419.8913 & 0.2118 & 0.2874 & 0.5701 & 0.5701 \tabularnewline
345 & 344.7 & 298.9172 & 172.7609 & 425.0735 & 0.2385 & 0.2219 & 0.571 & 0.5686 \tabularnewline
346 & 344.7 & 298.8416 & 167.9214 & 429.7619 & 0.2462 & 0.2462 & 0.5879 & 0.5656 \tabularnewline
347 & 329.3 & 298.6004 & 163.1777 & 434.0232 & 0.3284 & 0.2523 & 0.6183 & 0.5621 \tabularnewline
348 & 323.5 & 298.3401 & 158.6557 & 438.0245 & 0.362 & 0.332 & 0.6145 & 0.5588 \tabularnewline
349 & 323.2 & 298.1753 & 154.413 & 441.9377 & 0.3665 & 0.3649 & 0.4635 & 0.5562 \tabularnewline
350 & 317.4 & 298.1495 & 150.4293 & 445.8696 & 0.3992 & 0.3698 & 0.522 & 0.5546 \tabularnewline
351 & 330.1 & 298.2356 & 146.6311 & 449.84 & 0.3402 & 0.4022 & 0.4863 & 0.5537 \tabularnewline
352 & 329.2 & 298.367 & 142.9318 & 453.8022 & 0.3487 & 0.3445 & 0.3733 & 0.553 \tabularnewline
353 & 334.9 & 298.4761 & 139.2674 & 457.6849 & 0.3269 & 0.3526 & 0.3359 & 0.5523 \tabularnewline
354 & 315.8 & 298.5231 & 135.6139 & 461.4324 & 0.4177 & 0.3308 & 0.3021 & 0.5513 \tabularnewline
355 & 315.4 & 298.5051 & 131.9837 & 465.0266 & 0.4212 & 0.4193 & 0.3406 & 0.5501 \tabularnewline
356 & 319.6 & 298.4474 & 128.4082 & 468.4866 & 0.4037 & 0.4225 & 0.2832 & 0.5488 \tabularnewline
357 & 317.3 & 298.385 & 124.9179 & 471.8521 & 0.4154 & 0.4053 & 0.3004 & 0.5476 \tabularnewline
358 & 313.8 & 298.3454 & 121.5281 & 475.1628 & 0.432 & 0.4168 & 0.3037 & 0.5465 \tabularnewline
359 & 315.8 & 298.3392 & 118.2354 & 478.4429 & 0.4246 & 0.4332 & 0.3681 & 0.5457 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65661&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]294.4[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]320[/C][C]292.1[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]321[/C][C]291.9[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]322[/C][C]282.5[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]323[/C][C]277.9[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]324[/C][C]287.5[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]325[/C][C]289.2[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]326[/C][C]285.6[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]327[/C][C]293.2[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]328[/C][C]290.8[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]329[/C][C]283.1[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]330[/C][C]275[/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]287.8[/C][C]294.9056[/C][C]274.6344[/C][C]315.1767[/C][C]0.246[/C][C]0.754[/C][C]0.6069[/C][C]0.754[/C][/ROW]
[ROW][C]333[/C][C]287.4[/C][C]295.4533[/C][C]258.3102[/C][C]332.5965[/C][C]0.3354[/C][C]0.6568[/C][C]0.5744[/C][C]0.6568[/C][/ROW]
