Free Statistics

of Irreproducible Research!

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 11:16:04 -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/t1260469005miaq9yyl61benyz.htm/, Retrieved Fri, 29 Mar 2024 11:41:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65679, Retrieved Fri, 29 Mar 2024 11:41:35 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact157
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]
- R PD  [ARIMA Forecasting] [ws10] [2009-12-09 16:10:11] [757146c69eaf0537be37c7b0c18216d8]
-   P       [ARIMA Forecasting] [ws10] [2009-12-10 18:16:04] [4563e36d4b7005634fe3557528d9fcab] [Current]
-   PD        [ARIMA Forecasting] [Arima forecast Ge...] [2009-12-11 20:49:56] [1b4c3bbe3f2ba180dd536c5a6a81a8e6]
-   P         [ARIMA Forecasting] [WS10 Forecast tes...] [2009-12-17 23:20:53] [1b4c3bbe3f2ba180dd536c5a6a81a8e6]
Feedback Forum

Post a new message
Dataseries X:
325412
326011
328282
317480
317539
313737
312276
309391
302950
300316
304035
333476
337698
335932
323931
313927
314485
313218
309664
302963
298989
298423
310631
329765
335083
327616
309119
295916
291413
291542
284678
276475
272566
264981
263290
296806
303598
286994
276427
266424
267153
268381
262522
255542
253158
243803
250741
280445
285257
270976
261076
255603
260376
263903
264291
263276
262572
256167
264221
293860
300713
287224




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65679&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]1 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=65679&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65679&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 time1 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[34])
22298423-------
23310631-------
24329765-------
25335083-------
26327616-------
27309119-------
28295916-------
29291413-------
30291542-------
31284678-------
32276475-------
33272566-------
34264981-------
35263290275098.5995265768.4789284428.72010.00660.983200.9832
36296806288258.7659273313.6256303203.90620.13120.999500.9989
37303598292535.448271822.9165313247.97950.14760.343100.9954
38286994288108.9499264016.7366312201.16320.46390.10387e-040.9701
39276427273788.8638247730.1655299847.56210.42140.16030.00390.7462
40266424262476.2446234278.6433290673.84580.39190.16610.01010.4309
41267153258583.4031227745.1668289421.63940.2930.30910.01850.3421
42268381259216.4237226032.1625292400.68490.29420.31960.02810.3667
43262522254132.5784219147.1114289118.04540.31920.21240.04350.2717
44255542247377.7243210700.9029284054.54570.33130.20920.060.1734
45253158244025.3359205506.1715282544.50040.32110.27890.07320.1431
46243803238184.0859197850.1887278517.9830.39240.23340.09640.0964
47250741246365.1744201841.482290888.86680.42360.54490.22810.2063
48280445256687.7449207677.0833305698.40650.1710.5940.05430.3701
49285257259914.2148205836.9979313991.43170.17920.22840.05670.4271
50270976256431.421198258.6102314604.23190.31210.16570.15160.3867
51261076245239.135183906.8318306571.43830.30640.20540.15950.2641
