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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 09:38:29 -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/t1260463171lbmb50w3bnzmrdh.htm/, Retrieved Fri, 29 Mar 2024 02:38:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65573, Retrieved Fri, 29 Mar 2024 02:38:15 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact225
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] [workshop 10] [2009-12-10 16:38:29] [6c94b261890ba36343a04d1029691995] [Current]
-   PD      [ARIMA Forecasting] [workshop 10.8] [2009-12-11 09:19:05] [f1a50df816abcbb519e7637ff6b72fa0]
-   P       [ARIMA Forecasting] [] [2009-12-14 21:39:47] [3af9fa3d2c04a43d660a9a466bdfbaa0]
- R P       [ARIMA Forecasting] [] [2009-12-20 23:15:06] [74be16979710d4c4e7c6647856088456]
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Dataseries X:
283.042
276.687
277.915
277.128
277.103
275.037
270.150
267.140
264.993
287.259
291.186
292.300
288.186
281.477
282.656
280.190
280.408
276.836
275.216
274.352
271.311
289.802
290.726
292.300
278.506
269.826
265.861
269.034
264.176
255.198
253.353
246.057
235.372
258.556
260.993
254.663
250.643
243.422
247.105
248.541
245.039
237.080
237.085
225.554
226.839
247.934
248.333
246.969
245.098
246.263
255.765
264.319
268.347
273.046
273.963
267.430
271.993
292.710
295.881
293.299




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=65573&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=65573&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65573&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[32])
20274.352-------
21271.311-------
22289.802-------
23290.726-------
24292.3-------
25278.506-------
26269.826-------
27265.861-------
28269.034-------
29264.176-------
30255.198-------
31253.353-------
32246.057-------
33235.372246.7359237.7785255.69320.00640.55900.559
34258.556251.6154238.0024265.22840.15880.990300.7882
35260.993250.3438231.6096269.07810.13260.195100.6731
36254.663250.8064229.0481272.56470.36410.17941e-040.6656
37250.643243.7391220.197267.28110.28270.18160.00190.4235
38243.422238.8688213.4106264.3270.3630.18230.00860.29
39247.105235.468207.7116263.22430.20560.28720.01590.2273
40248.541238.9722209.2587268.68580.2640.29580.02370.3201
41245.039235.2796204.0326266.52650.27020.20280.03490.2495
42237.08229.7259196.9677262.48420.330.17980.06370.1643
43237.085227.9293193.5448262.31380.30090.3010.07360.1507
44225.554222.2635186.3277258.19930.42880.20940.09720.0972
45226.839223.703184.0096263.39640.43850.46360.28220.1348
46247.934223.4796179.9527267.00650.13540.43990.05710.1547
47248.333223.1418175.199271.08470.15150.15540.06090.1744
48246.969223.2989171.8285274.76930.18370.17020.11620.1931
49245.098223.5413169.3257277.75690.21790.19850.16360.2078
50246.263223.4649166.4188280.51110.21670.22870.24650.2188
51255.765223.2976163.1916283.40350.14490.2270.21880.229
52264.319223.3294160.4124286.24640.10080.15610.21610.2395
53268.347223.4427158.0615288.82380.08910.11020.25870.2489
54273.046223.4342155.6436291.22470.07570.0970.34660.2565