[ROW][C]334[/C][C]284[/C][C]297.8645[/C][C]247.8549[/C][C]347.8741[/C][C]0.2934[/C][C]0.6591[/C][C]0.7265[/C][C]0.6534[/C][/ROW]
[ROW][C]335[/C][C]277.8[/C][C]299.7465[/C][C]237.8695[/C][C]361.6236[/C][C]0.2435[/C][C]0.691[/C][C]0.7555[/C][C]0.6474[/C][/ROW]
[ROW][C]336[/C][C]277.6[/C][C]300.5624[/C][C]228.1601[/C][C]372.9646[/C][C]0.2671[/C][C]0.7311[/C][C]0.6382[/C][C]0.6351[/C][/ROW]
[ROW][C]337[/C][C]304.9[/C][C]300.2448[/C][C]218.8999[/C][C]381.5897[/C][C]0.4553[/C][C]0.7073[/C][C]0.6049[/C][C]0.6179[/C][/ROW]
[ROW][C]338[/C][C]294[/C][C]299.2374[/C][C]210.4635[/C][C]388.0114[/C][C]0.454[/C][C]0.4503[/C][C]0.6183[/C][C]0.5997[/C][/ROW]
[ROW][C]339[/C][C]300.9[/C][C]298.1514[/C][C]203.1152[/C][C]393.1876[/C][C]0.4774[/C][C]0.5341[/C][C]0.5407[/C][C]0.5845[/C][/ROW]
[ROW][C]340[/C][C]324[/C][C]297.4648[/C][C]196.8889[/C][C]398.0407[/C][C]0.3025[/C][C]0.4733[/C][C]0.5517[/C][C]0.5747[/C][/ROW]
[ROW][C]341[/C][C]332.9[/C][C]297.3579[/C][C]191.5745[/C][C]403.1414[/C][C]0.2551[/C][C]0.3108[/C][C]0.6042[/C][C]0.5703[/C][/ROW]
[ROW][C]342[/C][C]341.6[/C][C]297.718[/C][C]186.8061[/C][C]408.6298[/C][C]0.219[/C][C]0.2671[/C][C]0.656[/C][C]0.5696[/C][/ROW]
[ROW][C]343[/C][C]333.4[/C][C]298.2665[/C][C]182.2128[/C][C]414.3203[/C][C]0.2765[/C][C]0.2321[/C][C]0.5702[/C][C]0.5702[/C][/ROW]
[ROW][C]344[/C][C]348.2[/C][C]298.7216[/C][C]177.552[/C][C]419.8913[/C][C]0.2118[/C][C]0.2874[/C][C]0.5701[/C][C]0.5701[/C][/ROW]
[ROW][C]345[/C][C]344.7[/C][C]298.9172[/C][C]172.7609[/C][C]425.0735[/C][C]0.2385[/C][C]0.2219[/C][C]0.571[/C][C]0.5686[/C][/ROW]
[ROW][C]346[/C][C]344.7[/C][C]298.8416[/C][C]167.9214[/C][C]429.7619[/C][C]0.2462[/C][C]0.2462[/C][C]0.5879[/C][C]0.5656[/C][/ROW]
[ROW][C]347[/C][C]329.3[/C][C]298.6004[/C][C]163.1777[/C][C]434.0232[/C][C]0.3284[/C][C]0.2523[/C][C]0.6183[/C][C]0.5621[/C][/ROW]
[ROW][C]348[/C][C]323.5[/C][C]298.3401[/C][C]158.6557[/C][C]438.0245[/C][C]0.362[/C][C]0.332[/C][C]0.6145[/C][C]0.5588[/C][/ROW]
[ROW][C]349[/C][C]323.2[/C][C]298.1753[/C][C]154.413[/C][C]441.9377[/C][C]0.3665[/C][C]0.3649[/C][C]0.4635[/C][C]0.5562[/C][/ROW]
[ROW][C]350[/C][C]317.4[/C][C]298.1495[/C][C]150.4293[/C][C]445.8696[/C][C]0.3992[/C][C]0.3698[/C][C]0.522[/C][C]0.5546[/C][/ROW]
[ROW][C]351[/C][C]330.1[/C][C]298.2356[/C][C]146.6311[/C][C]449.84[/C][C]0.3402[/C][C]0.4022[/C][C]0.4863[/C][C]0.5537[/C][/ROW]
[ROW][C]352[/C][C]329.2[/C][C]298.367[/C][C]142.9318[/C][C]453.8022[/C][C]0.3487[/C][C]0.3445[/C][C]0.3733[/C][C]0.553[/C][/ROW]
[ROW][C]353[/C][C]334.9[/C][C]298.4761[/C][C]139.2674[/C][C]457.6849[/C][C]0.3269[/C][C]0.3526[/C][C]0.3359[/C][C]0.5523[/C][/ROW]
[ROW][C]354[/C][C]315.8[/C][C]298.5231[/C][C]135.6139[/C][C]461.4324[/C][C]0.4177[/C][C]0.3308[/C][C]0.3021[/C][C]0.5513[/C][/ROW]
