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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 computationSun, 22 Jan 2017 18:57:25 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Jan/22/t14851079065skkyj8kc4wzeli.htm/, Retrieved Thu, 31 Oct 2024 23:54:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=303493, Retrieved Thu, 31 Oct 2024 23:54:52 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact86
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [ARIMA Forecasting] [] [2017-01-22 17:57:25] [94ac3c9a028ddd47e8862e80eac9f626] [Current]
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Dataseries X:
980
1154
1014
1362
1328
1512
1656
1868
2170
1366
1134
1734
1292
1890
2066
2036
2376
2176
2122
2638
2366
2260
1846
1800
1408
1342
1510
1540
1536
2044
2666
2356
2592
2980
2178
2298
1654
1790
1894
1966
1772
1924
2472
2792
2720
2324
2244
2638
2598
2488
2576
2554
1520
2490
2762
2908
3882
3138
2550
2748
2484
2774
3182
2948
2296
3114
2358
2480
2616
1970
1882
1770
2862
2926
2622
2236
1906
2240
2316
1750
2384
2296
2046
2568
2366
2220
2374
1886
2234
2096
2666
2920
4850
2738
1922
2628
2106
1636
1148
1300
1116
1582
1272
1712
1440
1712
1626
1210
1372
1354
1202
1080
926
1590
2080
2268
1956
1670
1182
992
1300
1250
1278
1302
1278
2388




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time1 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303493&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]1 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=303493&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303493&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center







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[102])
902096-------
912666-------
922920-------
934850-------
942738-------
951922-------
962628-------
972106-------
981636-------
991148-------
1001300-------
1011116-------
1021582-------
10312721702.0441079.95172539.70830.15720.61060.01210.6106
10417121776.42581020.232857.47770.45350.81980.01910.6378
10514401821.7513998.76353030.83410.2680.570600.6512
10617121849.1038986.75453136.0340.41730.73340.08790.6579
10716261865.516977.16373205.55040.3630.58880.46710.6608
10812101875.3306968.08483255.36160.17230.63840.14250.6615
10913721881.188959.05063293.91940.240.82410.37760.661
11013541884.6796949.99883325.89890.23520.75720.63240.6597
11112021886.7595940.96453353.96050.18020.76170.83820.658
11210801887.998931.9933379.64670.14420.81630.78010.6562
1139261888.7352923.11863403.8620.10650.85230.84130.6542
11415901889.174914.36333427.14160.35150.89020.65230.6523
11520801889.4351905.73943449.80410.40540.64660.7810.6503
11622681889.5906897.25293472.04040.31960.40680.5870.6484
11719561889.683888.90563493.96590.46770.3220.70860.6465
11816701889.7381880.69683515.6510.39550.46820.58480.6447
11911821889.7708872.62453537.13950.19990.60310.62320.6429
1209921889.7903864.68563558.45890.14580.79710.78770.6411
12113001889.8019856.87683579.62740.2470.85110.72590.6395
12212501889.8088849.19453600.65730.23180.75040.73030.6378
12312781889.8129841.63523621.55750.24430.76550.78190.6362
12413021889.8154834.19543642.33450.25550.75310.81740.6347
