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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 computationFri, 14 Dec 2012 05:40:49 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/14/t13554816812uzr8sgivaw4yhl.htm/, Retrieved Thu, 28 Mar 2024 18:24:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=199473, Retrieved Thu, 28 Mar 2024 18:24:02 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact112
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-12 13:32:37] [76963dc1903f0f612b6153510a3818cf]
- R  D  [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-17 12:14:40] [76963dc1903f0f612b6153510a3818cf]
-         [Univariate Explorative Data Analysis] [Run Sequence Plot...] [2008-12-22 18:19:51] [1ce0d16c8f4225c977b42c8fa93bc163]
- RMP       [Univariate Data Series] [Identifying Integ...] [2009-11-22 12:08:06] [b98453cac15ba1066b407e146608df68]
- RMP         [(Partial) Autocorrelation Function] [Births] [2010-11-29 09:36:27] [b98453cac15ba1066b407e146608df68]
- R P           [(Partial) Autocorrelation Function] [Workshop 9 ACF Bi...] [2012-11-30 10:33:31] [d63e92c9ef4b8a0e48798c0b0ce2077f]
- RMP               [ARIMA Forecasting] [arima forecasting] [2012-12-14 10:40:49] [e5ad38085056e4424dc3e3ce5946aa62] [Current]
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Dataseries X:
9700
9081
9084
9743
8587
9731
9563
9998
9437
10038
9918
9252
9737
9035
9133
9487
8700
9627
8947
9283
8829
9947
9628
9318
9605
8640
9214
9567
8547
9185
9470
9123
9278
10170
9434
9655
9429
8739
9552
9687
9019
9672
9206
9069
9788
10312
10105
9863
9656
9295
9946
9701
9049
10190
9706
9765
9893
9994
10433
10073
10112
9266
9820
10097
9115
10411
9678
10408
10153
10368
10581
10597
10680
9738
9556




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'George Udny Yule' @ yule.wessa.net

\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 & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199473&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]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199473&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199473&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'George Udny Yule' @ yule.wessa.net







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[51])
509295-------
519946-------
5297019451.38968619.955210282.82390.27810.12180.12180.1218
5390499537.88198706.029210369.73460.12470.35040.35040.1681
54101909481.65938629.692610333.6260.05160.84020.84020.1427
5597069470.91738614.059110327.77540.29540.050.050.1386
5697659457.68058597.517410317.84370.24190.28580.28580.1329
5798939450.36938588.555810312.18290.1570.23710.23710.1298
5899949445.20828582.433910307.98250.10630.15450.15450.1276
59104339441.87748578.54310305.21170.01220.1050.1050.1262
60100739439.65978575.984410303.3350.07530.01210.01210.1253
61101129438.19948574.312310302.08660.06320.07490.07490.1246
6292669437.23418573.212710301.25550.34880.06290.06290.1242
6398209436.59688572.489210300.70450.19220.65060.65060.124
64100979436.17598572.012510300.33940.0670.1920.1920.1238
6591159435.8988571.698110300.09790.23340.06690.06690.1237