[ROW][C]355[/C][C]315.4[/C][C]298.5051[/C][C]131.9837[/C][C]465.0266[/C][C]0.4212[/C][C]0.4193[/C][C]0.3406[/C][C]0.5501[/C][/ROW]
[ROW][C]356[/C][C]319.6[/C][C]298.4474[/C][C]128.4082[/C][C]468.4866[/C][C]0.4037[/C][C]0.4225[/C][C]0.2832[/C][C]0.5488[/C][/ROW]
[ROW][C]357[/C][C]317.3[/C][C]298.385[/C][C]124.9179[/C][C]471.8521[/C][C]0.4154[/C][C]0.4053[/C][C]0.3004[/C][C]0.5476[/C][/ROW]
[ROW][C]358[/C][C]313.8[/C][C]298.3454[/C][C]121.5281[/C][C]475.1628[/C][C]0.432[/C][C]0.4168[/C][C]0.3037[/C][C]0.5465[/C][/ROW]
[ROW][C]359[/C][C]315.8[/C][C]298.3392[/C][C]118.2354[/C][C]478.4429[/C][C]0.4246[/C][C]0.4332[/C][C]0.3681[/C][C]0.5457[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65661&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65661&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])
319294.4-------
320292.1-------
321291.9-------
322282.5-------
323277.9-------
324287.5-------
325289.2-------
326285.6-------
327293.2-------
328290.8-------
329283.1-------
330275-------
331287.8-------
332287.8294.9056274.6344315.17670.2460.7540.60690.754
333287.4295.4533258.3102332.59650.33540.65680.57440.6568
334284297.8645247.8549347.87410.29340.65910.72650.6534
335277.8299.7465237.8695361.62360.24350.6910.75550.6474
336277.6300.5624228.1601372.96460.26710.73110.63820.6351
337304.9300.2448218.8999381.58970.45530.70730.60490.6179
338294299.2374210.4635388.01140.4540.45030.61830.5997
339300.9298.1514203.1152393.18760.47740.53410.54070.5845
340324297.4648196.8889398.04070.30250.47330.55170.5747
341332.9297.3579191.5745403.14140.25510.31080.60420.5703
342341.6297.718186.8061408.62980.2190.26710.6560.5696
343333.4298.2665182.2128414.32030.27650.23210.57020.5702
344348.2298.7216177.552419.89130.21180.28740.57010.5701
345344.7298.9172172.7609425.07350.23850.22190.5710.5686
346344.7298.8416167.9214429.76190.24620.24620.58790.5656
347329.3298.6004163.1777434.02320.32840.25230.61830.5621
348323.5298.3401158.6557438.02450.3620.3320.61450.5588
349323.2298.1753154.413441.93770.36650.36490.46350.5562
350317.4298.1495150.4293445.86960.39920.36980.5220.5546
351330.1298.2356146.6311449.840.34020.40220.48630.5537
352329.2298.367142.9318453.80220.34870.34450.37330.553
353334.9298.4761139.2674457.68490.32690.35260.33590.5523
354315.8298.5231135.6139461.43240.41770.33080.30210.5513
355315.4298.5051131.9837465.02660.42120.41930.34060.5501
356319.6298.4474128.4082468.48660.40370.42250.28320.5488
357317.3298.385124.9179471.85210.41540.40530.30040.5476
358313.8298.3454121.5281475.16280.4320.41680.30370.5465
359315.8298.3392118.2354478.44290.42460.43320.36810.5457







Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