12512781889.8168826.87163662.99360.24940.74210.85660.6332
12623881889.8177819.66053683.53930.29310.74810.62840.6317

\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[102]) \tabularnewline
90 & 2096 & - & - & - & - & - & - & - \tabularnewline
91 & 2666 & - & - & - & - & - & - & - \tabularnewline
92 & 2920 & - & - & - & - & - & - & - \tabularnewline
93 & 4850 & - & - & - & - & - & - & - \tabularnewline
94 & 2738 & - & - & - & - & - & - & - \tabularnewline
95 & 1922 & - & - & - & - & - & - & - \tabularnewline
96 & 2628 & - & - & - & - & - & - & - \tabularnewline
97 & 2106 & - & - & - & - & - & - & - \tabularnewline
98 & 1636 & - & - & - & - & - & - & - \tabularnewline
99 & 1148 & - & - & - & - & - & - & - \tabularnewline
100 & 1300 & - & - & - & - & - & - & - \tabularnewline
101 & 1116 & - & - & - & - & - & - & - \tabularnewline
102 & 1582 & - & - & - & - & - & - & - \tabularnewline
103 & 1272 & 1702.044 & 1079.9517 & 2539.7083 & 0.1572 & 0.6106 & 0.0121 & 0.6106 \tabularnewline
104 & 1712 & 1776.4258 & 1020.23 & 2857.4777 & 0.4535 & 0.8198 & 0.0191 & 0.6378 \tabularnewline
105 & 1440 & 1821.7513 & 998.7635 & 3030.8341 & 0.268 & 0.5706 & 0 & 0.6512 \tabularnewline
106 & 1712 & 1849.1038 & 986.7545 & 3136.034 & 0.4173 & 0.7334 & 0.0879 & 0.6579 \tabularnewline
107 & 1626 & 1865.516 & 977.1637 & 3205.5504 & 0.363 & 0.5888 & 0.4671 & 0.6608 \tabularnewline
108 & 1210 & 1875.3306 & 968.0848 & 3255.3616 & 0.1723 & 0.6384 & 0.1425 & 0.6615 \tabularnewline
109 & 1372 & 1881.188 & 959.0506 & 3293.9194 & 0.24 & 0.8241 & 0.3776 & 0.661 \tabularnewline
110 & 1354 & 1884.6796 & 949.9988 & 3325.8989 & 0.2352 & 0.7572 & 0.6324 & 0.6597 \tabularnewline
111 & 1202 & 1886.7595 & 940.9645 & 3353.9605 & 0.1802 & 0.7617 & 0.8382 & 0.658 \tabularnewline
112 & 1080 & 1887.998 & 931.993 & 3379.6467 & 0.1442 & 0.8163 & 0.7801 & 0.6562 \tabularnewline
113 & 926 & 1888.7352 & 923.1186 & 3403.862 & 0.1065 & 0.8523 & 0.8413 & 0.6542 \tabularnewline
114 & 1590 & 1889.174 & 914.3633 & 3427.1416 & 0.3515 & 0.8902 & 0.6523 & 0.6523 \tabularnewline
115 & 2080 & 1889.4351 & 905.7394 & 3449.8041 & 0.4054 & 0.6466 & 0.781 & 0.6503 \tabularnewline
116 & 2268 & 1889.5906 & 897.2529 & 3472.0404 & 0.3196 & 0.4068 & 0.587 & 0.6484 \tabularnewline
117 & 1956 & 1889.683 & 888.9056 & 3493.9659 & 0.4677 & 0.322 & 0.7086 & 0.6465 \tabularnewline
118 & 1670 & 1889.7381 & 880.6968 & 3515.651 & 0.3955 & 0.4682 & 0.5848 & 0.6447 \tabularnewline
119 & 1182 & 1889.7708 & 872.6245 & 3537.1395 & 0.1999 & 0.6031 & 0.6232 & 0.6429 \tabularnewline
120 & 992 & 1889.7903 & 864.6856 & 3558.4589 & 0.1458 & 0.7971 & 0.7877 & 0.6411 \tabularnewline
121 & 1300 & 1889.8019 & 856.8768 & 3579.6274 & 0.247 & 0.8511 & 0.7259 & 0.6395 \tabularnewline
122 & 1250 & 1889.8088 & 849.1945 & 3600.6573 & 0.2318 & 0.7504 & 0.7303 & 0.6378 \tabularnewline
123 & 1278 & 1889.8129 & 841.6352 & 3621.5575 & 0.2443 & 0.7655 & 0.7819 & 0.6362 \tabularnewline
124 & 1302 & 1889.8154 & 834.1954 & 3642.3345 & 0.2555 & 0.7531 & 0.8174 & 0.6347 \tabularnewline