55273.963223.3589153.0993293.61860.0790.08290.35090.2633
56267.43223.3563150.7181295.99440.11720.0860.47640.2701
57271.993223.4054148.5528298.25790.10160.12450.46420.2765
58292.71223.4125146.4144300.41050.03890.10810.26620.2822
59295.881223.381144.2414302.52070.03630.0430.26830.2872
60293.299223.3729142.139304.60680.04580.04010.28460.2921

\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[32]) \tabularnewline
20 & 274.352 & - & - & - & - & - & - & - \tabularnewline
21 & 271.311 & - & - & - & - & - & - & - \tabularnewline
22 & 289.802 & - & - & - & - & - & - & - \tabularnewline
23 & 290.726 & - & - & - & - & - & - & - \tabularnewline
24 & 292.3 & - & - & - & - & - & - & - \tabularnewline
25 & 278.506 & - & - & - & - & - & - & - \tabularnewline
26 & 269.826 & - & - & - & - & - & - & - \tabularnewline
27 & 265.861 & - & - & - & - & - & - & - \tabularnewline
28 & 269.034 & - & - & - & - & - & - & - \tabularnewline
29 & 264.176 & - & - & - & - & - & - & - \tabularnewline
30 & 255.198 & - & - & - & - & - & - & - \tabularnewline
31 & 253.353 & - & - & - & - & - & - & - \tabularnewline
32 & 246.057 & - & - & - & - & - & - & - \tabularnewline
33 & 235.372 & 246.7359 & 237.7785 & 255.6932 & 0.0064 & 0.559 & 0 & 0.559 \tabularnewline
34 & 258.556 & 251.6154 & 238.0024 & 265.2284 & 0.1588 & 0.9903 & 0 & 0.7882 \tabularnewline
35 & 260.993 & 250.3438 & 231.6096 & 269.0781 & 0.1326 & 0.1951 & 0 & 0.6731 \tabularnewline
36 & 254.663 & 250.8064 & 229.0481 & 272.5647 & 0.3641 & 0.1794 & 1e-04 & 0.6656 \tabularnewline
37 & 250.643 & 243.7391 & 220.197 & 267.2811 & 0.2827 & 0.1816 & 0.0019 & 0.4235 \tabularnewline
38 & 243.422 & 238.8688 & 213.4106 & 264.327 & 0.363 & 0.1823 & 0.0086 & 0.29 \tabularnewline
39 & 247.105 & 235.468 & 207.7116 & 263.2243 & 0.2056 & 0.2872 & 0.0159 & 0.2273 \tabularnewline
40 & 248.541 & 238.9722 & 209.2587 & 268.6858 & 0.264 & 0.2958 & 0.0237 & 0.3201 \tabularnewline
41 & 245.039 & 235.2796 & 204.0326 & 266.5265 & 0.2702 & 0.2028 & 0.0349 & 0.2495 \tabularnewline
42 & 237.08 & 229.7259 & 196.9677 & 262.4842 & 0.33 & 0.1798 & 0.0637 & 0.1643 \tabularnewline
43 & 237.085 & 227.9293 & 193.5448 & 262.3138 & 0.3009 & 0.301 & 0.0736 & 0.1507 \tabularnewline
44 & 225.554 & 222.2635 & 186.3277 & 258.1993 & 0.4288 & 0.2094 & 0.0972 & 0.0972 \tabularnewline
45 & 226.839 & 223.703 & 184.0096 & 263.3964 & 0.4385 & 0.4636 & 0.2822 & 0.1348 \tabularnewline
46 & 247.934 & 223.4796 & 179.9527 & 267.0065 & 0.1354 & 0.4399 & 0.0571 & 0.1547 \tabularnewline
47 & 248.333 & 223.1418 & 175.199 & 271.0847 & 0.1515 & 0.1554 & 0.0609 & 0.1744 \tabularnewline
48 & 246.969 & 223.2989 & 171.8285 & 274.7693 & 0.1837 & 0.1702 & 0.1162 & 0.1931 \tabularnewline
49 & 245.098 & 223.5413 & 169.3257 & 277.7569 & 0.2179 & 0.1985 & 0.1636 & 0.2078 \tabularnewline
50 & 246.263 & 223.4649 & 166.4188 & 280.5111 & 0.2167 & 0.2287 & 0.2465 & 0.2188 \tabularnewline
51 & 255.765 & 223.2976 & 163.1916 & 283.4035 & 0.1449 & 0.227 & 0.2188 & 0.229 \tabularnewline
52 & 264.319 & 223.3294 & 160.4124 & 286.2464 & 0.1008 & 0.1561 & 0.2161 & 0.2395 \tabularnewline