52255603236334.7824171842.2146300827.35020.27910.22610.18020.192
53260376233204.5805165268.7594301140.40150.21650.25910.16370.1796
54263903233682.7541162490.5254304874.98270.20270.23120.16970.1944
55264291229711.3412155670.5489303752.13350.180.18270.19250.1752
56263276224406.8503147658.6747301155.02580.16040.15420.21330.1501
57262572221743.9074142226.3741301261.44080.15710.1530.21940.1433
58256167217121.4829134876.3206299366.64520.17610.13940.26240.127
59264221223580.9641137186.7509309975.17730.17830.22990.26890.1738
60293860231732.1756141004.4965322459.85480.08980.24140.14630.2363
61300713234271.7761138850.6118329692.94040.08620.11050.14750.2641
62287224231516.5117131940.8102331092.21310.13640.08660.21870.255

\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[34]) \tabularnewline
22 & 298423 & - & - & - & - & - & - & - \tabularnewline
23 & 310631 & - & - & - & - & - & - & - \tabularnewline
24 & 329765 & - & - & - & - & - & - & - \tabularnewline
25 & 335083 & - & - & - & - & - & - & - \tabularnewline
26 & 327616 & - & - & - & - & - & - & - \tabularnewline
27 & 309119 & - & - & - & - & - & - & - \tabularnewline
28 & 295916 & - & - & - & - & - & - & - \tabularnewline
29 & 291413 & - & - & - & - & - & - & - \tabularnewline
30 & 291542 & - & - & - & - & - & - & - \tabularnewline
31 & 284678 & - & - & - & - & - & - & - \tabularnewline
32 & 276475 & - & - & - & - & - & - & - \tabularnewline
33 & 272566 & - & - & - & - & - & - & - \tabularnewline
34 & 264981 & - & - & - & - & - & - & - \tabularnewline
35 & 263290 & 275098.5995 & 265768.4789 & 284428.7201 & 0.0066 & 0.9832 & 0 & 0.9832 \tabularnewline
36 & 296806 & 288258.7659 & 273313.6256 & 303203.9062 & 0.1312 & 0.9995 & 0 & 0.9989 \tabularnewline
37 & 303598 & 292535.448 & 271822.9165 & 313247.9795 & 0.1476 & 0.3431 & 0 & 0.9954 \tabularnewline
38 & 286994 & 288108.9499 & 264016.7366 & 312201.1632 & 0.4639 & 0.1038 & 7e-04 & 0.9701 \tabularnewline
39 & 276427 & 273788.8638 & 247730.1655 & 299847.5621 & 0.4214 & 0.1603 & 0.0039 & 0.7462 \tabularnewline
40 & 266424 & 262476.2446 & 234278.6433 & 290673.8458 & 0.3919 & 0.1661 & 0.0101 & 0.4309 \tabularnewline
41 & 267153 & 258583.4031 & 227745.1668 & 289421.6394 & 0.293 & 0.3091 & 0.0185 & 0.3421 \tabularnewline
42 & 268381 & 259216.4237 & 226032.1625 & 292400.6849 & 0.2942 & 0.3196 & 0.0281 & 0.3667 \tabularnewline
43 & 262522 & 254132.5784 & 219147.1114 & 289118.0454 & 0.3192 & 0.2124 & 0.0435 & 0.2717 \tabularnewline
44 & 255542 & 247377.7243 & 210700.9029 & 284054.5457 & 0.3313 & 0.2092 & 0.06 & 0.1734 \tabularnewline
45 & 253158 & 244025.3359 & 205506.1715 & 282544.5004 & 0.3211 & 0.2789 & 0.0732 & 0.1431 \tabularnewline
46 & 243803 & 238184.0859 & 197850.1887 & 278517.983 & 0.3924 & 0.2334 & 0.0964 & 0.0964 \tabularnewline
47 & 250741 & 246365.1744 & 201841.482 & 290888.8668 & 0.4236 & 0.5449 & 0.2281 & 0.2063 \tabularnewline
48 & 280445 & 256687.7449 & 207677.0833 & 305698.4065 & 0.171 & 0.594 & 0.0543 & 0.3701 \tabularnewline
49 & 285257 & 259914.2148 & 205836.9979 & 313991.4317 & 0.1792 & 0.2284 & 0.0567 & 0.4271 \tabularnewline