125 & 1278 & 1889.8168 & 826.8716 & 3662.9936 & 0.2494 & 0.7421 & 0.8566 & 0.6332 \tabularnewline
126 & 2388 & 1889.8177 & 819.6605 & 3683.5393 & 0.2931 & 0.7481 & 0.6284 & 0.6317 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303493&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[102])[/C][/ROW]
[ROW][C]90[/C][C]2096[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]91[/C][C]2666[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]92[/C][C]2920[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]93[/C][C]4850[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]94[/C][C]2738[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]95[/C][C]1922[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]96[/C][C]2628[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]97[/C][C]2106[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]98[/C][C]1636[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]99[/C][C]1148[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]100[/C][C]1300[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]101[/C][C]1116[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]102[/C][C]1582[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]103[/C][C]1272[/C][C]1702.044[/C][C]1079.9517[/C][C]2539.7083[/C][C]0.1572[/C][C]0.6106[/C][C]0.0121[/C][C]0.6106[/C][/ROW]
[ROW][C]104[/C][C]1712[/C][C]1776.4258[/C][C]1020.23[/C][C]2857.4777[/C][C]0.4535[/C][C]0.8198[/C][C]0.0191[/C][C]0.6378[/C][/ROW]
[ROW][C]105[/C][C]1440[/C][C]1821.7513[/C][C]998.7635[/C][C]3030.8341[/C][C]0.268[/C][C]0.5706[/C][C]0[/C][C]0.6512[/C][/ROW]
[ROW][C]106[/C][C]1712[/C][C]1849.1038[/C][C]986.7545[/C][C]3136.034[/C][C]0.4173[/C][C]0.7334[/C][C]0.0879[/C][C]0.6579[/C][/ROW]
[ROW][C]107[/C][C]1626[/C][C]1865.516[/C][C]977.1637[/C][C]3205.5504[/C][C]0.363[/C][C]0.5888[/C][C]0.4671[/C][C]0.6608[/C][/ROW]
[ROW][C]108[/C][C]1210[/C][C]1875.3306[/C][C]968.0848[/C][C]3255.3616[/C][C]0.1723[/C][C]0.6384[/C][C]0.1425[/C][C]0.6615[/C][/ROW]
[ROW][C]109[/C][C]1372[/C][C]1881.188[/C][C]959.0506[/C][C]3293.9194[/C][C]0.24[/C][C]0.8241[/C][C]0.3776[/C][C]0.661[/C][/ROW]
[ROW][C]110[/C][C]1354[/C][C]1884.6796[/C][C]949.9988[/C][C]3325.8989[/C][C]0.2352[/C][C]0.7572[/C][C]0.6324[/C][C]0.6597[/C][/ROW]
[ROW][C]111[/C][C]1202[/C][C]1886.7595[/C][C]940.9645[/C][C]3353.9605[/C][C]0.1802[/C][C]0.7617[/C][C]0.8382[/C][C]0.658[/C][/ROW]
[ROW][C]112[/C][C]1080[/C][C]1887.998[/C][C]931.993[/C][C]3379.6467[/C][C]0.1442[/C][C]0.8163[/C][C]0.7801[/C][C]0.6562[/C][/ROW]
[ROW][C]113[/C][C]926[/C][C]1888.7352[/C][C]923.1186[/C][C]3403.862[/C][C]0.1065[/C][C]0.8523[/C][C]0.8413[/C][C]0.6542[/C][/ROW]
[ROW][C]114[/C][C]1590[/C][C]1889.174[/C][C]914.3633[/C][C]3427.1416[/C][C]0.3515[/C][C]0.8902[/C][C]0.6523[/C][C]0.6523[/C][/ROW]
[ROW][C]115[/C][C]2080[/C][C]1889.4351[/C][C]905.7394[/C][C]3449.8041[/C][C]0.4054[/C][C]0.6466[/C][C]0.781[/C][C]0.6503[/C][/ROW]
[ROW][C]116[/C][C]2268[/C][C]1889.5906[/C][C]897.2529[/C][C]3472.0404[/C][C]0.3196[/C][C]0.4068[/C][C]0.587[/C][C]0.6484[/C][/ROW]
[ROW][C]117[/C][C]1956[/C][C]1889.683[/C][C]888.9056[/C][C]3493.9659[/C][C]0.4677[/C][C]0.322[/C][C]0.7086[/C][C]0.6465[/C][/ROW]