53 & 268.347 & 223.4427 & 158.0615 & 288.8238 & 0.0891 & 0.1102 & 0.2587 & 0.2489 \tabularnewline
54 & 273.046 & 223.4342 & 155.6436 & 291.2247 & 0.0757 & 0.097 & 0.3466 & 0.2565 \tabularnewline
55 & 273.963 & 223.3589 & 153.0993 & 293.6186 & 0.079 & 0.0829 & 0.3509 & 0.2633 \tabularnewline
56 & 267.43 & 223.3563 & 150.7181 & 295.9944 & 0.1172 & 0.086 & 0.4764 & 0.2701 \tabularnewline
57 & 271.993 & 223.4054 & 148.5528 & 298.2579 & 0.1016 & 0.1245 & 0.4642 & 0.2765 \tabularnewline
58 & 292.71 & 223.4125 & 146.4144 & 300.4105 & 0.0389 & 0.1081 & 0.2662 & 0.2822 \tabularnewline
59 & 295.881 & 223.381 & 144.2414 & 302.5207 & 0.0363 & 0.043 & 0.2683 & 0.2872 \tabularnewline
60 & 293.299 & 223.3729 & 142.139 & 304.6068 & 0.0458 & 0.0401 & 0.2846 & 0.2921 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65573&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[32])[/C][/ROW]
[ROW][C]20[/C][C]274.352[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]21[/C][C]271.311[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]22[/C][C]289.802[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]23[/C][C]290.726[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]24[/C][C]292.3[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]25[/C][C]278.506[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]26[/C][C]269.826[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]27[/C][C]265.861[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]28[/C][C]269.034[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]29[/C][C]264.176[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]30[/C][C]255.198[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]31[/C][C]253.353[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]32[/C][C]246.057[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]33[/C][C]235.372[/C][C]246.7359[/C][C]237.7785[/C][C]255.6932[/C][C]0.0064[/C][C]0.559[/C][C]0[/C][C]0.559[/C][/ROW]
[ROW][C]34[/C][C]258.556[/C][C]251.6154[/C][C]238.0024[/C][C]265.2284[/C][C]0.1588[/C][C]0.9903[/C][C]0[/C][C]0.7882[/C][/ROW]
[ROW][C]35[/C][C]260.993[/C][C]250.3438[/C][C]231.6096[/C][C]269.0781[/C][C]0.1326[/C][C]0.1951[/C][C]0[/C][C]0.6731[/C][/ROW]
[ROW][C]36[/C][C]254.663[/C][C]250.8064[/C][C]229.0481[/C][C]272.5647[/C][C]0.3641[/C][C]0.1794[/C][C]1e-04[/C][C]0.6656[/C][/ROW]
[ROW][C]37[/C][C]250.643[/C][C]243.7391[/C][C]220.197[/C][C]267.2811[/C][C]0.2827[/C][C]0.1816[/C][C]0.0019[/C][C]0.4235[/C][/ROW]
[ROW][C]38[/C][C]243.422[/C][C]238.8688[/C][C]213.4106[/C][C]264.327[/C][C]0.363[/C][C]0.1823[/C][C]0.0086[/C][C]0.29[/C][/ROW]
[ROW][C]39[/C][C]247.105[/C][C]235.468[/C][C]207.7116[/C][C]263.2243[/C][C]0.2056[/C][C]0.2872[/C][C]0.0159[/C][C]0.2273[/C][/ROW]
[ROW][C]40[/C][C]248.541[/C][C]238.9722[/C][C]209.2587[/C][C]268.6858[/C][C]0.264[/C][C]0.2958[/C][C]0.0237[/C][C]0.3201[/C][/ROW]
[ROW][C]41[/C][C]245.039[/C][C]235.2796[/C][C]204.0326[/C][C]266.5265[/C][C]0.2702[/C][C]0.2028[/C][C]0.0349[/C][C]0.2495[/C][/ROW]
[ROW][C]42[/C][C]237.08[/C][C]229.7259[/C][C]196.9677[/C][C]262.4842[/C][C]0.33[/C][C]0.1798[/C][C]0.0637[/C][C]0.1643[/C][/ROW]
