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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 computationTue, 21 Dec 2010 19:43:55 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/21/t1292960533ns8rzf1muxzrnap.htm/, Retrieved Thu, 31 Oct 2024 23:16:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=113903, Retrieved Thu, 31 Oct 2024 23:16:02 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact146
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [ARIMA Forecasting] [arima forecast] [2010-12-21 19:43:55] [06510e00f8d0c95cc2ff019b83c7c2eb] [Current]
- RMP     [Univariate Data Series] [] [2010-12-22 10:53:04] [ac04aa21811479ebaa189c580d7b203f]
-    D      [Univariate Data Series] [ruwe data goudkoers] [2010-12-29 15:16:05] [ca50229b6b451ac8f5a30a9e3154d674]
- RMPD      [Univariate Explorative Data Analysis] [Tutorial 2 Un EDA] [2011-12-23 19:36:45] [7d6928c5d92ed6ea67ea25577f8d26ae]
- RMPD      [Variance Reduction Matrix] [] [2011-12-23 20:08:10] [7d6928c5d92ed6ea67ea25577f8d26ae]
- RMPD      [(Partial) Autocorrelation Function] [] [2011-12-23 20:17:37] [7d6928c5d92ed6ea67ea25577f8d26ae]
- RMPD      [(Partial) Autocorrelation Function] [] [2011-12-23 20:25:00] [7d6928c5d92ed6ea67ea25577f8d26ae]
- RMPD      [(Partial) Autocorrelation Function] [] [2011-12-23 20:25:00] [7d6928c5d92ed6ea67ea25577f8d26ae]
- RMPD      [(Partial) Autocorrelation Function] [] [2011-12-23 20:30:36] [7d6928c5d92ed6ea67ea25577f8d26ae]
- RMPD      [Spectral Analysis] [] [2011-12-23 20:33:42] [7d6928c5d92ed6ea67ea25577f8d26ae]
- RMPD      [Spectral Analysis] [] [2011-12-23 20:33:42] [7d6928c5d92ed6ea67ea25577f8d26ae]
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Dataseries X:
2350.44
2440.25
2408.64
2472.81
2407.6
2454.62
2448.05
2497.84
2645.64
2756.76
2849.27
2921.44
2981.85
3080.58
3106.22
3119.31
3061.26
3097.31
3161.69
3257.16
3277.01
3295.32
3363.99
3494.17
3667.03
3813.06
3917.96
3895.51
3801.06
3570.12
3701.61
3862.27
3970.1
4138.52
4199.75
4290.89
4443.91
4502.64
4356.98
4591.27
4696.96
4621.4
4562.84
4202.52
4296.49
4435.23
4105.18
4116.68
3844.49
3720.98
3674.4
3857.62
3801.06
3504.37
3032.6
3047.03
2962.34
2197.82
2014.45
1862.83
1905.41
1810.99
1670.07
1864.44
2052.02
2029.6
2070.83
2293.41
2443.27
2513.17
2466.92
2502.66
2539.91




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 1 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113903&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113903&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113903&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24







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[49])
374443.91-------
384502.64-------
394356.98-------
404591.27-------
414696.96-------
424621.4-------
434562.84-------
444202.52-------
454296.49-------
464435.23-------
474105.18-------
484116.68-------
493844.49-------
503720.983795.07813544.57614045.58010.2810.349500.3495
513674.43786.10813398.35384173.86250.28620.6290.0020.384
523857.623784.47983291.62024277.33940.38560.66927e-040.4057
533801.063784.18423204.19994364.16850.47730.4020.0010.4193
543504.373784.13053128.37194439.88910.20150.47980.00620.4284
553032.63784.12083060.45844507.78310.02090.77570.01750.4351
563047.033784.1192998.39614569.8420.0330.96960.14830.4401
572962.343784.11872940.88994627.34750.02810.95670.11680.4442
582197.823784.11862887.06274681.17463e-040.96370.07740.4475
592014.453784.11862836.28754731.94981e-040.99950.25340.4503
601862.833784.11862788.09734780.13991e-040.99980.25640.4527
611905.413784.11862742.13354826.10372e-040.99980.45480.4548
621810.993784.11862698.11334870.12392e-040.99970.54540.4566
631670.073784.11862655.80924912.4281e-040.99970.57560.4582
641864.443784.11862615.03494953.20236e-040.99980.4510.4597
652052.023784.11862575.63564992.60160.00250.99910.4890.461
662029.63784.11862537.48095030.75640.00290.99680.670.4622
672070.833784.11862500.45975067.77760.00440.99630.87440.4633
682293.413784.11862464.47675103.76050.01340.99450.86320.4643
692443.273784.11862429.44925138.78810.02620.98450.88280.4652
702513.173784.11862395.30485172.93250.03640.97080.98740.4661
712466.923784.11862361.97995206.25730.03470.96010.99260.4668
722502.663784.11862329.41835238.81890.04210.9620.99520.4676
732539.913784.11862297.56975270.66750.05050.95440.99340.4683