[ROW][C]118[/C][C]1670[/C][C]1889.7381[/C][C]880.6968[/C][C]3515.651[/C][C]0.3955[/C][C]0.4682[/C][C]0.5848[/C][C]0.6447[/C][/ROW]
[ROW][C]119[/C][C]1182[/C][C]1889.7708[/C][C]872.6245[/C][C]3537.1395[/C][C]0.1999[/C][C]0.6031[/C][C]0.6232[/C][C]0.6429[/C][/ROW]
[ROW][C]120[/C][C]992[/C][C]1889.7903[/C][C]864.6856[/C][C]3558.4589[/C][C]0.1458[/C][C]0.7971[/C][C]0.7877[/C][C]0.6411[/C][/ROW]
[ROW][C]121[/C][C]1300[/C][C]1889.8019[/C][C]856.8768[/C][C]3579.6274[/C][C]0.247[/C][C]0.8511[/C][C]0.7259[/C][C]0.6395[/C][/ROW]
[ROW][C]122[/C][C]1250[/C][C]1889.8088[/C][C]849.1945[/C][C]3600.6573[/C][C]0.2318[/C][C]0.7504[/C][C]0.7303[/C][C]0.6378[/C][/ROW]
[ROW][C]123[/C][C]1278[/C][C]1889.8129[/C][C]841.6352[/C][C]3621.5575[/C][C]0.2443[/C][C]0.7655[/C][C]0.7819[/C][C]0.6362[/C][/ROW]
[ROW][C]124[/C][C]1302[/C][C]1889.8154[/C][C]834.1954[/C][C]3642.3345[/C][C]0.2555[/C][C]0.7531[/C][C]0.8174[/C][C]0.6347[/C][/ROW]
[ROW][C]125[/C][C]1278[/C][C]1889.8168[/C][C]826.8716[/C][C]3662.9936[/C][C]0.2494[/C][C]0.7421[/C][C]0.8566[/C][C]0.6332[/C][/ROW]
[ROW][C]126[/C][C]2388[/C][C]1889.8177[/C][C]819.6605[/C][C]3683.5393[/C][C]0.2931[/C][C]0.7481[/C][C]0.6284[/C][C]0.6317[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303493&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303493&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[102])
902096-------
912666-------
922920-------
934850-------
942738-------
951922-------
962628-------
972106-------
981636-------
991148-------
1001300-------
1011116-------
1021582-------
10312721702.0441079.95172539.70830.15720.61060.01210.6106
10417121776.42581020.232857.47770.45350.81980.01910.6378
10514401821.7513998.76353030.83410.2680.570600.6512
10617121849.1038986.75453136.0340.41730.73340.08790.6579
10716261865.516977.16373205.55040.3630.58880.46710.6608
10812101875.3306968.08483255.36160.17230.63840.14250.6615
10913721881.188959.05063293.91940.240.82410.37760.661
11013541884.6796949.99883325.89890.23520.75720.63240.6597
11112021886.7595940.96453353.96050.18020.76170.83820.658
11210801887.998931.9933379.64670.14420.81630.78010.6562
1139261888.7352923.11863403.8620.10650.85230.84130.6542
11415901889.174914.36333427.14160.35150.89020.65230.6523
11520801889.4351905.73943449.80410.40540.64660.7810.6503
11622681889.5906897.25293472.04040.31960.40680.5870.6484
11719561889.683888.90563493.96590.46770.3220.70860.6465
11816701889.7381880.69683515.6510.39550.46820.58480.6447
11911821889.7708872.62453537.13950.19990.60310.62320.6429
1209921889.7903864.68563558.45890.14580.79710.78770.6411
12113001889.8019856.87683579.62740.2470.85110.72590.6395
12212501889.8088849.19453600.65730.23180.75040.73030.6378
12312781889.8129841.63523621.55750.24430.76550.78190.6362
12413021889.8154834.19543642.33450.25550.75310.81740.6347
12512781889.8168826.87163662.99360.24940.74210.85660.6332
12623881889.8177819.66053683.53930.29310.74810.62840.6317







Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPEsMAPESq.EMSERMSEScaledEMASE
1030.2511-0.33810.33810.2892184937.846300-1.5811.581
1040.3105-0.03760.18790.16314150.681494544.2638307.4805-0.23690.909