50 & 270976 & 256431.421 & 198258.6102 & 314604.2319 & 0.3121 & 0.1657 & 0.1516 & 0.3867 \tabularnewline
51 & 261076 & 245239.135 & 183906.8318 & 306571.4383 & 0.3064 & 0.2054 & 0.1595 & 0.2641 \tabularnewline
52 & 255603 & 236334.7824 & 171842.2146 & 300827.3502 & 0.2791 & 0.2261 & 0.1802 & 0.192 \tabularnewline
53 & 260376 & 233204.5805 & 165268.7594 & 301140.4015 & 0.2165 & 0.2591 & 0.1637 & 0.1796 \tabularnewline
54 & 263903 & 233682.7541 & 162490.5254 & 304874.9827 & 0.2027 & 0.2312 & 0.1697 & 0.1944 \tabularnewline
55 & 264291 & 229711.3412 & 155670.5489 & 303752.1335 & 0.18 & 0.1827 & 0.1925 & 0.1752 \tabularnewline
56 & 263276 & 224406.8503 & 147658.6747 & 301155.0258 & 0.1604 & 0.1542 & 0.2133 & 0.1501 \tabularnewline
57 & 262572 & 221743.9074 & 142226.3741 & 301261.4408 & 0.1571 & 0.153 & 0.2194 & 0.1433 \tabularnewline
58 & 256167 & 217121.4829 & 134876.3206 & 299366.6452 & 0.1761 & 0.1394 & 0.2624 & 0.127 \tabularnewline
59 & 264221 & 223580.9641 & 137186.7509 & 309975.1773 & 0.1783 & 0.2299 & 0.2689 & 0.1738 \tabularnewline
60 & 293860 & 231732.1756 & 141004.4965 & 322459.8548 & 0.0898 & 0.2414 & 0.1463 & 0.2363 \tabularnewline
61 & 300713 & 234271.7761 & 138850.6118 & 329692.9404 & 0.0862 & 0.1105 & 0.1475 & 0.2641 \tabularnewline
62 & 287224 & 231516.5117 & 131940.8102 & 331092.2131 & 0.1364 & 0.0866 & 0.2187 & 0.255 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65679&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[34])[/C][/ROW]
[ROW][C]22[/C][C]298423[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]23[/C][C]310631[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]24[/C][C]329765[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]25[/C][C]335083[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]26[/C][C]327616[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]27[/C][C]309119[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]28[/C][C]295916[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]29[/C][C]291413[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]30[/C][C]291542[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]31[/C][C]284678[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]32[/C][C]276475[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]33[/C][C]272566[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]34[/C][C]264981[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]35[/C][C]263290[/C][C]275098.5995[/C][C]265768.4789[/C][C]284428.7201[/C][C]0.0066[/C][C]0.9832[/C][C]0[/C][C]0.9832[/C][/ROW]
[ROW][C]36[/C][C]296806[/C][C]288258.7659[/C][C]273313.6256[/C][C]303203.9062[/C][C]0.1312[/C][C]0.9995[/C][C]0[/C][C]0.9989[/C][/ROW]
[ROW][C]37[/C][C]303598[/C][C]292535.448[/C][C]271822.9165[/C][C]313247.9795[/C][C]0.1476[/C][C]0.3431[/C][C]0[/C][C]0.9954[/C][/ROW]
[ROW][C]38[/C][C]286994[/C][C]288108.9499[/C][C]264016.7366[/C][C]312201.1632[/C][C]0.4639[/C][C]0.1038[/C][C]7e-04[/C][C]0.9701[/C][/ROW]