3320.0351-0.0241050.488900
3330.0641-0.02730.025764.856357.67267.5942
3340.0857-0.04650.0326192.2241102.523110.1254
3350.1053-0.07320.0428481.6506197.30514.0465
3360.1229-0.07640.0495527.2707263.298116.2265
3370.13820.01550.043821.6709223.026914.9341
3380.1514-0.01750.040127.4308195.084613.9673
3390.16260.00920.03627.555171.643413.1013
3400.17250.08920.0421704.1149230.806915.1923
3410.18150.11950.04981263.2381334.0518.277
3420.19010.14740.05871925.6308478.739221.8801
3430.19850.11780.06361234.3594541.707523.2746
3440.2070.16560.07152448.1103688.353926.2365
3450.21530.15320.07732096.0632788.904628.0874
3460.22350.15350.08242102.9886876.510229.6059
3470.23140.10280.0837942.4628880.632229.6754
3480.23890.08430.0837633.0202866.066829.429
3490.2460.08390.0837626.2334852.742729.2018
3500.25280.06460.0827370.5834827.365928.764
3510.25940.10680.08391015.3421836.764728.9269
3520.26580.10330.0848950.6743842.18929.0205
3530.27210.1220.08651326.6982864.212129.3975
3540.27840.05790.0853298.4899839.615528.9761
3550.28460.05660.0841285.4362816.524728.5749
3560.29070.07090.0836447.4328801.76128.3154
3570.29660.06340.0828357.7777784.684728.0122
3580.30240.05180.0816238.8433764.468427.649
3590.3080.05850.0808304.8803748.054527.3506

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
332 & 0.0351 & -0.0241 & 0 & 50.4889 & 0 & 0 \tabularnewline
333 & 0.0641 & -0.0273 & 0.0257 & 64.8563 & 57.6726 & 7.5942 \tabularnewline
334 & 0.0857 & -0.0465 & 0.0326 & 192.2241 & 102.5231 & 10.1254 \tabularnewline
335 & 0.1053 & -0.0732 & 0.0428 & 481.6506 & 197.305 & 14.0465 \tabularnewline
336 & 0.1229 & -0.0764 & 0.0495 & 527.2707 & 263.2981 & 16.2265 \tabularnewline
337 & 0.1382 & 0.0155 & 0.0438 & 21.6709 & 223.0269 & 14.9341 \tabularnewline
338 & 0.1514 & -0.0175 & 0.0401 & 27.4308 & 195.0846 & 13.9673 \tabularnewline
339 & 0.1626 & 0.0092 & 0.0362 & 7.555 & 171.6434 & 13.1013 \tabularnewline
340 & 0.1725 & 0.0892 & 0.0421 & 704.1149 & 230.8069 & 15.1923 \tabularnewline
341 & 0.1815 & 0.1195 & 0.0498 & 1263.2381 & 334.05 & 18.277 \tabularnewline
342 & 0.1901 & 0.1474 & 0.0587 & 1925.6308 & 478.7392 & 21.8801 \tabularnewline
343 & 0.1985 & 0.1178 & 0.0636 & 1234.3594 & 541.7075 & 23.2746 \tabularnewline
344 & 0.207 & 0.1656 & 0.0715 & 2448.1103 & 688.3539 & 26.2365 \tabularnewline
345 & 0.2153 & 0.1532 & 0.0773 & 2096.0632 & 788.9046 & 28.0874 \tabularnewline
346 & 0.2235 & 0.1535 & 0.0824 & 2102.9886 & 876.5102 & 29.6059 \tabularnewline
347 & 0.2314 & 0.1028 & 0.0837 & 942.4628 & 880.6322 & 29.6754 \tabularnewline
348 & 0.2389 & 0.0843 & 0.0837 & 633.0202 & 866.0668 & 29.429 \tabularnewline
349 & 0.246 & 0.0839 & 0.0837 & 626.2334 & 852.7427 & 29.2018 \tabularnewline