66104119435.71458571.490810299.93820.01350.76650.76650.1236
6796789435.59338571.353910299.83260.29120.01350.01350.1235
68104089435.51328571.263610299.76290.01370.29120.29120.1235
69101539435.46038571.203910299.71680.05180.01370.01370.1235
70103689435.42548571.164510299.68630.01720.05180.05180.1235
71105819435.40248571.138510299.66620.00470.01720.01720.1234
72105979435.38728571.121410299.6530.00420.00470.00470.1234
73106809435.37718571.1110299.64420.00240.00420.00420.1234
7497389435.37058571.102510299.63840.24630.00240.00240.1234
7595569435.36618571.097610299.63460.39220.24630.24630.1234

\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[51]) \tabularnewline
50 & 9295 & - & - & - & - & - & - & - \tabularnewline
51 & 9946 & - & - & - & - & - & - & - \tabularnewline
52 & 9701 & 9451.3896 & 8619.9552 & 10282.8239 & 0.2781 & 0.1218 & 0.1218 & 0.1218 \tabularnewline
53 & 9049 & 9537.8819 & 8706.0292 & 10369.7346 & 0.1247 & 0.3504 & 0.3504 & 0.1681 \tabularnewline
54 & 10190 & 9481.6593 & 8629.6926 & 10333.626 & 0.0516 & 0.8402 & 0.8402 & 0.1427 \tabularnewline
55 & 9706 & 9470.9173 & 8614.0591 & 10327.7754 & 0.2954 & 0.05 & 0.05 & 0.1386 \tabularnewline
56 & 9765 & 9457.6805 & 8597.5174 & 10317.8437 & 0.2419 & 0.2858 & 0.2858 & 0.1329 \tabularnewline
57 & 9893 & 9450.3693 & 8588.5558 & 10312.1829 & 0.157 & 0.2371 & 0.2371 & 0.1298 \tabularnewline
58 & 9994 & 9445.2082 & 8582.4339 & 10307.9825 & 0.1063 & 0.1545 & 0.1545 & 0.1276 \tabularnewline
59 & 10433 & 9441.8774 & 8578.543 & 10305.2117 & 0.0122 & 0.105 & 0.105 & 0.1262 \tabularnewline
60 & 10073 & 9439.6597 & 8575.9844 & 10303.335 & 0.0753 & 0.0121 & 0.0121 & 0.1253 \tabularnewline
61 & 10112 & 9438.1994 & 8574.3123 & 10302.0866 & 0.0632 & 0.0749 & 0.0749 & 0.1246 \tabularnewline
62 & 9266 & 9437.2341 & 8573.2127 & 10301.2555 & 0.3488 & 0.0629 & 0.0629 & 0.1242 \tabularnewline
63 & 9820 & 9436.5968 & 8572.4892 & 10300.7045 & 0.1922 & 0.6506 & 0.6506 & 0.124 \tabularnewline
64 & 10097 & 9436.1759 & 8572.0125 & 10300.3394 & 0.067 & 0.192 & 0.192 & 0.1238 \tabularnewline
65 & 9115 & 9435.898 & 8571.6981 & 10300.0979 & 0.2334 & 0.0669 & 0.0669 & 0.1237 \tabularnewline
66 & 10411 & 9435.7145 & 8571.4908 & 10299.9382 & 0.0135 & 0.7665 & 0.7665 & 0.1236 \tabularnewline
67 & 9678 & 9435.5933 & 8571.3539 & 10299.8326 & 0.2912 & 0.0135 & 0.0135 & 0.1235 \tabularnewline
68 & 10408 & 9435.5132 & 8571.2636 & 10299.7629 & 0.0137 & 0.2912 & 0.2912 & 0.1235 \tabularnewline
69 & 10153 & 9435.4603 & 8571.2039 & 10299.7168 & 0.0518 & 0.0137 & 0.0137 & 0.1235 \tabularnewline
70 & 10368 & 9435.4254 & 8571.1645 & 10299.6863 & 0.0172 & 0.0518 & 0.0518 & 0.1235 \tabularnewline
71 & 10581 & 9435.4024 & 8571.1385 & 10299.6662 & 0.0047 & 0.0172 & 0.0172 & 0.1234 \tabularnewline
72 & 10597 & 9435.3872 & 8571.1214 & 10299.653 & 0.0042 & 0.0047 & 0.0047 & 0.1234 \tabularnewline
73 & 10680 & 9435.3771 & 8571.11 & 10299.6442 & 0.0024 & 0.0042 & 0.0042 & 0.1234 \tabularnewline
74 & 9738 & 9435.3705 & 8571.1025 & 10299.6384 & 0.2463 & 0.0024 & 0.0024 & 0.1234 \tabularnewline