[ROW][C]43[/C][C]237.085[/C][C]227.9293[/C][C]193.5448[/C][C]262.3138[/C][C]0.3009[/C][C]0.301[/C][C]0.0736[/C][C]0.1507[/C][/ROW]
[ROW][C]44[/C][C]225.554[/C][C]222.2635[/C][C]186.3277[/C][C]258.1993[/C][C]0.4288[/C][C]0.2094[/C][C]0.0972[/C][C]0.0972[/C][/ROW]
[ROW][C]45[/C][C]226.839[/C][C]223.703[/C][C]184.0096[/C][C]263.3964[/C][C]0.4385[/C][C]0.4636[/C][C]0.2822[/C][C]0.1348[/C][/ROW]
[ROW][C]46[/C][C]247.934[/C][C]223.4796[/C][C]179.9527[/C][C]267.0065[/C][C]0.1354[/C][C]0.4399[/C][C]0.0571[/C][C]0.1547[/C][/ROW]
[ROW][C]47[/C][C]248.333[/C][C]223.1418[/C][C]175.199[/C][C]271.0847[/C][C]0.1515[/C][C]0.1554[/C][C]0.0609[/C][C]0.1744[/C][/ROW]
[ROW][C]48[/C][C]246.969[/C][C]223.2989[/C][C]171.8285[/C][C]274.7693[/C][C]0.1837[/C][C]0.1702[/C][C]0.1162[/C][C]0.1931[/C][/ROW]
[ROW][C]49[/C][C]245.098[/C][C]223.5413[/C][C]169.3257[/C][C]277.7569[/C][C]0.2179[/C][C]0.1985[/C][C]0.1636[/C][C]0.2078[/C][/ROW]
[ROW][C]50[/C][C]246.263[/C][C]223.4649[/C][C]166.4188[/C][C]280.5111[/C][C]0.2167[/C][C]0.2287[/C][C]0.2465[/C][C]0.2188[/C][/ROW]
[ROW][C]51[/C][C]255.765[/C][C]223.2976[/C][C]163.1916[/C][C]283.4035[/C][C]0.1449[/C][C]0.227[/C][C]0.2188[/C][C]0.229[/C][/ROW]
[ROW][C]52[/C][C]264.319[/C][C]223.3294[/C][C]160.4124[/C][C]286.2464[/C][C]0.1008[/C][C]0.1561[/C][C]0.2161[/C][C]0.2395[/C][/ROW]
[ROW][C]53[/C][C]268.347[/C][C]223.4427[/C][C]158.0615[/C][C]288.8238[/C][C]0.0891[/C][C]0.1102[/C][C]0.2587[/C][C]0.2489[/C][/ROW]
[ROW][C]54[/C][C]273.046[/C][C]223.4342[/C][C]155.6436[/C][C]291.2247[/C][C]0.0757[/C][C]0.097[/C][C]0.3466[/C][C]0.2565[/C][/ROW]
[ROW][C]55[/C][C]273.963[/C][C]223.3589[/C][C]153.0993[/C][C]293.6186[/C][C]0.079[/C][C]0.0829[/C][C]0.3509[/C][C]0.2633[/C][/ROW]
[ROW][C]56[/C][C]267.43[/C][C]223.3563[/C][C]150.7181[/C][C]295.9944[/C][C]0.1172[/C][C]0.086[/C][C]0.4764[/C][C]0.2701[/C][/ROW]
[ROW][C]57[/C][C]271.993[/C][C]223.4054[/C][C]148.5528[/C][C]298.2579[/C][C]0.1016[/C][C]0.1245[/C][C]0.4642[/C][C]0.2765[/C][/ROW]
[ROW][C]58[/C][C]292.71[/C][C]223.4125[/C][C]146.4144[/C][C]300.4105[/C][C]0.0389[/C][C]0.1081[/C][C]0.2662[/C][C]0.2822[/C][/ROW]
[ROW][C]59[/C][C]295.881[/C][C]223.381[/C][C]144.2414[/C][C]302.5207[/C][C]0.0363[/C][C]0.043[/C][C]0.2683[/C][C]0.2872[/C][/ROW]
[ROW][C]60[/C][C]293.299[/C][C]223.3729[/C][C]142.139[/C][C]304.6068[/C][C]0.0458[/C][C]0.0401[/C][C]0.2846[/C][C]0.2921[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65573&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65573&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[32])
20274.352-------
21271.311-------
22289.802-------
23290.726-------
24292.3-------
25278.506-------
26269.826-------
27265.861-------
28269.034-------
29264.176-------
30255.198-------
31253.353-------
32246.057-------
33235.372246.7359237.7785255.69320.00640.55900.559
34258.556251.6154238.0024265.22840.15880.990300.7882
35260.993250.3438231.6096269.07810.13260.195100.6731
36254.663250.8064229.0481272.56470.36410.17941e-040.6656
37250.643243.7391220.197267.28110.28270.18160.00190.4235
38243.422238.8688213.4106264.3270.3630.18230.00860.29