[ROW][C]39[/C][C]276427[/C][C]273788.8638[/C][C]247730.1655[/C][C]299847.5621[/C][C]0.4214[/C][C]0.1603[/C][C]0.0039[/C][C]0.7462[/C][/ROW]
[ROW][C]40[/C][C]266424[/C][C]262476.2446[/C][C]234278.6433[/C][C]290673.8458[/C][C]0.3919[/C][C]0.1661[/C][C]0.0101[/C][C]0.4309[/C][/ROW]
[ROW][C]41[/C][C]267153[/C][C]258583.4031[/C][C]227745.1668[/C][C]289421.6394[/C][C]0.293[/C][C]0.3091[/C][C]0.0185[/C][C]0.3421[/C][/ROW]
[ROW][C]42[/C][C]268381[/C][C]259216.4237[/C][C]226032.1625[/C][C]292400.6849[/C][C]0.2942[/C][C]0.3196[/C][C]0.0281[/C][C]0.3667[/C][/ROW]
[ROW][C]43[/C][C]262522[/C][C]254132.5784[/C][C]219147.1114[/C][C]289118.0454[/C][C]0.3192[/C][C]0.2124[/C][C]0.0435[/C][C]0.2717[/C][/ROW]
[ROW][C]44[/C][C]255542[/C][C]247377.7243[/C][C]210700.9029[/C][C]284054.5457[/C][C]0.3313[/C][C]0.2092[/C][C]0.06[/C][C]0.1734[/C][/ROW]
[ROW][C]45[/C][C]253158[/C][C]244025.3359[/C][C]205506.1715[/C][C]282544.5004[/C][C]0.3211[/C][C]0.2789[/C][C]0.0732[/C][C]0.1431[/C][/ROW]
[ROW][C]46[/C][C]243803[/C][C]238184.0859[/C][C]197850.1887[/C][C]278517.983[/C][C]0.3924[/C][C]0.2334[/C][C]0.0964[/C][C]0.0964[/C][/ROW]
[ROW][C]47[/C][C]250741[/C][C]246365.1744[/C][C]201841.482[/C][C]290888.8668[/C][C]0.4236[/C][C]0.5449[/C][C]0.2281[/C][C]0.2063[/C][/ROW]
[ROW][C]48[/C][C]280445[/C][C]256687.7449[/C][C]207677.0833[/C][C]305698.4065[/C][C]0.171[/C][C]0.594[/C][C]0.0543[/C][C]0.3701[/C][/ROW]
[ROW][C]49[/C][C]285257[/C][C]259914.2148[/C][C]205836.9979[/C][C]313991.4317[/C][C]0.1792[/C][C]0.2284[/C][C]0.0567[/C][C]0.4271[/C][/ROW]
[ROW][C]50[/C][C]270976[/C][C]256431.421[/C][C]198258.6102[/C][C]314604.2319[/C][C]0.3121[/C][C]0.1657[/C][C]0.1516[/C][C]0.3867[/C][/ROW]
[ROW][C]51[/C][C]261076[/C][C]245239.135[/C][C]183906.8318[/C][C]306571.4383[/C][C]0.3064[/C][C]0.2054[/C][C]0.1595[/C][C]0.2641[/C][/ROW]
[ROW][C]52[/C][C]255603[/C][C]236334.7824[/C][C]171842.2146[/C][C]300827.3502[/C][C]0.2791[/C][C]0.2261[/C][C]0.1802[/C][C]0.192[/C][/ROW]
[ROW][C]53[/C][C]260376[/C][C]233204.5805[/C][C]165268.7594[/C][C]301140.4015[/C][C]0.2165[/C][C]0.2591[/C][C]0.1637[/C][C]0.1796[/C][/ROW]
[ROW][C]54[/C][C]263903[/C][C]233682.7541[/C][C]162490.5254[/C][C]304874.9827[/C][C]0.2027[/C][C]0.2312[/C][C]0.1697[/C][C]0.1944[/C][/ROW]
[ROW][C]55[/C][C]264291[/C][C]229711.3412[/C][C]155670.5489[/C][C]303752.1335[/C][C]0.18[/C][C]0.1827[/C][C]0.1925[/C][C]0.1752[/C][/ROW]
[ROW][C]56[/C][C]263276[/C][C]224406.8503[/C][C]147658.6747[/C][C]301155.0258[/C][C]0.1604[/C][C]0.1542[/C][C]0.2133[/C][C]0.1501[/C][/ROW]
[ROW][C]57[/C][C]262572[/C][C]221743.9074[/C][C]142226.3741[/C][C]301261.4408[/C][C]0.1571[/C][C]0.153[/C][C]0.2194[/C][C]0.1433[/C][/ROW]
[ROW][C]58[/C][C]256167[/C][C]217121.4829[/C][C]134876.3206[/C][C]299366.6452[/C][C]0.1761[/C][C]0.1394[/C][C]0.2624[/C][C]0.127[/C][/ROW]
[ROW][C]59[/C][C]264221[/C][C]223580.9641[/C][C]137186.7509[/C][C]309975.1773[/C][C]0.1783[/C][C]0.2299[/C][C]0.2689[/C][C]0.1738[/C][/ROW]
[ROW][C]60[/C][C]293860[/C][C]231732.1756[/C][C]141004.4965[/C][C]322459.8548[/C][C]0.0898[/C][C]0.2414[/C][C]0.1463[/C][C]0.2363[/C][/ROW]
