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 03:17:58 -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/t1260440532m4mqtdmy36isqdq.htm/, Retrieved Thu, 28 Mar 2024 08:11:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65248, Retrieved Thu, 28 Mar 2024 08:11:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact172
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] [ARIMA-Forecasting] [2009-12-09 16:48:03] [ee7c2e7343f5b1451e62c5c16ec521f1]
-   PD    [ARIMA Forecasting] [ARIMA forecasting] [2009-12-10 08:34:29] [a542c511726eba04a1fc2f4bd37a90f8]
-   P         [ARIMA Forecasting] [] [2009-12-10 10:17:58] [865cd78857e928bd6e7d79509c6cdcc5] [Current]
Feedback Forum

Post a new message
Dataseries X:
2916
2434
2540
2349
2310
2189
2660
2194
2419
2742
2137
2710
2173
2363
2126
1905
2121
1983
1734
2074
2049
2406
2558
2251
2059
2397
1747
1707
2319
1631
1627
1791
2034
1997
2169
2028
2253
2218
1855
2187
1852
1570
1851
1954
1828
2251
2277
2085
2282
2266
1878
2267
2069
1746
2299
2360
2214
2825
2355
2333
3016
2155
2172
2150
2533
2058
2160
2260
2498
2695
2799
2947
2930
2318
2540
2570
2669
2450
2842
3440
2678
2981
2260
2844
2546
2456
2295
2379
2479
2057
2280
2351
2276
2548
2311
2201
2725
2408
2139
1898
2537
2069
2063
2524
2437
2189
2793
2074
2622
2278
2144
2427
2139
1828
2072
1800
1758
2246
1987
1868
2514




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=65248&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=65248&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65248&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[97])
852546-------
862456-------
872295-------
882379-------
892479-------
902057-------
912280-------
922351-------
932276-------
942548-------
952311-------
962201-------
972725-------
9824082279.28631791.29892767.27360.30260.03670.23890.0367
9921392204.26071705.93752702.58380.39870.21150.36060.0203
10018982319.83981792.13592847.54360.05860.74910.4130.0662
10125372337.931765.33462910.52540.24780.9340.31460.0926
10220692110.05931519.31812700.80060.44580.07830.56990.0207
10320632287.86981673.66472902.07490.23650.75750.510.0815
10425242389.2651752.8843025.6460.33910.84250.54690.1506
10524372319.94621665.62152974.27080.36290.27050.55240.1125
10621892540.16031867.26253213.05810.15320.61810.49090.2952
10727932373.36131683.09313063.62940.11670.69970.57030.159
10820742432.40191725.82123138.98270.16010.15860.73950.2085
10926222529.00821806.17263251.84390.40050.89140.29760.2976
11022782324.62091569.00523080.23670.45190.22020.41440.1495
11121442226.89641453.67623000.11660.41680.44850.58820.1034
11224272281.00461488.38263073.62670.3590.63260.82820.1361
11321392344.42061531.41773157.42340.31020.42110.32120.1794
11418282111.03981280.52582941.55390.25210.47370.53950.0737
11520722276.06571427.74883124.38270.31860.84970.68870.1498
11618002381.47751515.79853247.15650.0940.75830.37350.2184