39247.105235.468207.7116263.22430.20560.28720.01590.2273
40248.541238.9722209.2587268.68580.2640.29580.02370.3201
41245.039235.2796204.0326266.52650.27020.20280.03490.2495
42237.08229.7259196.9677262.48420.330.17980.06370.1643
43237.085227.9293193.5448262.31380.30090.3010.07360.1507
44225.554222.2635186.3277258.19930.42880.20940.09720.0972
45226.839223.703184.0096263.39640.43850.46360.28220.1348
46247.934223.4796179.9527267.00650.13540.43990.05710.1547
47248.333223.1418175.199271.08470.15150.15540.06090.1744
48246.969223.2989171.8285274.76930.18370.17020.11620.1931
49245.098223.5413169.3257277.75690.21790.19850.16360.2078
50246.263223.4649166.4188280.51110.21670.22870.24650.2188
51255.765223.2976163.1916283.40350.14490.2270.21880.229
52264.319223.3294160.4124286.24640.10080.15610.21610.2395
53268.347223.4427158.0615288.82380.08910.11020.25870.2489
54273.046223.4342155.6436291.22470.07570.0970.34660.2565
55273.963223.3589153.0993293.61860.0790.08290.35090.2633
56267.43223.3563150.7181295.99440.11720.0860.47640.2701
57271.993223.4054148.5528298.25790.10160.12450.46420.2765
58292.71223.4125146.4144300.41050.03890.10810.26620.2822
59295.881223.381144.2414302.52070.03630.0430.26830.2872
60293.299223.3729142.139304.60680.04580.04010.28460.2921







Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
330.0185-0.04610129.137200
340.02760.02760.036848.172388.65479.4157
350.03820.04250.0387113.404996.90489.844
360.04430.01540.032914.873376.39698.7405
370.04930.02830.03247.66470.65038.4054
380.05440.01910.029820.731662.33067.895
390.06010.04940.0326135.420872.7728.5307
400.06340.040.033691.561575.12078.6672
410.06780.04150.034495.246177.35698.7953
420.07280.0320.034254.082175.02948.662
430.0770.04020.034783.826575.82918.708
440.08250.01480.033110.827270.41238.3912
450.09050.0140.03169.834565.75258.1088
460.09940.10940.0372598.0177103.771410.1868
470.10960.11290.0422634.5958139.159711.7966
480.11760.1060.0462560.2732165.479312.8639
490.12370.09640.0492464.6927183.080113.5307
500.13020.1020.0521519.7521201.784114.2051
510.13730.14540.0571054.133246.644615.7049
520.14370.18350.06331680.1473318.319717.8415
530.14930.2010.06992016.3982399.180619.9795
540.15480.2220.07682461.3331492.914822.2017
550.16050.22660.08332560.773582.821724.1417
560.16590.19730.08811942.493639.474625.2878
570.17090.21750.09322360.7579708.32626.6144
580.17580.31020.10164802.1484865.780729.4242
590.18080.32460.10985256.2461028.390532.0685
600.18550.3130.11714889.65411166.292834.151

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
33 & 0.0185 & -0.0461 & 0 & 129.1372 & 0 & 0 \tabularnewline
34 & 0.0276 & 0.0276 & 0.0368 & 48.1723 & 88.6547 & 9.4157 \tabularnewline
35 & 0.0382 & 0.0425 & 0.0387 & 113.4049 & 96.9048 & 9.844 \tabularnewline
36 & 0.0443 & 0.0154 & 0.0329 & 14.8733 & 76.3969 & 8.7405 \tabularnewline
37 & 0.0493 & 0.0283 & 0.032 & 47.664 & 70.6503 & 8.4054 \tabularnewline
38 & 0.0544 & 0.0191 & 0.0298 & 20.7316 & 62.3306 & 7.895 \tabularnewline
39 & 0.0601 & 0.0494 & 0.0326 & 135.4208 & 72.772 & 8.5307 \tabularnewline
40 & 0.0634 & 0.04 & 0.0336 & 91.5615 & 75.1207 & 8.6672 \tabularnewline