350 & 0.2528 & 0.0646 & 0.0827 & 370.5834 & 827.3659 & 28.764 \tabularnewline
351 & 0.2594 & 0.1068 & 0.0839 & 1015.3421 & 836.7647 & 28.9269 \tabularnewline
352 & 0.2658 & 0.1033 & 0.0848 & 950.6743 & 842.189 & 29.0205 \tabularnewline
353 & 0.2721 & 0.122 & 0.0865 & 1326.6982 & 864.2121 & 29.3975 \tabularnewline
354 & 0.2784 & 0.0579 & 0.0853 & 298.4899 & 839.6155 & 28.9761 \tabularnewline
355 & 0.2846 & 0.0566 & 0.0841 & 285.4362 & 816.5247 & 28.5749 \tabularnewline
356 & 0.2907 & 0.0709 & 0.0836 & 447.4328 & 801.761 & 28.3154 \tabularnewline
357 & 0.2966 & 0.0634 & 0.0828 & 357.7777 & 784.6847 & 28.0122 \tabularnewline
358 & 0.3024 & 0.0518 & 0.0816 & 238.8433 & 764.4684 & 27.649 \tabularnewline
359 & 0.308 & 0.0585 & 0.0808 & 304.8803 & 748.0545 & 27.3506 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65661&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]0.0351[/C][C]-0.0241[/C][C]0[/C][C]50.4889[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]333[/C][C]0.0641[/C][C]-0.0273[/C][C]0.0257[/C][C]64.8563[/C][C]57.6726[/C][C]7.5942[/C][/ROW]
[ROW][C]334[/C][C]0.0857[/C][C]-0.0465[/C][C]0.0326[/C][C]192.2241[/C][C]102.5231[/C][C]10.1254[/C][/ROW]
[ROW][C]335[/C][C]0.1053[/C][C]-0.0732[/C][C]0.0428[/C][C]481.6506[/C][C]197.305[/C][C]14.0465[/C][/ROW]
[ROW][C]336[/C][C]0.1229[/C][C]-0.0764[/C][C]0.0495[/C][C]527.2707[/C][C]263.2981[/C][C]16.2265[/C][/ROW]
[ROW][C]337[/C][C]0.1382[/C][C]0.0155[/C][C]0.0438[/C][C]21.6709[/C][C]223.0269[/C][C]14.9341[/C][/ROW]
[ROW][C]338[/C][C]0.1514[/C][C]-0.0175[/C][C]0.0401[/C][C]27.4308[/C][C]195.0846[/C][C]13.9673[/C][/ROW]
[ROW][C]339[/C][C]0.1626[/C][C]0.0092[/C][C]0.0362[/C][C]7.555[/C][C]171.6434[/C][C]13.1013[/C][/ROW]
[ROW][C]340[/C][C]0.1725[/C][C]0.0892[/C][C]0.0421[/C][C]704.1149[/C][C]230.8069[/C][C]15.1923[/C][/ROW]
[ROW][C]341[/C][C]0.1815[/C][C]0.1195[/C][C]0.0498[/C][C]1263.2381[/C][C]334.05[/C][C]18.277[/C][/ROW]
[ROW][C]342[/C][C]0.1901[/C][C]0.1474[/C][C]0.0587[/C][C]1925.6308[/C][C]478.7392[/C][C]21.8801[/C][/ROW]
[ROW][C]343[/C][C]0.1985[/C][C]0.1178[/C][C]0.0636[/C][C]1234.3594[/C][C]541.7075[/C][C]23.2746[/C][/ROW]
[ROW][C]344[/C][C]0.207[/C][C]0.1656[/C][C]0.0715[/C][C]2448.1103[/C][C]688.3539[/C][C]26.2365[/C][/ROW]
[ROW][C]345[/C][C]0.2153[/C][C]0.1532[/C][C]0.0773[/C][C]2096.0632[/C][C]788.9046[/C][C]28.0874[/C][/ROW]
[ROW][C]346[/C][C]0.2235[/C][C]0.1535[/C][C]0.0824[/C][C]2102.9886[/C][C]876.5102[/C][C]29.6059[/C][/ROW]
[ROW][C]347[/C][C]0.2314[/C][C]0.1028[/C][C]0.0837[/C][C]942.4628[/C][C]880.6322[/C][C]29.6754[/C][/ROW]
[ROW][C]348[/C][C]0.2389[/C][C]0.0843[/C][C]0.0837[/C][C]633.0202[/C][C]866.0668[/C][C]29.429[/C][/ROW]
[ROW][C]349[/C][C]0.246[/C][C]0.0839[/C][C]0.0837[/C][C]626.2334[/C][C]852.7427[/C][C]29.2018[/C][/ROW]