75 & 9556 & 9435.3661 & 8571.0976 & 10299.6346 & 0.3922 & 0.2463 & 0.2463 & 0.1234 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199473&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[51])[/C][/ROW]
[ROW][C]50[/C][C]9295[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]51[/C][C]9946[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]52[/C][C]9701[/C][C]9451.3896[/C][C]8619.9552[/C][C]10282.8239[/C][C]0.2781[/C][C]0.1218[/C][C]0.1218[/C][C]0.1218[/C][/ROW]
[ROW][C]53[/C][C]9049[/C][C]9537.8819[/C][C]8706.0292[/C][C]10369.7346[/C][C]0.1247[/C][C]0.3504[/C][C]0.3504[/C][C]0.1681[/C][/ROW]
[ROW][C]54[/C][C]10190[/C][C]9481.6593[/C][C]8629.6926[/C][C]10333.626[/C][C]0.0516[/C][C]0.8402[/C][C]0.8402[/C][C]0.1427[/C][/ROW]
[ROW][C]55[/C][C]9706[/C][C]9470.9173[/C][C]8614.0591[/C][C]10327.7754[/C][C]0.2954[/C][C]0.05[/C][C]0.05[/C][C]0.1386[/C][/ROW]
[ROW][C]56[/C][C]9765[/C][C]9457.6805[/C][C]8597.5174[/C][C]10317.8437[/C][C]0.2419[/C][C]0.2858[/C][C]0.2858[/C][C]0.1329[/C][/ROW]
[ROW][C]57[/C][C]9893[/C][C]9450.3693[/C][C]8588.5558[/C][C]10312.1829[/C][C]0.157[/C][C]0.2371[/C][C]0.2371[/C][C]0.1298[/C][/ROW]
[ROW][C]58[/C][C]9994[/C][C]9445.2082[/C][C]8582.4339[/C][C]10307.9825[/C][C]0.1063[/C][C]0.1545[/C][C]0.1545[/C][C]0.1276[/C][/ROW]
[ROW][C]59[/C][C]10433[/C][C]9441.8774[/C][C]8578.543[/C][C]10305.2117[/C][C]0.0122[/C][C]0.105[/C][C]0.105[/C][C]0.1262[/C][/ROW]
[ROW][C]60[/C][C]10073[/C][C]9439.6597[/C][C]8575.9844[/C][C]10303.335[/C][C]0.0753[/C][C]0.0121[/C][C]0.0121[/C][C]0.1253[/C][/ROW]
[ROW][C]61[/C][C]10112[/C][C]9438.1994[/C][C]8574.3123[/C][C]10302.0866[/C][C]0.0632[/C][C]0.0749[/C][C]0.0749[/C][C]0.1246[/C][/ROW]
[ROW][C]62[/C][C]9266[/C][C]9437.2341[/C][C]8573.2127[/C][C]10301.2555[/C][C]0.3488[/C][C]0.0629[/C][C]0.0629[/C][C]0.1242[/C][/ROW]
[ROW][C]63[/C][C]9820[/C][C]9436.5968[/C][C]8572.4892[/C][C]10300.7045[/C][C]0.1922[/C][C]0.6506[/C][C]0.6506[/C][C]0.124[/C][/ROW]
[ROW][C]64[/C][C]10097[/C][C]9436.1759[/C][C]8572.0125[/C][C]10300.3394[/C][C]0.067[/C][C]0.192[/C][C]0.192[/C][C]0.1238[/C][/ROW]
[ROW][C]65[/C][C]9115[/C][C]9435.898[/C][C]8571.6981[/C][C]10300.0979[/C][C]0.2334[/C][C]0.0669[/C][C]0.0669[/C][C]0.1237[/C][/ROW]
[ROW][C]66[/C][C]10411[/C][C]9435.7145[/C][C]8571.4908[/C][C]10299.9382[/C][C]0.0135[/C][C]0.7665[/C][C]0.7665[/C][C]0.1236[/C][/ROW]
[ROW][C]67[/C][C]9678[/C][C]9435.5933[/C][C]8571.3539[/C][C]10299.8326[/C][C]0.2912[/C][C]0.0135[/C][C]0.0135[/C][C]0.1235[/C][/ROW]
[ROW][C]68[/C][C]10408[/C][C]9435.5132[/C][C]8571.2636[/C][C]10299.7629[/C][C]0.0137[/C][C]0.2912[/C][C]0.2912[/C][C]0.1235[/C][/ROW]
[ROW][C]69[/C][C]10153[/C][C]9435.4603[/C][C]8571.2039[/C][C]10299.7168[/C][C]0.0518[/C][C]0.0137[/C][C]0.0137[/C][C]0.1235[/C][/ROW]
[ROW][C]70[/C][C]10368[/C][C]9435.4254[/C][C]8571.1645[/C][C]10299.6863[/C][C]0.0172[/C][C]0.0518[/C][C]0.0518[/C][C]0.1235[/C][/ROW]
[ROW][C]71[/C][C]10581[/C][C]9435.4024[/C][C]8571.1385[/C][C]10299.6662[/C][C]0.0047[/C][C]0.0172[/C][C]0.0172[/C][C]0.1234[/C][/ROW]
[ROW][C]72[/C][C]10597[/C][C]9435.3872[/C][C]8571.1214[/C][C]10299.653[/C][C]0.0042[/C][C]0.0047[/C][C]0.0047[/C][C]0.1234[/C][/ROW]
[ROW][C]73[/C][C]10680[/C][C]9435.3771[/C][C]8571.11[/C][C]10299.6442[/C][C]0.0024[/C][C]0.0042[/C][C]0.0042[/C][C]0.1234[/C][/ROW]