11717582310.74141428.47823193.00460.10970.87170.38960.1787
11822462522.14771623.40583420.88970.27350.95220.76620.3291
11919872363.111448.34323277.87680.21020.59910.17850.2191
12018682418.85241488.47473349.23020.12290.81850.76620.2595
12125142511.77711565.7143457.84020.49820.90890.40970.3293

\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[97]) \tabularnewline
85 & 2546 & - & - & - & - & - & - & - \tabularnewline
86 & 2456 & - & - & - & - & - & - & - \tabularnewline
87 & 2295 & - & - & - & - & - & - & - \tabularnewline
88 & 2379 & - & - & - & - & - & - & - \tabularnewline
89 & 2479 & - & - & - & - & - & - & - \tabularnewline
90 & 2057 & - & - & - & - & - & - & - \tabularnewline
91 & 2280 & - & - & - & - & - & - & - \tabularnewline
92 & 2351 & - & - & - & - & - & - & - \tabularnewline
93 & 2276 & - & - & - & - & - & - & - \tabularnewline
94 & 2548 & - & - & - & - & - & - & - \tabularnewline
95 & 2311 & - & - & - & - & - & - & - \tabularnewline
96 & 2201 & - & - & - & - & - & - & - \tabularnewline
97 & 2725 & - & - & - & - & - & - & - \tabularnewline
98 & 2408 & 2279.2863 & 1791.2989 & 2767.2736 & 0.3026 & 0.0367 & 0.2389 & 0.0367 \tabularnewline
99 & 2139 & 2204.2607 & 1705.9375 & 2702.5838 & 0.3987 & 0.2115 & 0.3606 & 0.0203 \tabularnewline
100 & 1898 & 2319.8398 & 1792.1359 & 2847.5436 & 0.0586 & 0.7491 & 0.413 & 0.0662 \tabularnewline
101 & 2537 & 2337.93 & 1765.3346 & 2910.5254 & 0.2478 & 0.934 & 0.3146 & 0.0926 \tabularnewline
102 & 2069 & 2110.0593 & 1519.3181 & 2700.8006 & 0.4458 & 0.0783 & 0.5699 & 0.0207 \tabularnewline
103 & 2063 & 2287.8698 & 1673.6647 & 2902.0749 & 0.2365 & 0.7575 & 0.51 & 0.0815 \tabularnewline
104 & 2524 & 2389.265 & 1752.884 & 3025.646 & 0.3391 & 0.8425 & 0.5469 & 0.1506 \tabularnewline
105 & 2437 & 2319.9462 & 1665.6215 & 2974.2708 & 0.3629 & 0.2705 & 0.5524 & 0.1125 \tabularnewline
106 & 2189 & 2540.1603 & 1867.2625 & 3213.0581 & 0.1532 & 0.6181 & 0.4909 & 0.2952 \tabularnewline
107 & 2793 & 2373.3613 & 1683.0931 & 3063.6294 & 0.1167 & 0.6997 & 0.5703 & 0.159 \tabularnewline
108 & 2074 & 2432.4019 & 1725.8212 & 3138.9827 & 0.1601 & 0.1586 & 0.7395 & 0.2085 \tabularnewline
109 & 2622 & 2529.0082 & 1806.1726 & 3251.8439 & 0.4005 & 0.8914 & 0.2976 & 0.2976 \tabularnewline
110 & 2278 & 2324.6209 & 1569.0052 & 3080.2367 & 0.4519 & 0.2202 & 0.4144 & 0.1495 \tabularnewline
111 & 2144 & 2226.8964 & 1453.6762 & 3000.1166 & 0.4168 & 0.4485 & 0.5882 & 0.1034 \tabularnewline
112 & 2427 & 2281.0046 & 1488.3826 & 3073.6267 & 0.359 & 0.6326 & 0.8282 & 0.1361 \tabularnewline
113 & 2139 & 2344.4206 & 1531.4177 & 3157.4234 & 0.3102 & 0.4211 & 0.3212 & 0.1794 \tabularnewline
114 & 1828 & 2111.0398 & 1280.5258 & 2941.5539 & 0.2521 & 0.4737 & 0.5395 & 0.0737 \tabularnewline
115 & 2072 & 2276.0657 & 1427.7488 & 3124.3827 & 0.3186 & 0.8497 & 0.6887 & 0.1498 \tabularnewline