[ROW][C]61[/C][C]300713[/C][C]234271.7761[/C][C]138850.6118[/C][C]329692.9404[/C][C]0.0862[/C][C]0.1105[/C][C]0.1475[/C][C]0.2641[/C][/ROW]
[ROW][C]62[/C][C]287224[/C][C]231516.5117[/C][C]131940.8102[/C][C]331092.2131[/C][C]0.1364[/C][C]0.0866[/C][C]0.2187[/C][C]0.255[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65679&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65679&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[34])
22298423-------
23310631-------
24329765-------
25335083-------
26327616-------
27309119-------
28295916-------
29291413-------
30291542-------
31284678-------
32276475-------
33272566-------
34264981-------
35263290275098.5995265768.4789284428.72010.00660.983200.9832
36296806288258.7659273313.6256303203.90620.13120.999500.9989
37303598292535.448271822.9165313247.97950.14760.343100.9954
38286994288108.9499264016.7366312201.16320.46390.10387e-040.9701
39276427273788.8638247730.1655299847.56210.42140.16030.00390.7462
40266424262476.2446234278.6433290673.84580.39190.16610.01010.4309
41267153258583.4031227745.1668289421.63940.2930.30910.01850.3421
42268381259216.4237226032.1625292400.68490.29420.31960.02810.3667
43262522254132.5784219147.1114289118.04540.31920.21240.04350.2717
44255542247377.7243210700.9029284054.54570.33130.20920.060.1734
45253158244025.3359205506.1715282544.50040.32110.27890.07320.1431
46243803238184.0859197850.1887278517.9830.39240.23340.09640.0964
47250741246365.1744201841.482290888.86680.42360.54490.22810.2063
48280445256687.7449207677.0833305698.40650.1710.5940.05430.3701
49285257259914.2148205836.9979313991.43170.17920.22840.05670.4271
50270976256431.421198258.6102314604.23190.31210.16570.15160.3867
51261076245239.135183906.8318306571.43830.30640.20540.15950.2641
52255603236334.7824171842.2146300827.35020.27910.22610.18020.192
53260376233204.5805165268.7594301140.40150.21650.25910.16370.1796
54263903233682.7541162490.5254304874.98270.20270.23120.16970.1944
55264291229711.3412155670.5489303752.13350.180.18270.19250.1752
56263276224406.8503147658.6747301155.02580.16040.15420.21330.1501
57262572221743.9074142226.3741301261.44080.15710.1530.21940.1433
58256167217121.4829134876.3206299366.64520.17610.13940.26240.127
59264221223580.9641137186.7509309975.17730.17830.22990.26890.1738
60293860231732.1756141004.4965322459.85480.08980.24140.14630.2363
61300713234271.7761138850.6118329692.94040.08620.11050.14750.2641
62287224231516.5117131940.8102331092.21310.13640.08660.21870.255







Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
350.0173-0.04290139443022.363600
360.02650.02970.036373055210.5206106249116.442110307.7212
370.03610.03780.0368122380057.0904111626096.658210565.3252
380.0427-0.00390.02861243113.316584030350.82289166.807
390.04860.00960.02486959762.590268616233.17638283.4916
400.05480.0150.023215584773.063559777656.49087731.6012
410.06080.03310.024673437990.970761729132.84517856.789
420.06530.03540.025983989458.550964511673.55838031.9159
430.07020.0330.026770382394.892265163975.92878072.4207