[ROW][C]74[/C][C]9738[/C][C]9435.3705[/C][C]8571.1025[/C][C]10299.6384[/C][C]0.2463[/C][C]0.0024[/C][C]0.0024[/C][C]0.1234[/C][/ROW]
[ROW][C]75[/C][C]9556[/C][C]9435.3661[/C][C]8571.0976[/C][C]10299.6346[/C][C]0.3922[/C][C]0.2463[/C][C]0.2463[/C][C]0.1234[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199473&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199473&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[51])
509295-------
519946-------
5297019451.38968619.955210282.82390.27810.12180.12180.1218
5390499537.88198706.029210369.73460.12470.35040.35040.1681
54101909481.65938629.692610333.6260.05160.84020.84020.1427
5597069470.91738614.059110327.77540.29540.050.050.1386
5697659457.68058597.517410317.84370.24190.28580.28580.1329
5798939450.36938588.555810312.18290.1570.23710.23710.1298
5899949445.20828582.433910307.98250.10630.15450.15450.1276
59104339441.87748578.54310305.21170.01220.1050.1050.1262
60100739439.65978575.984410303.3350.07530.01210.01210.1253
61101129438.19948574.312310302.08660.06320.07490.07490.1246
6292669437.23418573.212710301.25550.34880.06290.06290.1242
6398209436.59688572.489210300.70450.19220.65060.65060.124
64100979436.17598572.012510300.33940.0670.1920.1920.1238
6591159435.8988571.698110300.09790.23340.06690.06690.1237
66104119435.71458571.490810299.93820.01350.76650.76650.1236
6796789435.59338571.353910299.83260.29120.01350.01350.1235
68104089435.51328571.263610299.76290.01370.29120.29120.1235
69101539435.46038571.203910299.71680.05180.01370.01370.1235
70103689435.42548571.164510299.68630.01720.05180.05180.1235
71105819435.40248571.138510299.66620.00470.01720.01720.1234
72105979435.38728571.121410299.6530.00420.00470.00470.1234
73106809435.37718571.1110299.64420.00240.00420.00420.1234
7497389435.37058571.102510299.63840.24630.00240.00240.1234
7595569435.36618571.097610299.63460.39220.24630.24630.1234







Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
520.04490.0264062305.369500
530.0445-0.05130.0388239005.5312150655.4504388.1436
540.04580.07470.0508501746.5036267685.8014517.3836
550.04620.02480.044355263.8808214580.3213463.2282
560.04640.03250.041994445.2526190553.3075436.5241
570.04650.04680.0428195921.9165191448.0757437.5478
580.04660.05810.0449301172.4503207122.9864455.1077
590.04670.1050.0524982324.0486304023.1191551.3829
600.04670.06710.0541401119.9019314811.6506561.0808
610.04670.07140.0558454007.2097328731.2065573.3509
620.0467-0.01810.052429321.1157301512.1073549.1012
630.04670.04060.0514146997.9868288635.9306537.2485
640.04670.070.0528436688.43300024.5844547.745
650.0467-0.0340.0515102975.5335285949.6522534.7426
660.04670.10340.055951181.8752330298.4671574.716
670.04670.02570.053158761.0302313327.3773559.7565
680.04670.10310.0561945730.5576350527.5643592.0537
690.04670.0760.0572514863.1505359657.3191599.7144
700.04670.09880.0594869695.3078386501.4238621.6924
710.04670.12140.06251312393.8875432796.047657.8724
720.04670.12310.06541349344.3786476441.2056690.2472
730.04670.13190.06841549086.1345525197.7933724.7053
740.04670.03210.066891584.6309506345.0471711.5793