116 & 1800 & 2381.4775 & 1515.7985 & 3247.1565 & 0.094 & 0.7583 & 0.3735 & 0.2184 \tabularnewline
117 & 1758 & 2310.7414 & 1428.4782 & 3193.0046 & 0.1097 & 0.8717 & 0.3896 & 0.1787 \tabularnewline
118 & 2246 & 2522.1477 & 1623.4058 & 3420.8897 & 0.2735 & 0.9522 & 0.7662 & 0.3291 \tabularnewline
119 & 1987 & 2363.11 & 1448.3432 & 3277.8768 & 0.2102 & 0.5991 & 0.1785 & 0.2191 \tabularnewline
120 & 1868 & 2418.8524 & 1488.4747 & 3349.2302 & 0.1229 & 0.8185 & 0.7662 & 0.2595 \tabularnewline
121 & 2514 & 2511.7771 & 1565.714 & 3457.8402 & 0.4982 & 0.9089 & 0.4097 & 0.3293 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65248&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[97])[/C][/ROW]
[ROW][C]85[/C][C]2546[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]86[/C][C]2456[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]87[/C][C]2295[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]88[/C][C]2379[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]89[/C][C]2479[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]90[/C][C]2057[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]91[/C][C]2280[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]92[/C][C]2351[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]93[/C][C]2276[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]94[/C][C]2548[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]95[/C][C]2311[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]96[/C][C]2201[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]97[/C][C]2725[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]98[/C][C]2408[/C][C]2279.2863[/C][C]1791.2989[/C][C]2767.2736[/C][C]0.3026[/C][C]0.0367[/C][C]0.2389[/C][C]0.0367[/C][/ROW]
[ROW][C]99[/C][C]2139[/C][C]2204.2607[/C][C]1705.9375[/C][C]2702.5838[/C][C]0.3987[/C][C]0.2115[/C][C]0.3606[/C][C]0.0203[/C][/ROW]
[ROW][C]100[/C][C]1898[/C][C]2319.8398[/C][C]1792.1359[/C][C]2847.5436[/C][C]0.0586[/C][C]0.7491[/C][C]0.413[/C][C]0.0662[/C][/ROW]
[ROW][C]101[/C][C]2537[/C][C]2337.93[/C][C]1765.3346[/C][C]2910.5254[/C][C]0.2478[/C][C]0.934[/C][C]0.3146[/C][C]0.0926[/C][/ROW]
[ROW][C]102[/C][C]2069[/C][C]2110.0593[/C][C]1519.3181[/C][C]2700.8006[/C][C]0.4458[/C][C]0.0783[/C][C]0.5699[/C][C]0.0207[/C][/ROW]
[ROW][C]103[/C][C]2063[/C][C]2287.8698[/C][C]1673.6647[/C][C]2902.0749[/C][C]0.2365[/C][C]0.7575[/C][C]0.51[/C][C]0.0815[/C][/ROW]
[ROW][C]104[/C][C]2524[/C][C]2389.265[/C][C]1752.884[/C][C]3025.646[/C][C]0.3391[/C][C]0.8425[/C][C]0.5469[/C][C]0.1506[/C][/ROW]
[ROW][C]105[/C][C]2437[/C][C]2319.9462[/C][C]1665.6215[/C][C]2974.2708[/C][C]0.3629[/C][C]0.2705[/C][C]0.5524[/C][C]0.1125[/C][/ROW]
[ROW][C]106[/C][C]2189[/C][C]2540.1603[/C][C]1867.2625[/C][C]3213.0581[/C][C]0.1532[/C][C]0.6181[/C][C]0.4909[/C][C]0.2952[/C][/ROW]
[ROW][C]107[/C][C]2793[/C][C]2373.3613[/C][C]1683.0931[/C][C]3063.6294[/C][C]0.1167[/C][C]0.6997[/C][C]0.5703[/C][C]0.159[/C][/ROW]