41 & 0.0678 & 0.0415 & 0.0344 & 95.2461 & 77.3569 & 8.7953 \tabularnewline
42 & 0.0728 & 0.032 & 0.0342 & 54.0821 & 75.0294 & 8.662 \tabularnewline
43 & 0.077 & 0.0402 & 0.0347 & 83.8265 & 75.8291 & 8.708 \tabularnewline
44 & 0.0825 & 0.0148 & 0.0331 & 10.8272 & 70.4123 & 8.3912 \tabularnewline
45 & 0.0905 & 0.014 & 0.0316 & 9.8345 & 65.7525 & 8.1088 \tabularnewline
46 & 0.0994 & 0.1094 & 0.0372 & 598.0177 & 103.7714 & 10.1868 \tabularnewline
47 & 0.1096 & 0.1129 & 0.0422 & 634.5958 & 139.1597 & 11.7966 \tabularnewline
48 & 0.1176 & 0.106 & 0.0462 & 560.2732 & 165.4793 & 12.8639 \tabularnewline
49 & 0.1237 & 0.0964 & 0.0492 & 464.6927 & 183.0801 & 13.5307 \tabularnewline
50 & 0.1302 & 0.102 & 0.0521 & 519.7521 & 201.7841 & 14.2051 \tabularnewline
51 & 0.1373 & 0.1454 & 0.057 & 1054.133 & 246.6446 & 15.7049 \tabularnewline
52 & 0.1437 & 0.1835 & 0.0633 & 1680.1473 & 318.3197 & 17.8415 \tabularnewline
53 & 0.1493 & 0.201 & 0.0699 & 2016.3982 & 399.1806 & 19.9795 \tabularnewline
54 & 0.1548 & 0.222 & 0.0768 & 2461.3331 & 492.9148 & 22.2017 \tabularnewline
55 & 0.1605 & 0.2266 & 0.0833 & 2560.773 & 582.8217 & 24.1417 \tabularnewline
56 & 0.1659 & 0.1973 & 0.0881 & 1942.493 & 639.4746 & 25.2878 \tabularnewline
57 & 0.1709 & 0.2175 & 0.0932 & 2360.7579 & 708.326 & 26.6144 \tabularnewline
58 & 0.1758 & 0.3102 & 0.1016 & 4802.1484 & 865.7807 & 29.4242 \tabularnewline
59 & 0.1808 & 0.3246 & 0.1098 & 5256.246 & 1028.3905 & 32.0685 \tabularnewline
60 & 0.1855 & 0.313 & 0.1171 & 4889.6541 & 1166.2928 & 34.151 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65573&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]33[/C][C]0.0185[/C][C]-0.0461[/C][C]0[/C][C]129.1372[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]34[/C][C]0.0276[/C][C]0.0276[/C][C]0.0368[/C][C]48.1723[/C][C]88.6547[/C][C]9.4157[/C][/ROW]
[ROW][C]35[/C][C]0.0382[/C][C]0.0425[/C][C]0.0387[/C][C]113.4049[/C][C]96.9048[/C][C]9.844[/C][/ROW]
[ROW][C]36[/C][C]0.0443[/C][C]0.0154[/C][C]0.0329[/C][C]14.8733[/C][C]76.3969[/C][C]8.7405[/C][/ROW]
[ROW][C]37[/C][C]0.0493[/C][C]0.0283[/C][C]0.032[/C][C]47.664[/C][C]70.6503[/C][C]8.4054[/C][/ROW]
[ROW][C]38[/C][C]0.0544[/C][C]0.0191[/C][C]0.0298[/C][C]20.7316[/C][C]62.3306[/C][C]7.895[/C][/ROW]
[ROW][C]39[/C][C]0.0601[/C][C]0.0494[/C][C]0.0326[/C][C]135.4208[/C][C]72.772[/C][C]8.5307[/C][/ROW]
[ROW][C]40[/C][C]0.0634[/C][C]0.04[/C][C]0.0336[/C][C]91.5615[/C][C]75.1207[/C][C]8.6672[/C][/ROW]
[ROW][C]41[/C][C]0.0678[/C][C]0.0415[/C][C]0.0344[/C][C]95.2461[/C][C]77.3569[/C][C]8.7953[/C][/ROW]
[ROW][C]42[/C][C]0.0728[/C][C]0.032[/C][C]0.0342[/C][C]54.0821[/C][C]75.0294[/C][C]8.662[/C][/ROW]
[ROW][C]43[/C][C]0.077[/C][C]0.0402[/C][C]0.0347[/C][C]83.8265[/C][C]75.8291[/C][C]8.708[/C][/ROW]
[ROW][C]44[/C][C]0.0825[/C][C]0.0148[/C][C]0.0331[/C][C]10.8272[/C][C]70.4123[/C][C]8.3912[/C][/ROW]
[ROW][C]45[/C][C]0.0905[/C][C]0.014[/C][C]0.0316[/C][C]9.8345[/C][C]65.7525[/C][C]8.1088[/C][/ROW]
[ROW][C]46[/C][C]0.0994[/C][C]0.1094[/C][C]0.0372[/C][C]598.0177[/C][C]103.7714[/C][C]10.1868[/C][/ROW]