750.04670.01280.064514552.5407485853.6927697.0321

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
52 & 0.0449 & 0.0264 & 0 & 62305.3695 & 0 & 0 \tabularnewline
53 & 0.0445 & -0.0513 & 0.0388 & 239005.5312 & 150655.4504 & 388.1436 \tabularnewline
54 & 0.0458 & 0.0747 & 0.0508 & 501746.5036 & 267685.8014 & 517.3836 \tabularnewline
55 & 0.0462 & 0.0248 & 0.0443 & 55263.8808 & 214580.3213 & 463.2282 \tabularnewline
56 & 0.0464 & 0.0325 & 0.0419 & 94445.2526 & 190553.3075 & 436.5241 \tabularnewline
57 & 0.0465 & 0.0468 & 0.0428 & 195921.9165 & 191448.0757 & 437.5478 \tabularnewline
58 & 0.0466 & 0.0581 & 0.0449 & 301172.4503 & 207122.9864 & 455.1077 \tabularnewline
59 & 0.0467 & 0.105 & 0.0524 & 982324.0486 & 304023.1191 & 551.3829 \tabularnewline
60 & 0.0467 & 0.0671 & 0.0541 & 401119.9019 & 314811.6506 & 561.0808 \tabularnewline
61 & 0.0467 & 0.0714 & 0.0558 & 454007.2097 & 328731.2065 & 573.3509 \tabularnewline
62 & 0.0467 & -0.0181 & 0.0524 & 29321.1157 & 301512.1073 & 549.1012 \tabularnewline
63 & 0.0467 & 0.0406 & 0.0514 & 146997.9868 & 288635.9306 & 537.2485 \tabularnewline
64 & 0.0467 & 0.07 & 0.0528 & 436688.43 & 300024.5844 & 547.745 \tabularnewline
65 & 0.0467 & -0.034 & 0.0515 & 102975.5335 & 285949.6522 & 534.7426 \tabularnewline
66 & 0.0467 & 0.1034 & 0.055 & 951181.8752 & 330298.4671 & 574.716 \tabularnewline
67 & 0.0467 & 0.0257 & 0.0531 & 58761.0302 & 313327.3773 & 559.7565 \tabularnewline
68 & 0.0467 & 0.1031 & 0.0561 & 945730.5576 & 350527.5643 & 592.0537 \tabularnewline
69 & 0.0467 & 0.076 & 0.0572 & 514863.1505 & 359657.3191 & 599.7144 \tabularnewline
70 & 0.0467 & 0.0988 & 0.0594 & 869695.3078 & 386501.4238 & 621.6924 \tabularnewline
71 & 0.0467 & 0.1214 & 0.0625 & 1312393.8875 & 432796.047 & 657.8724 \tabularnewline
72 & 0.0467 & 0.1231 & 0.0654 & 1349344.3786 & 476441.2056 & 690.2472 \tabularnewline
73 & 0.0467 & 0.1319 & 0.0684 & 1549086.1345 & 525197.7933 & 724.7053 \tabularnewline
74 & 0.0467 & 0.0321 & 0.0668 & 91584.6309 & 506345.0471 & 711.5793 \tabularnewline
75 & 0.0467 & 0.0128 & 0.0645 & 14552.5407 & 485853.6927 & 697.0321 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199473&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]52[/C][C]0.0449[/C][C]0.0264[/C][C]0[/C][C]62305.3695[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]53[/C][C]0.0445[/C][C]-0.0513[/C][C]0.0388[/C][C]239005.5312[/C][C]150655.4504[/C][C]388.1436[/C][/ROW]
[ROW][C]54[/C][C]0.0458[/C][C]0.0747[/C][C]0.0508[/C][C]501746.5036[/C][C]267685.8014[/C][C]517.3836[/C][/ROW]
[ROW][C]55[/C][C]0.0462[/C][C]0.0248[/C][C]0.0443[/C][C]55263.8808[/C][C]214580.3213[/C][C]463.2282[/C][/ROW]
[ROW][C]56[/C][C]0.0464[/C][C]0.0325[/C][C]0.0419[/C][C]94445.2526[/C][C]190553.3075[/C][C]436.5241[/C][/ROW]
[ROW][C]57[/C][C]0.0465[/C][C]0.0468[/C][C]0.0428[/C][C]195921.9165[/C][C]191448.0757[/C][C]437.5478[/C][/ROW]
[ROW][C]58[/C][C]0.0466[/C][C]0.0581[/C][C]0.0449[/C][C]301172.4503[/C][C]207122.9864[/C][C]455.1077[/C][/ROW]
[ROW][C]59[/C][C]0.0467[/C][C]0.105[/C][C]0.0524[/C][C]982324.0486[/C][C]304023.1191[/C][C]551.3829[/C][/ROW]