[ROW][C]108[/C][C]2074[/C][C]2432.4019[/C][C]1725.8212[/C][C]3138.9827[/C][C]0.1601[/C][C]0.1586[/C][C]0.7395[/C][C]0.2085[/C][/ROW]
[ROW][C]109[/C][C]2622[/C][C]2529.0082[/C][C]1806.1726[/C][C]3251.8439[/C][C]0.4005[/C][C]0.8914[/C][C]0.2976[/C][C]0.2976[/C][/ROW]
[ROW][C]110[/C][C]2278[/C][C]2324.6209[/C][C]1569.0052[/C][C]3080.2367[/C][C]0.4519[/C][C]0.2202[/C][C]0.4144[/C][C]0.1495[/C][/ROW]
[ROW][C]111[/C][C]2144[/C][C]2226.8964[/C][C]1453.6762[/C][C]3000.1166[/C][C]0.4168[/C][C]0.4485[/C][C]0.5882[/C][C]0.1034[/C][/ROW]
[ROW][C]112[/C][C]2427[/C][C]2281.0046[/C][C]1488.3826[/C][C]3073.6267[/C][C]0.359[/C][C]0.6326[/C][C]0.8282[/C][C]0.1361[/C][/ROW]
[ROW][C]113[/C][C]2139[/C][C]2344.4206[/C][C]1531.4177[/C][C]3157.4234[/C][C]0.3102[/C][C]0.4211[/C][C]0.3212[/C][C]0.1794[/C][/ROW]
[ROW][C]114[/C][C]1828[/C][C]2111.0398[/C][C]1280.5258[/C][C]2941.5539[/C][C]0.2521[/C][C]0.4737[/C][C]0.5395[/C][C]0.0737[/C][/ROW]
[ROW][C]115[/C][C]2072[/C][C]2276.0657[/C][C]1427.7488[/C][C]3124.3827[/C][C]0.3186[/C][C]0.8497[/C][C]0.6887[/C][C]0.1498[/C][/ROW]
[ROW][C]116[/C][C]1800[/C][C]2381.4775[/C][C]1515.7985[/C][C]3247.1565[/C][C]0.094[/C][C]0.7583[/C][C]0.3735[/C][C]0.2184[/C][/ROW]
[ROW][C]117[/C][C]1758[/C][C]2310.7414[/C][C]1428.4782[/C][C]3193.0046[/C][C]0.1097[/C][C]0.8717[/C][C]0.3896[/C][C]0.1787[/C][/ROW]
[ROW][C]118[/C][C]2246[/C][C]2522.1477[/C][C]1623.4058[/C][C]3420.8897[/C][C]0.2735[/C][C]0.9522[/C][C]0.7662[/C][C]0.3291[/C][/ROW]
[ROW][C]119[/C][C]1987[/C][C]2363.11[/C][C]1448.3432[/C][C]3277.8768[/C][C]0.2102[/C][C]0.5991[/C][C]0.1785[/C][C]0.2191[/C][/ROW]
[ROW][C]120[/C][C]1868[/C][C]2418.8524[/C][C]1488.4747[/C][C]3349.2302[/C][C]0.1229[/C][C]0.8185[/C][C]0.7662[/C][C]0.2595[/C][/ROW]
[ROW][C]121[/C][C]2514[/C][C]2511.7771[/C][C]1565.714[/C][C]3457.8402[/C][C]0.4982[/C][C]0.9089[/C][C]0.4097[/C][C]0.3293[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65248&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65248&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[97])
852546-------
862456-------
872295-------
882379-------
892479-------
902057-------
912280-------
922351-------
932276-------
942548-------
952311-------
962201-------
972725-------
9824082279.28631791.29892767.27360.30260.03670.23890.0367
9921392204.26071705.93752702.58380.39870.21150.36060.0203
10018982319.83981792.13592847.54360.05860.74910.4130.0662
10125372337.931765.33462910.52540.24780.9340.31460.0926
10220692110.05931519.31812700.80060.44580.07830.56990.0207
10320632287.86981673.66472902.07490.23650.75750.510.0815
10425242389.2651752.8843025.6460.33910.84250.54690.1506
10524372319.94621665.62152974.27080.36290.27050.55240.1125
10621892540.16031867.26253213.05810.15320.61810.49090.2952
10727932373.36131683.09313063.62940.11670.69970.57030.159