[ROW][C]47[/C][C]0.1096[/C][C]0.1129[/C][C]0.0422[/C][C]634.5958[/C][C]139.1597[/C][C]11.7966[/C][/ROW]
[ROW][C]48[/C][C]0.1176[/C][C]0.106[/C][C]0.0462[/C][C]560.2732[/C][C]165.4793[/C][C]12.8639[/C][/ROW]
[ROW][C]49[/C][C]0.1237[/C][C]0.0964[/C][C]0.0492[/C][C]464.6927[/C][C]183.0801[/C][C]13.5307[/C][/ROW]
[ROW][C]50[/C][C]0.1302[/C][C]0.102[/C][C]0.0521[/C][C]519.7521[/C][C]201.7841[/C][C]14.2051[/C][/ROW]
[ROW][C]51[/C][C]0.1373[/C][C]0.1454[/C][C]0.057[/C][C]1054.133[/C][C]246.6446[/C][C]15.7049[/C][/ROW]
[ROW][C]52[/C][C]0.1437[/C][C]0.1835[/C][C]0.0633[/C][C]1680.1473[/C][C]318.3197[/C][C]17.8415[/C][/ROW]
[ROW][C]53[/C][C]0.1493[/C][C]0.201[/C][C]0.0699[/C][C]2016.3982[/C][C]399.1806[/C][C]19.9795[/C][/ROW]
[ROW][C]54[/C][C]0.1548[/C][C]0.222[/C][C]0.0768[/C][C]2461.3331[/C][C]492.9148[/C][C]22.2017[/C][/ROW]
[ROW][C]55[/C][C]0.1605[/C][C]0.2266[/C][C]0.0833[/C][C]2560.773[/C][C]582.8217[/C][C]24.1417[/C][/ROW]
[ROW][C]56[/C][C]0.1659[/C][C]0.1973[/C][C]0.0881[/C][C]1942.493[/C][C]639.4746[/C][C]25.2878[/C][/ROW]
[ROW][C]57[/C][C]0.1709[/C][C]0.2175[/C][C]0.0932[/C][C]2360.7579[/C][C]708.326[/C][C]26.6144[/C][/ROW]
[ROW][C]58[/C][C]0.1758[/C][C]0.3102[/C][C]0.1016[/C][C]4802.1484[/C][C]865.7807[/C][C]29.4242[/C][/ROW]
[ROW][C]59[/C][C]0.1808[/C][C]0.3246[/C][C]0.1098[/C][C]5256.246[/C][C]1028.3905[/C][C]32.0685[/C][/ROW]
[ROW][C]60[/C][C]0.1855[/C][C]0.313[/C][C]0.1171[/C][C]4889.6541[/C][C]1166.2928[/C][C]34.151[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65573&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65573&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
330.0185-0.04610129.137200
340.02760.02760.036848.172388.65479.4157
350.03820.04250.0387113.404996.90489.844
360.04430.01540.032914.873376.39698.7405
370.04930.02830.03247.66470.65038.4054
380.05440.01910.029820.731662.33067.895
390.06010.04940.0326135.420872.7728.5307
400.06340.040.033691.561575.12078.6672
410.06780.04150.034495.246177.35698.7953
420.07280.0320.034254.082175.02948.662
430.0770.04020.034783.826575.82918.708
440.08250.01480.033110.827270.41238.3912
450.09050.0140.03169.834565.75258.1088
460.09940.10940.0372598.0177103.771410.1868
470.10960.11290.0422634.5958139.159711.7966
480.11760.1060.0462560.2732165.479312.8639
490.12370.09640.0492464.6927183.080113.5307
500.13020.1020.0521519.7521201.784114.2051
510.13730.14540.0571054.133246.644615.7049
520.14370.18350.06331680.1473318.319717.8415
530.14930.2010.06992016.3982399.180619.9795
540.15480.2220.07682461.3331492.914822.2017
550.16050.22660.08332560.773582.821724.1417
560.16590.19730.08811942.493639.474625.2878
570.17090.21750.09322360.7579708.32626.6144
580.17580.31020.10164802.1484865.780729.4242
590.18080.32460.10985256.2461028.390532.0685
600.18550.3130.11714889.65411166.292834.151



Parameters (Session):
par1 = 24 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 2 ; par7 = 0 ; par8 = 0 ; par9 = 1 ; par10 = FALSE ;
Parameters (R input):
par1 = 24 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 2 ; par7 = 0 ; par8 = 0 ; par9 = 1 ; 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')