[ROW][C]60[/C][C]0.0467[/C][C]0.0671[/C][C]0.0541[/C][C]401119.9019[/C][C]314811.6506[/C][C]561.0808[/C][/ROW]
[ROW][C]61[/C][C]0.0467[/C][C]0.0714[/C][C]0.0558[/C][C]454007.2097[/C][C]328731.2065[/C][C]573.3509[/C][/ROW]
[ROW][C]62[/C][C]0.0467[/C][C]-0.0181[/C][C]0.0524[/C][C]29321.1157[/C][C]301512.1073[/C][C]549.1012[/C][/ROW]
[ROW][C]63[/C][C]0.0467[/C][C]0.0406[/C][C]0.0514[/C][C]146997.9868[/C][C]288635.9306[/C][C]537.2485[/C][/ROW]
[ROW][C]64[/C][C]0.0467[/C][C]0.07[/C][C]0.0528[/C][C]436688.43[/C][C]300024.5844[/C][C]547.745[/C][/ROW]
[ROW][C]65[/C][C]0.0467[/C][C]-0.034[/C][C]0.0515[/C][C]102975.5335[/C][C]285949.6522[/C][C]534.7426[/C][/ROW]
[ROW][C]66[/C][C]0.0467[/C][C]0.1034[/C][C]0.055[/C][C]951181.8752[/C][C]330298.4671[/C][C]574.716[/C][/ROW]
[ROW][C]67[/C][C]0.0467[/C][C]0.0257[/C][C]0.0531[/C][C]58761.0302[/C][C]313327.3773[/C][C]559.7565[/C][/ROW]
[ROW][C]68[/C][C]0.0467[/C][C]0.1031[/C][C]0.0561[/C][C]945730.5576[/C][C]350527.5643[/C][C]592.0537[/C][/ROW]
[ROW][C]69[/C][C]0.0467[/C][C]0.076[/C][C]0.0572[/C][C]514863.1505[/C][C]359657.3191[/C][C]599.7144[/C][/ROW]
[ROW][C]70[/C][C]0.0467[/C][C]0.0988[/C][C]0.0594[/C][C]869695.3078[/C][C]386501.4238[/C][C]621.6924[/C][/ROW]
[ROW][C]71[/C][C]0.0467[/C][C]0.1214[/C][C]0.0625[/C][C]1312393.8875[/C][C]432796.047[/C][C]657.8724[/C][/ROW]
[ROW][C]72[/C][C]0.0467[/C][C]0.1231[/C][C]0.0654[/C][C]1349344.3786[/C][C]476441.2056[/C][C]690.2472[/C][/ROW]
[ROW][C]73[/C][C]0.0467[/C][C]0.1319[/C][C]0.0684[/C][C]1549086.1345[/C][C]525197.7933[/C][C]724.7053[/C][/ROW]
[ROW][C]74[/C][C]0.0467[/C][C]0.0321[/C][C]0.0668[/C][C]91584.6309[/C][C]506345.0471[/C][C]711.5793[/C][/ROW]
[ROW][C]75[/C][C]0.0467[/C][C]0.0128[/C][C]0.0645[/C][C]14552.5407[/C][C]485853.6927[/C][C]697.0321[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199473&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199473&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
520.04490.0264062305.369500
530.0445-0.05130.0388239005.5312150655.4504388.1436
540.04580.07470.0508501746.5036267685.8014517.3836
550.04620.02480.044355263.8808214580.3213463.2282
560.04640.03250.041994445.2526190553.3075436.5241
570.04650.04680.0428195921.9165191448.0757437.5478
580.04660.05810.0449301172.4503207122.9864455.1077
590.04670.1050.0524982324.0486304023.1191551.3829
600.04670.06710.0541401119.9019314811.6506561.0808
610.04670.07140.0558454007.2097328731.2065573.3509
620.0467-0.01810.052429321.1157301512.1073549.1012
630.04670.04060.0514146997.9868288635.9306537.2485
640.04670.070.0528436688.43300024.5844547.745
650.0467-0.0340.0515102975.5335285949.6522534.7426
660.04670.10340.055951181.8752330298.4671574.716
670.04670.02570.053158761.0302313327.3773559.7565
680.04670.10310.0561945730.5576350527.5643592.0537
690.04670.0760.0572514863.1505359657.3191599.7144
700.04670.09880.0594869695.3078386501.4238621.6924
710.04670.12140.06251312393.8875432796.047657.8724
720.04670.12310.06541349344.3786476441.2056690.2472
730.04670.13190.06841549086.1345525197.7933724.7053
740.04670.03210.066891584.6309506345.0471711.5793
750.04670.01280.064514552.5407485853.6927697.0321



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