1050.3386-0.26510.21360.1867145734.0815111607.5364334.0771-1.40351.0738
1060.3551-0.08010.18020.159318797.445788405.0137297.3298-0.50410.9314
1070.3665-0.14730.17360.154957367.907482197.5925286.7012-0.88060.9212
1080.3755-0.54990.23630.2009442664.7644142275.4545377.1942-2.44611.1754
1090.3832-0.37110.25560.217259272.4289158989.308398.7346-1.8721.2749
1100.3902-0.39190.27260.2308281620.8829174318.2548417.5144-1.9511.3594
1110.3968-0.56970.30560.2544468895.6339207049.0747455.0265-2.51751.4881
1120.4031-0.74810.34990.2834652860.7437251630.2416501.6276-2.97061.6363
1130.4093-1.03970.41260.3199926859.0811313014.6816559.4772-3.53951.8093
1140.4154-0.18820.39390.307589505.0823294388.8816542.5762-1.09991.7502
1150.42130.09160.37060.291336314.9644274537.0418523.96280.70061.6695
1160.42730.16680.35610.2835143193.7093265155.3752514.93241.39121.6496
1170.43310.03390.33460.26694397.9396247771.5462497.76660.24381.5559
1180.439-0.13160.32190.257948284.8201235303.6258485.0811-0.80791.5091
1190.4448-0.59880.33820.2698500939.5359250929.2676500.9284-2.60211.5734
1200.4505-0.9050.36970.2895806027.4411281768.055530.8183-3.30071.6694
1210.4562-0.45370.37410.2937347866.2898285246.9095534.0851-2.16841.6956
1220.4619-0.51180.3810.2994409355.3111291452.3295539.8633-2.35221.7285
1230.4675-0.47870.38570.3035374315.0428295398.173543.5054-2.24931.7533
1240.4731-0.45150.38870.3065345526.8957297676.7513545.5976-2.16111.7718
1250.4787-0.47870.39260.3099374319.8123301009.0583548.6429-2.24931.7926
1260.48430.20860.38490.3067248185.626298808.082546.63341.83161.7942

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & sMAPE & Sq.E & MSE & RMSE & ScaledE & MASE \tabularnewline
103 & 0.2511 & -0.3381 & 0.3381 & 0.2892 & 184937.8463 & 0 & 0 & -1.581 & 1.581 \tabularnewline
104 & 0.3105 & -0.0376 & 0.1879 & 0.1631 & 4150.6814 & 94544.2638 & 307.4805 & -0.2369 & 0.909 \tabularnewline
105 & 0.3386 & -0.2651 & 0.2136 & 0.1867 & 145734.0815 & 111607.5364 & 334.0771 & -1.4035 & 1.0738 \tabularnewline
106 & 0.3551 & -0.0801 & 0.1802 & 0.1593 & 18797.4457 & 88405.0137 & 297.3298 & -0.5041 & 0.9314 \tabularnewline
107 & 0.3665 & -0.1473 & 0.1736 & 0.1549 & 57367.9074 & 82197.5925 & 286.7012 & -0.8806 & 0.9212 \tabularnewline
108 & 0.3755 & -0.5499 & 0.2363 & 0.2009 & 442664.7644 & 142275.4545 & 377.1942 & -2.4461 & 1.1754 \tabularnewline
109 & 0.3832 & -0.3711 & 0.2556 & 0.217 & 259272.4289 & 158989.308 & 398.7346 & -1.872 & 1.2749 \tabularnewline
110 & 0.3902 & -0.3919 & 0.2726 & 0.2308 & 281620.8829 & 174318.2548 & 417.5144 & -1.951 & 1.3594 \tabularnewline
111 & 0.3968 & -0.5697 & 0.3056 & 0.2544 & 468895.6339 & 207049.0747 & 455.0265 & -2.5175 & 1.4881 \tabularnewline
112 & 0.4031 & -0.7481 & 0.3499 & 0.2834 & 652860.7437 & 251630.2416 & 501.6276 & -2.9706 & 1.6363 \tabularnewline
113 & 0.4093 & -1.0397 & 0.4126 & 0.3199 & 926859.0811 & 313014.6816 & 559.4772 & -3.5395 & 1.8093 \tabularnewline
114 & 0.4154 & -0.1882 & 0.3939 & 0.3075 & 89505.0823 & 294388.8816 & 542.5762 & -1.0999 & 1.7502 \tabularnewline
115 & 0.4213 & 0.0916 & 0.3706 & 0.2913 & 36314.9644 & 274537.0418 & 523.9628 & 0.7006 & 1.6695 \tabularnewline