440.07560.0330.027366655397.898465313118.12578081.6532
450.08050.03740.028383405552.670866957884.90258182.7798
460.08640.02360.027931572196.150664009077.50658000.5673
470.09220.01780.027119147849.305560558213.79887781.9158
480.09740.09260.0318564407170.490796547424.9919825.8549
490.10620.09750.0362642256763.2638132928047.542611529.4426
500.11570.05670.0374211544777.0027137841593.133811740.5959
510.12760.06460.039250806291.7939144486575.407912020.2569
520.13920.08150.0414371264208.5661157085332.805612533.3688
530.14860.11650.0453738286039.7788187674843.698913699.4468
540.15540.12930.0495913263265.0296223954264.765514965.1016
550.16440.15050.05441195752801.8127270230385.577216438.6856
560.17450.17320.05981510810801.9781326620404.504618072.6424
570.1830.18410.06521666933141.4308384894871.327419618.7378
580.19330.17980.06991524552406.2208432380601.94820793.7635
590.19710.18180.07441651612515.7663481149878.500721935.1289
600.19980.26810.08193859866560.2027611100520.104624720.4474
610.20780.28360.08934414436233.8814751964805.800127421.9767
620.21940.24060.09473103324254.3528835941928.962728912.6604

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
35 & 0.0173 & -0.0429 & 0 & 139443022.3636 & 0 & 0 \tabularnewline
36 & 0.0265 & 0.0297 & 0.0363 & 73055210.5206 & 106249116.4421 & 10307.7212 \tabularnewline
37 & 0.0361 & 0.0378 & 0.0368 & 122380057.0904 & 111626096.6582 & 10565.3252 \tabularnewline
38 & 0.0427 & -0.0039 & 0.0286 & 1243113.3165 & 84030350.8228 & 9166.807 \tabularnewline
39 & 0.0486 & 0.0096 & 0.0248 & 6959762.5902 & 68616233.1763 & 8283.4916 \tabularnewline
40 & 0.0548 & 0.015 & 0.0232 & 15584773.0635 & 59777656.4908 & 7731.6012 \tabularnewline
41 & 0.0608 & 0.0331 & 0.0246 & 73437990.9707 & 61729132.8451 & 7856.789 \tabularnewline
42 & 0.0653 & 0.0354 & 0.0259 & 83989458.5509 & 64511673.5583 & 8031.9159 \tabularnewline
43 & 0.0702 & 0.033 & 0.0267 & 70382394.8922 & 65163975.9287 & 8072.4207 \tabularnewline
44 & 0.0756 & 0.033 & 0.0273 & 66655397.8984 & 65313118.1257 & 8081.6532 \tabularnewline
45 & 0.0805 & 0.0374 & 0.0283 & 83405552.6708 & 66957884.9025 & 8182.7798 \tabularnewline
46 & 0.0864 & 0.0236 & 0.0279 & 31572196.1506 & 64009077.5065 & 8000.5673 \tabularnewline
47 & 0.0922 & 0.0178 & 0.0271 & 19147849.3055 & 60558213.7988 & 7781.9158 \tabularnewline
48 & 0.0974 & 0.0926 & 0.0318 & 564407170.4907 & 96547424.991 & 9825.8549 \tabularnewline
49 & 0.1062 & 0.0975 & 0.0362 & 642256763.2638 & 132928047.5426 & 11529.4426 \tabularnewline
50 & 0.1157 & 0.0567 & 0.0374 & 211544777.0027 & 137841593.1338 & 11740.5959 \tabularnewline
51 & 0.1276 & 0.0646 & 0.039 & 250806291.7939 & 144486575.4079 & 12020.2569 \tabularnewline
52 & 0.1392 & 0.0815 & 0.0414 & 371264208.5661 & 157085332.8056 & 12533.3688 \tabularnewline
53 & 0.1486 & 0.1165 & 0.0453 & 738286039.7788 & 187674843.6989 & 13699.4468 \tabularnewline
54 & 0.1554 & 0.1293 & 0.0495 & 913263265.0296 & 223954264.7655 & 14965.1016 \tabularnewline
55 & 0.1644 & 0.1505 & 0.0544 & 1195752801.8127 & 270230385.5772 & 16438.6856 \tabularnewline