10820742432.40191725.82123138.98270.16010.15860.73950.2085
10926222529.00821806.17263251.84390.40050.89140.29760.2976
11022782324.62091569.00523080.23670.45190.22020.41440.1495
11121442226.89641453.67623000.11660.41680.44850.58820.1034
11224272281.00461488.38263073.62670.3590.63260.82820.1361
11321392344.42061531.41773157.42340.31020.42110.32120.1794
11418282111.03981280.52582941.55390.25210.47370.53950.0737
11520722276.06571427.74883124.38270.31860.84970.68870.1498
11618002381.47751515.79853247.15650.0940.75830.37350.2184
11717582310.74141428.47823193.00460.10970.87170.38960.1787
11822462522.14771623.40583420.88970.27350.95220.76620.3291
11919872363.111448.34323277.87680.21020.59910.17850.2191
12018682418.85241488.47473349.23020.12290.81850.76620.2595
12125142511.77711565.7143457.84020.49820.90890.40970.3293







Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
980.10920.0565016567.220800
990.1153-0.02960.0434258.954610413.0877102.0445
1000.1161-0.18180.0893177948.776966258.3175257.4069
1010.1250.08510.088339628.860659600.9533244.1331
1020.1428-0.01950.07451685.868748017.9363219.13
1030.137-0.09830.078550566.42748442.6848220.097
1040.13590.05640.075318153.516444115.6607210.0373
1050.14390.05050.072213701.598340313.9029200.7832
1060.1352-0.13820.0795123313.551349536.0861222.567
1070.14840.17680.0893176096.657562192.1432249.3835
1080.1482-0.14730.0946128451.936568215.7608261.1815
1090.14580.03680.08978647.470563251.7366251.499
1100.1658-0.02010.08442173.512758553.4117241.9781
1110.1772-0.03720.0816871.81454861.869234.2261
1120.17730.0640.079921314.650252625.3877229.4022
1130.1769-0.08760.080442197.602951973.6512227.9773
1140.2007-0.13410.083580111.546653628.8215231.579
1150.1902-0.08970.083941642.824152962.9328230.1368
1160.1855-0.24420.0923338116.061467970.9922260.7125
1170.1948-0.23920.0996305523.073879848.5962282.5749
1180.1818-0.10950.100176257.56379677.5947282.2722
1190.1975-0.15920.1028141458.736882485.8284287.2035
1200.1962-0.22770.1082303438.404392092.4621303.4674
1210.19229e-040.10384.941288255.4821297.0782

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
98 & 0.1092 & 0.0565 & 0 & 16567.2208 & 0 & 0 \tabularnewline
99 & 0.1153 & -0.0296 & 0.043 & 4258.9546 & 10413.0877 & 102.0445 \tabularnewline
100 & 0.1161 & -0.1818 & 0.0893 & 177948.7769 & 66258.3175 & 257.4069 \tabularnewline
101 & 0.125 & 0.0851 & 0.0883 & 39628.8606 & 59600.9533 & 244.1331 \tabularnewline
102 & 0.1428 & -0.0195 & 0.0745 & 1685.8687 & 48017.9363 & 219.13 \tabularnewline
103 & 0.137 & -0.0983 & 0.0785 & 50566.427 & 48442.6848 & 220.097 \tabularnewline
104 & 0.1359 & 0.0564 & 0.0753 & 18153.5164 & 44115.6607 & 210.0373 \tabularnewline
105 & 0.1439 & 0.0505 & 0.0722 & 13701.5983 & 40313.9029 & 200.7832 \tabularnewline
106 & 0.1352 & -0.1382 & 0.0795 & 123313.5513 & 49536.0861 & 222.567 \tabularnewline