[ROW][C]350[/C][C]0.2528[/C][C]0.0646[/C][C]0.0827[/C][C]370.5834[/C][C]827.3659[/C][C]28.764[/C][/ROW]
[ROW][C]351[/C][C]0.2594[/C][C]0.1068[/C][C]0.0839[/C][C]1015.3421[/C][C]836.7647[/C][C]28.9269[/C][/ROW]
[ROW][C]352[/C][C]0.2658[/C][C]0.1033[/C][C]0.0848[/C][C]950.6743[/C][C]842.189[/C][C]29.0205[/C][/ROW]
[ROW][C]353[/C][C]0.2721[/C][C]0.122[/C][C]0.0865[/C][C]1326.6982[/C][C]864.2121[/C][C]29.3975[/C][/ROW]
[ROW][C]354[/C][C]0.2784[/C][C]0.0579[/C][C]0.0853[/C][C]298.4899[/C][C]839.6155[/C][C]28.9761[/C][/ROW]
[ROW][C]355[/C][C]0.2846[/C][C]0.0566[/C][C]0.0841[/C][C]285.4362[/C][C]816.5247[/C][C]28.5749[/C][/ROW]
[ROW][C]356[/C][C]0.2907[/C][C]0.0709[/C][C]0.0836[/C][C]447.4328[/C][C]801.761[/C][C]28.3154[/C][/ROW]
[ROW][C]357[/C][C]0.2966[/C][C]0.0634[/C][C]0.0828[/C][C]357.7777[/C][C]784.6847[/C][C]28.0122[/C][/ROW]
[ROW][C]358[/C][C]0.3024[/C][C]0.0518[/C][C]0.0816[/C][C]238.8433[/C][C]764.4684[/C][C]27.649[/C][/ROW]
[ROW][C]359[/C][C]0.308[/C][C]0.0585[/C][C]0.0808[/C][C]304.8803[/C][C]748.0545[/C][C]27.3506[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65661&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65661&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
3320.0351-0.0241050.488900
3330.0641-0.02730.025764.856357.67267.5942
3340.0857-0.04650.0326192.2241102.523110.1254
3350.1053-0.07320.0428481.6506197.30514.0465
3360.1229-0.07640.0495527.2707263.298116.2265
3370.13820.01550.043821.6709223.026914.9341
3380.1514-0.01750.040127.4308195.084613.9673
3390.16260.00920.03627.555171.643413.1013
3400.17250.08920.0421704.1149230.806915.1923
3410.18150.11950.04981263.2381334.0518.277
3420.19010.14740.05871925.6308478.739221.8801
3430.19850.11780.06361234.3594541.707523.2746
3440.2070.16560.07152448.1103688.353926.2365
3450.21530.15320.07732096.0632788.904628.0874
3460.22350.15350.08242102.9886876.510229.6059
3470.23140.10280.0837942.4628880.632229.6754
3480.23890.08430.0837633.0202866.066829.429
3490.2460.08390.0837626.2334852.742729.2018
3500.25280.06460.0827370.5834827.365928.764
3510.25940.10680.08391015.3421836.764728.9269
3520.26580.10330.0848950.6743842.18929.0205
3530.27210.1220.08651326.6982864.212129.3975
3540.27840.05790.0853298.4899839.615528.9761
3550.28460.05660.0841285.4362816.524728.5749
3560.29070.07090.0836447.4328801.76128.3154
3570.29660.06340.0828357.7777784.684728.0122
3580.30240.05180.0816238.8433764.468427.649
3590.3080.05850.0808304.8803748.054527.3506



Parameters (Session):
par1 = 24 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 0 ; par7 = 0 ; par8 = 0 ; par9 = 0 ; par10 = FALSE ;
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')