116 & 0.4273 & 0.1668 & 0.3561 & 0.2835 & 143193.7093 & 265155.3752 & 514.9324 & 1.3912 & 1.6496 \tabularnewline
117 & 0.4331 & 0.0339 & 0.3346 & 0.2669 & 4397.9396 & 247771.5462 & 497.7666 & 0.2438 & 1.5559 \tabularnewline
118 & 0.439 & -0.1316 & 0.3219 & 0.2579 & 48284.8201 & 235303.6258 & 485.0811 & -0.8079 & 1.5091 \tabularnewline
119 & 0.4448 & -0.5988 & 0.3382 & 0.2698 & 500939.5359 & 250929.2676 & 500.9284 & -2.6021 & 1.5734 \tabularnewline
120 & 0.4505 & -0.905 & 0.3697 & 0.2895 & 806027.4411 & 281768.055 & 530.8183 & -3.3007 & 1.6694 \tabularnewline
121 & 0.4562 & -0.4537 & 0.3741 & 0.2937 & 347866.2898 & 285246.9095 & 534.0851 & -2.1684 & 1.6956 \tabularnewline
122 & 0.4619 & -0.5118 & 0.381 & 0.2994 & 409355.3111 & 291452.3295 & 539.8633 & -2.3522 & 1.7285 \tabularnewline
123 & 0.4675 & -0.4787 & 0.3857 & 0.3035 & 374315.0428 & 295398.173 & 543.5054 & -2.2493 & 1.7533 \tabularnewline
124 & 0.4731 & -0.4515 & 0.3887 & 0.3065 & 345526.8957 & 297676.7513 & 545.5976 & -2.1611 & 1.7718 \tabularnewline
125 & 0.4787 & -0.4787 & 0.3926 & 0.3099 & 374319.8123 & 301009.0583 & 548.6429 & -2.2493 & 1.7926 \tabularnewline
126 & 0.4843 & 0.2086 & 0.3849 & 0.3067 & 248185.626 & 298808.082 & 546.6334 & 1.8316 & 1.7942 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303493&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]sMAPE[/C][C]Sq.E[/C][C]MSE[/C][C]RMSE[/C][C]ScaledE[/C][C]MASE[/C][/ROW]
[ROW][C]103[/C][C]0.2511[/C][C]-0.3381[/C][C]0.3381[/C][C]0.2892[/C][C]184937.8463[/C][C]0[/C][C]0[/C][C]-1.581[/C][C]1.581[/C][/ROW]
[ROW][C]104[/C][C]0.3105[/C][C]-0.0376[/C][C]0.1879[/C][C]0.1631[/C][C]4150.6814[/C][C]94544.2638[/C][C]307.4805[/C][C]-0.2369[/C][C]0.909[/C][/ROW]
[ROW][C]105[/C][C]0.3386[/C][C]-0.2651[/C][C]0.2136[/C][C]0.1867[/C][C]145734.0815[/C][C]111607.5364[/C][C]334.0771[/C][C]-1.4035[/C][C]1.0738[/C][/ROW]
[ROW][C]106[/C][C]0.3551[/C][C]-0.0801[/C][C]0.1802[/C][C]0.1593[/C][C]18797.4457[/C][C]88405.0137[/C][C]297.3298[/C][C]-0.5041[/C][C]0.9314[/C][/ROW]
[ROW][C]107[/C][C]0.3665[/C][C]-0.1473[/C][C]0.1736[/C][C]0.1549[/C][C]57367.9074[/C][C]82197.5925[/C][C]286.7012[/C][C]-0.8806[/C][C]0.9212[/C][/ROW]
[ROW][C]108[/C][C]0.3755[/C][C]-0.5499[/C][C]0.2363[/C][C]0.2009[/C][C]442664.7644[/C][C]142275.4545[/C][C]377.1942[/C][C]-2.4461[/C][C]1.1754[/C][/ROW]
[ROW][C]109[/C][C]0.3832[/C][C]-0.3711[/C][C]0.2556[/C][C]0.217[/C][C]259272.4289[/C][C]158989.308[/C][C]398.7346[/C][C]-1.872[/C][C]1.2749[/C][/ROW]
[ROW][C]110[/C][C]0.3902[/C][C]-0.3919[/C][C]0.2726[/C][C]0.2308[/C][C]281620.8829[/C][C]174318.2548[/C][C]417.5144[/C][C]-1.951[/C][C]1.3594[/C][/ROW]
[ROW][C]111[/C][C]0.3968[/C][C]-0.5697[/C][C]0.3056[/C][C]0.2544[/C][C]468895.6339[/C][C]207049.0747[/C][C]455.0265[/C][C]-2.5175[/C][C]1.4881[/C][/ROW]
[ROW][C]112[/C][C]0.4031[/C][C]-0.7481[/C][C]0.3499[/C][C]0.2834[/C][C]652860.7437[/C][C]251630.2416[/C][C]501.6276[/C][C]-2.9706[/C][C]1.6363[/C][/ROW]
[ROW][C]113[/C][C]0.4093[/C][C]-1.0397[/C][C]0.4126[/C][C]0.3199[/C][C]926859.0811[/C][C]313014.6816[/C][C]559.4772[/C][C]-3.5395[/C][C]1.8093[/C][/ROW]