107 & 0.1484 & 0.1768 & 0.0893 & 176096.6575 & 62192.1432 & 249.3835 \tabularnewline
108 & 0.1482 & -0.1473 & 0.0946 & 128451.9365 & 68215.7608 & 261.1815 \tabularnewline
109 & 0.1458 & 0.0368 & 0.0897 & 8647.4705 & 63251.7366 & 251.499 \tabularnewline
110 & 0.1658 & -0.0201 & 0.0844 & 2173.5127 & 58553.4117 & 241.9781 \tabularnewline
111 & 0.1772 & -0.0372 & 0.081 & 6871.814 & 54861.869 & 234.2261 \tabularnewline
112 & 0.1773 & 0.064 & 0.0799 & 21314.6502 & 52625.3877 & 229.4022 \tabularnewline
113 & 0.1769 & -0.0876 & 0.0804 & 42197.6029 & 51973.6512 & 227.9773 \tabularnewline
114 & 0.2007 & -0.1341 & 0.0835 & 80111.5466 & 53628.8215 & 231.579 \tabularnewline
115 & 0.1902 & -0.0897 & 0.0839 & 41642.8241 & 52962.9328 & 230.1368 \tabularnewline
116 & 0.1855 & -0.2442 & 0.0923 & 338116.0614 & 67970.9922 & 260.7125 \tabularnewline
117 & 0.1948 & -0.2392 & 0.0996 & 305523.0738 & 79848.5962 & 282.5749 \tabularnewline
118 & 0.1818 & -0.1095 & 0.1001 & 76257.563 & 79677.5947 & 282.2722 \tabularnewline
119 & 0.1975 & -0.1592 & 0.1028 & 141458.7368 & 82485.8284 & 287.2035 \tabularnewline
120 & 0.1962 & -0.2277 & 0.1082 & 303438.4043 & 92092.4621 & 303.4674 \tabularnewline
121 & 0.1922 & 9e-04 & 0.1038 & 4.9412 & 88255.4821 & 297.0782 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65248&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]98[/C][C]0.1092[/C][C]0.0565[/C][C]0[/C][C]16567.2208[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]99[/C][C]0.1153[/C][C]-0.0296[/C][C]0.043[/C][C]4258.9546[/C][C]10413.0877[/C][C]102.0445[/C][/ROW]
[ROW][C]100[/C][C]0.1161[/C][C]-0.1818[/C][C]0.0893[/C][C]177948.7769[/C][C]66258.3175[/C][C]257.4069[/C][/ROW]
[ROW][C]101[/C][C]0.125[/C][C]0.0851[/C][C]0.0883[/C][C]39628.8606[/C][C]59600.9533[/C][C]244.1331[/C][/ROW]
[ROW][C]102[/C][C]0.1428[/C][C]-0.0195[/C][C]0.0745[/C][C]1685.8687[/C][C]48017.9363[/C][C]219.13[/C][/ROW]
[ROW][C]103[/C][C]0.137[/C][C]-0.0983[/C][C]0.0785[/C][C]50566.427[/C][C]48442.6848[/C][C]220.097[/C][/ROW]
[ROW][C]104[/C][C]0.1359[/C][C]0.0564[/C][C]0.0753[/C][C]18153.5164[/C][C]44115.6607[/C][C]210.0373[/C][/ROW]
[ROW][C]105[/C][C]0.1439[/C][C]0.0505[/C][C]0.0722[/C][C]13701.5983[/C][C]40313.9029[/C][C]200.7832[/C][/ROW]
[ROW][C]106[/C][C]0.1352[/C][C]-0.1382[/C][C]0.0795[/C][C]123313.5513[/C][C]49536.0861[/C][C]222.567[/C][/ROW]
[ROW][C]107[/C][C]0.1484[/C][C]0.1768[/C][C]0.0893[/C][C]176096.6575[/C][C]62192.1432[/C][C]249.3835[/C][/ROW]
[ROW][C]108[/C][C]0.1482[/C][C]-0.1473[/C][C]0.0946[/C][C]128451.9365[/C][C]68215.7608[/C][C]261.1815[/C][/ROW]
[ROW][C]109[/C][C]0.1458[/C][C]0.0368[/C][C]0.0897[/C][C]8647.4705[/C][C]63251.7366[/C][C]251.499[/C][/ROW]
[ROW][C]110[/C][C]0.1658[/C][C]-0.0201[/C][C]0.0844[/C][C]2173.5127[/C][C]58553.4117[/C][C]241.9781[/C][/ROW]
[ROW][C]111[/C][C]0.1772[/C][C]-0.0372[/C][C]0.081[/C][C]6871.814[/C][C]54861.869[/C][C]234.2261[/C][/ROW]