56 & 0.1745 & 0.1732 & 0.0598 & 1510810801.9781 & 326620404.5046 & 18072.6424 \tabularnewline
57 & 0.183 & 0.1841 & 0.0652 & 1666933141.4308 & 384894871.3274 & 19618.7378 \tabularnewline
58 & 0.1933 & 0.1798 & 0.0699 & 1524552406.2208 & 432380601.948 & 20793.7635 \tabularnewline
59 & 0.1971 & 0.1818 & 0.0744 & 1651612515.7663 & 481149878.5007 & 21935.1289 \tabularnewline
60 & 0.1998 & 0.2681 & 0.0819 & 3859866560.2027 & 611100520.1046 & 24720.4474 \tabularnewline
61 & 0.2078 & 0.2836 & 0.0893 & 4414436233.8814 & 751964805.8001 & 27421.9767 \tabularnewline
62 & 0.2194 & 0.2406 & 0.0947 & 3103324254.3528 & 835941928.9627 & 28912.6604 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65679&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]35[/C][C]0.0173[/C][C]-0.0429[/C][C]0[/C][C]139443022.3636[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]36[/C][C]0.0265[/C][C]0.0297[/C][C]0.0363[/C][C]73055210.5206[/C][C]106249116.4421[/C][C]10307.7212[/C][/ROW]
[ROW][C]37[/C][C]0.0361[/C][C]0.0378[/C][C]0.0368[/C][C]122380057.0904[/C][C]111626096.6582[/C][C]10565.3252[/C][/ROW]
[ROW][C]38[/C][C]0.0427[/C][C]-0.0039[/C][C]0.0286[/C][C]1243113.3165[/C][C]84030350.8228[/C][C]9166.807[/C][/ROW]
[ROW][C]39[/C][C]0.0486[/C][C]0.0096[/C][C]0.0248[/C][C]6959762.5902[/C][C]68616233.1763[/C][C]8283.4916[/C][/ROW]
[ROW][C]40[/C][C]0.0548[/C][C]0.015[/C][C]0.0232[/C][C]15584773.0635[/C][C]59777656.4908[/C][C]7731.6012[/C][/ROW]
[ROW][C]41[/C][C]0.0608[/C][C]0.0331[/C][C]0.0246[/C][C]73437990.9707[/C][C]61729132.8451[/C][C]7856.789[/C][/ROW]
[ROW][C]42[/C][C]0.0653[/C][C]0.0354[/C][C]0.0259[/C][C]83989458.5509[/C][C]64511673.5583[/C][C]8031.9159[/C][/ROW]
[ROW][C]43[/C][C]0.0702[/C][C]0.033[/C][C]0.0267[/C][C]70382394.8922[/C][C]65163975.9287[/C][C]8072.4207[/C][/ROW]
[ROW][C]44[/C][C]0.0756[/C][C]0.033[/C][C]0.0273[/C][C]66655397.8984[/C][C]65313118.1257[/C][C]8081.6532[/C][/ROW]
[ROW][C]45[/C][C]0.0805[/C][C]0.0374[/C][C]0.0283[/C][C]83405552.6708[/C][C]66957884.9025[/C][C]8182.7798[/C][/ROW]
[ROW][C]46[/C][C]0.0864[/C][C]0.0236[/C][C]0.0279[/C][C]31572196.1506[/C][C]64009077.5065[/C][C]8000.5673[/C][/ROW]
[ROW][C]47[/C][C]0.0922[/C][C]0.0178[/C][C]0.0271[/C][C]19147849.3055[/C][C]60558213.7988[/C][C]7781.9158[/C][/ROW]
[ROW][C]48[/C][C]0.0974[/C][C]0.0926[/C][C]0.0318[/C][C]564407170.4907[/C][C]96547424.991[/C][C]9825.8549[/C][/ROW]
[ROW][C]49[/C][C]0.1062[/C][C]0.0975[/C][C]0.0362[/C][C]642256763.2638[/C][C]132928047.5426[/C][C]11529.4426[/C][/ROW]
[ROW][C]50[/C][C]0.1157[/C][C]0.0567[/C][C]0.0374[/C][C]211544777.0027[/C][C]137841593.1338[/C][C]11740.5959[/C][/ROW]
[ROW][C]51[/C][C]0.1276[/C][C]0.0646[/C][C]0.039[/C][C]250806291.7939[/C][C]144486575.4079[/C][C]12020.2569[/C][/ROW]
[ROW][C]52[/C][C]0.1392[/C][C]0.0815[/C][C]0.0414[/C][C]371264208.5661[/C][C]157085332.8056[/C][C]12533.3688[/C][/ROW]
[ROW][C]53[/C][C]0.1486[/C][C]0.1165[/C][C]0.0453[/C][C]738286039.7788[/C][C]187674843.6989[/C][C]13699.4468[/C][/ROW]
[ROW][C]54[/C][C]0.1554[/C][C]0.1293[/C][C]0.0495[/C][C]913263265.0296[/C][C]223954264.7655[/C][C]14965.1016[/C][/ROW]