[ROW][C]114[/C][C]0.4154[/C][C]-0.1882[/C][C]0.3939[/C][C]0.3075[/C][C]89505.0823[/C][C]294388.8816[/C][C]542.5762[/C][C]-1.0999[/C][C]1.7502[/C][/ROW]
[ROW][C]115[/C][C]0.4213[/C][C]0.0916[/C][C]0.3706[/C][C]0.2913[/C][C]36314.9644[/C][C]274537.0418[/C][C]523.9628[/C][C]0.7006[/C][C]1.6695[/C][/ROW]
[ROW][C]116[/C][C]0.4273[/C][C]0.1668[/C][C]0.3561[/C][C]0.2835[/C][C]143193.7093[/C][C]265155.3752[/C][C]514.9324[/C][C]1.3912[/C][C]1.6496[/C][/ROW]
[ROW][C]117[/C][C]0.4331[/C][C]0.0339[/C][C]0.3346[/C][C]0.2669[/C][C]4397.9396[/C][C]247771.5462[/C][C]497.7666[/C][C]0.2438[/C][C]1.5559[/C][/ROW]
[ROW][C]118[/C][C]0.439[/C][C]-0.1316[/C][C]0.3219[/C][C]0.2579[/C][C]48284.8201[/C][C]235303.6258[/C][C]485.0811[/C][C]-0.8079[/C][C]1.5091[/C][/ROW]
[ROW][C]119[/C][C]0.4448[/C][C]-0.5988[/C][C]0.3382[/C][C]0.2698[/C][C]500939.5359[/C][C]250929.2676[/C][C]500.9284[/C][C]-2.6021[/C][C]1.5734[/C][/ROW]
[ROW][C]120[/C][C]0.4505[/C][C]-0.905[/C][C]0.3697[/C][C]0.2895[/C][C]806027.4411[/C][C]281768.055[/C][C]530.8183[/C][C]-3.3007[/C][C]1.6694[/C][/ROW]
[ROW][C]121[/C][C]0.4562[/C][C]-0.4537[/C][C]0.3741[/C][C]0.2937[/C][C]347866.2898[/C][C]285246.9095[/C][C]534.0851[/C][C]-2.1684[/C][C]1.6956[/C][/ROW]
[ROW][C]122[/C][C]0.4619[/C][C]-0.5118[/C][C]0.381[/C][C]0.2994[/C][C]409355.3111[/C][C]291452.3295[/C][C]539.8633[/C][C]-2.3522[/C][C]1.7285[/C][/ROW]
[ROW][C]123[/C][C]0.4675[/C][C]-0.4787[/C][C]0.3857[/C][C]0.3035[/C][C]374315.0428[/C][C]295398.173[/C][C]543.5054[/C][C]-2.2493[/C][C]1.7533[/C][/ROW]
[ROW][C]124[/C][C]0.4731[/C][C]-0.4515[/C][C]0.3887[/C][C]0.3065[/C][C]345526.8957[/C][C]297676.7513[/C][C]545.5976[/C][C]-2.1611[/C][C]1.7718[/C][/ROW]
[ROW][C]125[/C][C]0.4787[/C][C]-0.4787[/C][C]0.3926[/C][C]0.3099[/C][C]374319.8123[/C][C]301009.0583[/C][C]548.6429[/C][C]-2.2493[/C][C]1.7926[/C][/ROW]
[ROW][C]126[/C][C]0.4843[/C][C]0.2086[/C][C]0.3849[/C][C]0.3067[/C][C]248185.626[/C][C]298808.082[/C][C]546.6334[/C][C]1.8316[/C][C]1.7942[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303493&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303493&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.PEMAPEsMAPESq.EMSERMSEScaledEMASE
1030.2511-0.33810.33810.2892184937.846300-1.5811.581
1040.3105-0.03760.18790.16314150.681494544.2638307.4805-0.23690.909
1050.3386-0.26510.21360.1867145734.0815111607.5364334.0771-1.40351.0738
1060.3551-0.08010.18020.159318797.445788405.0137297.3298-0.50410.9314
1070.3665-0.14730.17360.154957367.907482197.5925286.7012-0.88060.9212
1080.3755-0.54990.23630.2009442664.7644142275.4545377.1942-2.44611.1754
1090.3832-0.37110.25560.217259272.4289158989.308398.7346-1.8721.2749
1100.3902-0.39190.27260.2308281620.8829174318.2548417.5144-1.9511.3594
1110.3968-0.56970.30560.2544468895.6339207049.0747455.0265-2.51751.4881
1120.4031-0.74810.34990.2834652860.7437251630.2416501.6276-2.97061.6363
1130.4093-1.03970.41260.3199926859.0811313014.6816559.4772-3.53951.8093
1140.4154-0.18820.39390.307589505.0823294388.8816542.5762-1.09991.7502
1150.42130.09160.37060.291336314.9644274537.0418523.96280.70061.6695