[ROW][C]112[/C][C]0.1773[/C][C]0.064[/C][C]0.0799[/C][C]21314.6502[/C][C]52625.3877[/C][C]229.4022[/C][/ROW]
[ROW][C]113[/C][C]0.1769[/C][C]-0.0876[/C][C]0.0804[/C][C]42197.6029[/C][C]51973.6512[/C][C]227.9773[/C][/ROW]
[ROW][C]114[/C][C]0.2007[/C][C]-0.1341[/C][C]0.0835[/C][C]80111.5466[/C][C]53628.8215[/C][C]231.579[/C][/ROW]
[ROW][C]115[/C][C]0.1902[/C][C]-0.0897[/C][C]0.0839[/C][C]41642.8241[/C][C]52962.9328[/C][C]230.1368[/C][/ROW]
[ROW][C]116[/C][C]0.1855[/C][C]-0.2442[/C][C]0.0923[/C][C]338116.0614[/C][C]67970.9922[/C][C]260.7125[/C][/ROW]
[ROW][C]117[/C][C]0.1948[/C][C]-0.2392[/C][C]0.0996[/C][C]305523.0738[/C][C]79848.5962[/C][C]282.5749[/C][/ROW]
[ROW][C]118[/C][C]0.1818[/C][C]-0.1095[/C][C]0.1001[/C][C]76257.563[/C][C]79677.5947[/C][C]282.2722[/C][/ROW]
[ROW][C]119[/C][C]0.1975[/C][C]-0.1592[/C][C]0.1028[/C][C]141458.7368[/C][C]82485.8284[/C][C]287.2035[/C][/ROW]
[ROW][C]120[/C][C]0.1962[/C][C]-0.2277[/C][C]0.1082[/C][C]303438.4043[/C][C]92092.4621[/C][C]303.4674[/C][/ROW]
[ROW][C]121[/C][C]0.1922[/C][C]9e-04[/C][C]0.1038[/C][C]4.9412[/C][C]88255.4821[/C][C]297.0782[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65248&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65248&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
980.10920.0565016567.220800
990.1153-0.02960.0434258.954610413.0877102.0445
1000.1161-0.18180.0893177948.776966258.3175257.4069
1010.1250.08510.088339628.860659600.9533244.1331
1020.1428-0.01950.07451685.868748017.9363219.13
1030.137-0.09830.078550566.42748442.6848220.097
1040.13590.05640.075318153.516444115.6607210.0373
1050.14390.05050.072213701.598340313.9029200.7832
1060.1352-0.13820.0795123313.551349536.0861222.567
1070.14840.17680.0893176096.657562192.1432249.3835
1080.1482-0.14730.0946128451.936568215.7608261.1815
1090.14580.03680.08978647.470563251.7366251.499
1100.1658-0.02010.08442173.512758553.4117241.9781
1110.1772-0.03720.0816871.81454861.869234.2261
1120.17730.0640.079921314.650252625.3877229.4022
1130.1769-0.08760.080442197.602951973.6512227.9773
1140.2007-0.13410.083580111.546653628.8215231.579
1150.1902-0.08970.083941642.824152962.9328230.1368
1160.1855-0.24420.0923338116.061467970.9922260.7125
1170.1948-0.23920.0996305523.073879848.5962282.5749
1180.1818-0.10950.100176257.56379677.5947282.2722
1190.1975-0.15920.1028141458.736882485.8284287.2035
1200.1962-0.22770.1082303438.404392092.4621303.4674
1210.19229e-040.10384.941288255.4821297.0782



Parameters (Session):
par1 = 24 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 1 ; par9 = 1 ; par10 = FALSE ;
Parameters (R input):
par1 = 24 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 1 ; par9 = 1 ; par10 = FALSE ;
R code (references can be found in the software module):
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
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')