[ROW][C]55[/C][C]0.1644[/C][C]0.1505[/C][C]0.0544[/C][C]1195752801.8127[/C][C]270230385.5772[/C][C]16438.6856[/C][/ROW]
[ROW][C]56[/C][C]0.1745[/C][C]0.1732[/C][C]0.0598[/C][C]1510810801.9781[/C][C]326620404.5046[/C][C]18072.6424[/C][/ROW]
[ROW][C]57[/C][C]0.183[/C][C]0.1841[/C][C]0.0652[/C][C]1666933141.4308[/C][C]384894871.3274[/C][C]19618.7378[/C][/ROW]
[ROW][C]58[/C][C]0.1933[/C][C]0.1798[/C][C]0.0699[/C][C]1524552406.2208[/C][C]432380601.948[/C][C]20793.7635[/C][/ROW]
[ROW][C]59[/C][C]0.1971[/C][C]0.1818[/C][C]0.0744[/C][C]1651612515.7663[/C][C]481149878.5007[/C][C]21935.1289[/C][/ROW]
[ROW][C]60[/C][C]0.1998[/C][C]0.2681[/C][C]0.0819[/C][C]3859866560.2027[/C][C]611100520.1046[/C][C]24720.4474[/C][/ROW]
[ROW][C]61[/C][C]0.2078[/C][C]0.2836[/C][C]0.0893[/C][C]4414436233.8814[/C][C]751964805.8001[/C][C]27421.9767[/C][/ROW]
[ROW][C]62[/C][C]0.2194[/C][C]0.2406[/C][C]0.0947[/C][C]3103324254.3528[/C][C]835941928.9627[/C][C]28912.6604[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65679&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65679&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
350.0173-0.04290139443022.363600
360.02650.02970.036373055210.5206106249116.442110307.7212
370.03610.03780.0368122380057.0904111626096.658210565.3252
380.0427-0.00390.02861243113.316584030350.82289166.807
390.04860.00960.02486959762.590268616233.17638283.4916
400.05480.0150.023215584773.063559777656.49087731.6012
410.06080.03310.024673437990.970761729132.84517856.789
420.06530.03540.025983989458.550964511673.55838031.9159
430.07020.0330.026770382394.892265163975.92878072.4207
440.07560.0330.027366655397.898465313118.12578081.6532
450.08050.03740.028383405552.670866957884.90258182.7798
460.08640.02360.027931572196.150664009077.50658000.5673
470.09220.01780.027119147849.305560558213.79887781.9158
480.09740.09260.0318564407170.490796547424.9919825.8549
490.10620.09750.0362642256763.2638132928047.542611529.4426
500.11570.05670.0374211544777.0027137841593.133811740.5959
510.12760.06460.039250806291.7939144486575.407912020.2569
520.13920.08150.0414371264208.5661157085332.805612533.3688
530.14860.11650.0453738286039.7788187674843.698913699.4468
540.15540.12930.0495913263265.0296223954264.765514965.1016
550.16440.15050.05441195752801.8127270230385.577216438.6856
560.17450.17320.05981510810801.9781326620404.504618072.6424
570.1830.18410.06521666933141.4308384894871.327419618.7378
580.19330.17980.06991524552406.2208432380601.94820793.7635
590.19710.18180.07441651612515.7663481149878.500721935.1289
600.19980.26810.08193859866560.2027611100520.104624720.4474
610.20780.28360.08934414436233.8814751964805.800127421.9767
620.21940.24060.09473103324254.3528835941928.962728912.6604



Parameters (Session):
par1 = 1 ; par2 = 1 ; par3 = 1 ; par4 = 12 ; par5 = 1 ; par6 = 1 ; par7 = 1 ; par8 = 3 ;
Parameters (R input):
par1 = 12 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 0 ; par8 = 1 ; 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')