1160.42730.16680.35610.2835143193.7093265155.3752514.93241.39121.6496
1170.43310.03390.33460.26694397.9396247771.5462497.76660.24381.5559
1180.439-0.13160.32190.257948284.8201235303.6258485.0811-0.80791.5091
1190.4448-0.59880.33820.2698500939.5359250929.2676500.9284-2.60211.5734
1200.4505-0.9050.36970.2895806027.4411281768.055530.8183-3.30071.6694
1210.4562-0.45370.37410.2937347866.2898285246.9095534.0851-2.16841.6956
1220.4619-0.51180.3810.2994409355.3111291452.3295539.8633-2.35221.7285
1230.4675-0.47870.38570.3035374315.0428295398.173543.5054-2.24931.7533
1240.4731-0.45150.38870.3065345526.8957297676.7513545.5976-2.16111.7718
1250.4787-0.47870.39260.3099374319.8123301009.0583548.6429-2.24931.7926
1260.48430.20860.38490.3067248185.626298808.082546.63341.83161.7942



Parameters (Session):
Parameters (R input):
par1 = 24 ; par2 = 0.3 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 1 ; par7 = 1 ; par8 = 0 ; par9 = 0 ; par10 = FALSE ;
R code (references can be found in the software module):
par10 <- 'FALSE'
par9 <- '0'
par8 <- '0'
par7 <- '1'
par6 <- '1'
par5 <- '12'
par4 <- '0'
par3 <- '1'
par2 <- '0.3'
par1 <- '12'
par1 <- as.numeric(par1) #cut off periods
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
par7 <- as.numeric(par7) #q
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*2
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,fx))
(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.spe <- array(0, dim=fx)
perf.scalederr <- array(0, dim=fx)
perf.mase <- array(0, dim=fx)
perf.mase1 <- array(0, dim=fx)
perf.mape <- array(0, dim=fx)
perf.smape <- array(0, dim=fx)
perf.mape1 <- array(0, dim=fx)
perf.smape1 <- 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)
perf.scaleddenom <- 0
for (i in 2:fx) {
perf.scaleddenom = perf.scaleddenom + abs(x[nx+i] - x[nx+i-1])
}
perf.scaleddenom = perf.scaleddenom / (fx-1)
for (i in 1:fx) {
locSD <- (ub[i] - forecast$pred[i]) / 1.96
perf.scalederr[i] = (x[nx+i] - forecast$pred[i]) / perf.scaleddenom
perf.pe[i] = (x[nx+i] - forecast$pred[i]) / x[nx+i]
perf.spe[i] = 2*(x[nx+i] - forecast$pred[i]) / (x[nx+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.smape[1] = abs(perf.spe[1])
perf.mape1[1] = perf.mape[1]
perf.smape1[1] = perf.smape[1]
perf.mse[1] = perf.se[1]
perf.mase[1] = abs(perf.scalederr[1])
perf.mase1[1] = perf.mase[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.smape[i] = perf.smape[i-1] + abs(perf.spe[i])
perf.smape1[i] = perf.smape[i] / i
perf.mse[i] = perf.mse[i-1] + perf.se[i]
perf.mse1[i] = perf.mse[i] / i
perf.mase[i] = perf.mase[i-1] + abs(perf.scalederr[i])
perf.mase1[i] = perf.mase[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',10,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,'sMAPE',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.element(a,'ScaledE',1,header=TRUE)
a<-table.element(a,'MASE',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.smape1[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.element(a,round(perf.scalederr[i],4))
a<-table.element(a,round(perf.mase1[i],4))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')