\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[49]) \tabularnewline
37 & 4443.91 & - & - & - & - & - & - & - \tabularnewline
38 & 4502.64 & - & - & - & - & - & - & - \tabularnewline
39 & 4356.98 & - & - & - & - & - & - & - \tabularnewline
40 & 4591.27 & - & - & - & - & - & - & - \tabularnewline
41 & 4696.96 & - & - & - & - & - & - & - \tabularnewline
42 & 4621.4 & - & - & - & - & - & - & - \tabularnewline
43 & 4562.84 & - & - & - & - & - & - & - \tabularnewline
44 & 4202.52 & - & - & - & - & - & - & - \tabularnewline
45 & 4296.49 & - & - & - & - & - & - & - \tabularnewline
46 & 4435.23 & - & - & - & - & - & - & - \tabularnewline
47 & 4105.18 & - & - & - & - & - & - & - \tabularnewline
48 & 4116.68 & - & - & - & - & - & - & - \tabularnewline
49 & 3844.49 & - & - & - & - & - & - & - \tabularnewline
50 & 3720.98 & 3795.0781 & 3544.5761 & 4045.5801 & 0.281 & 0.3495 & 0 & 0.3495 \tabularnewline
51 & 3674.4 & 3786.1081 & 3398.3538 & 4173.8625 & 0.2862 & 0.629 & 0.002 & 0.384 \tabularnewline
52 & 3857.62 & 3784.4798 & 3291.6202 & 4277.3394 & 0.3856 & 0.6692 & 7e-04 & 0.4057 \tabularnewline
53 & 3801.06 & 3784.1842 & 3204.1999 & 4364.1685 & 0.4773 & 0.402 & 0.001 & 0.4193 \tabularnewline
54 & 3504.37 & 3784.1305 & 3128.3719 & 4439.8891 & 0.2015 & 0.4798 & 0.0062 & 0.4284 \tabularnewline
55 & 3032.6 & 3784.1208 & 3060.4584 & 4507.7831 & 0.0209 & 0.7757 & 0.0175 & 0.4351 \tabularnewline
56 & 3047.03 & 3784.119 & 2998.3961 & 4569.842 & 0.033 & 0.9696 & 0.1483 & 0.4401 \tabularnewline
57 & 2962.34 & 3784.1187 & 2940.8899 & 4627.3475 & 0.0281 & 0.9567 & 0.1168 & 0.4442 \tabularnewline
58 & 2197.82 & 3784.1186 & 2887.0627 & 4681.1746 & 3e-04 & 0.9637 & 0.0774 & 0.4475 \tabularnewline
59 & 2014.45 & 3784.1186 & 2836.2875 & 4731.9498 & 1e-04 & 0.9995 & 0.2534 & 0.4503 \tabularnewline
60 & 1862.83 & 3784.1186 & 2788.0973 & 4780.1399 & 1e-04 & 0.9998 & 0.2564 & 0.4527 \tabularnewline
61 & 1905.41 & 3784.1186 & 2742.1335 & 4826.1037 & 2e-04 & 0.9998 & 0.4548 & 0.4548 \tabularnewline
62 & 1810.99 & 3784.1186 & 2698.1133 & 4870.1239 & 2e-04 & 0.9997 & 0.5454 & 0.4566 \tabularnewline
63 & 1670.07 & 3784.1186 & 2655.8092 & 4912.428 & 1e-04 & 0.9997 & 0.5756 & 0.4582 \tabularnewline
64 & 1864.44 & 3784.1186 & 2615.0349 & 4953.2023 & 6e-04 & 0.9998 & 0.451 & 0.4597 \tabularnewline
65 & 2052.02 & 3784.1186 & 2575.6356 & 4992.6016 & 0.0025 & 0.9991 & 0.489 & 0.461 \tabularnewline
66 & 2029.6 & 3784.1186 & 2537.4809 & 5030.7564 & 0.0029 & 0.9968 & 0.67 & 0.4622 \tabularnewline
67 & 2070.83 & 3784.1186 & 2500.4597 & 5067.7776 & 0.0044 & 0.9963 & 0.8744 & 0.4633 \tabularnewline
68 & 2293.41 & 3784.1186 & 2464.4767 & 5103.7605 & 0.0134 & 0.9945 & 0.8632 & 0.4643 \tabularnewline
69 & 2443.27 & 3784.1186 & 2429.4492 & 5138.7881 & 0.0262 & 0.9845 & 0.8828 & 0.4652 \tabularnewline
70 & 2513.17 & 3784.1186 & 2395.3048 & 5172.9325 & 0.0364 & 0.9708 & 0.9874 & 0.4661 \tabularnewline
71 & 2466.92 & 3784.1186 & 2361.9799 & 5206.2573 & 0.0347 & 0.9601 & 0.9926 & 0.4668 \tabularnewline
72 & 2502.66 & 3784.1186 & 2329.4183 & 5238.8189 & 0.0421 & 0.962 & 0.9952 & 0.4676 \tabularnewline
73 & 2539.91 & 3784.1186 & 2297.5697 & 5270.6675 & 0.0505 & 0.9544 & 0.9934 & 0.4683 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113903&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[49])[/C][/ROW]
[ROW][C]37[/C][C]4443.91[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]38[/C][C]4502.64[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]39[/C][C]4356.98[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]40[/C][C]4591.27[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]41[/C][C]4696.96[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]42[/C][C]4621.4[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]43[/C][C]4562.84[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]44[/C][C]4202.52[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]45[/C][C]4296.49[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]46[/C][C]4435.23[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]47[/C][C]4105.18[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]48[/C][C]4116.68[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]49[/C][C]3844.49[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]50[/C][C]3720.98[/C][C]3795.0781[/C][C]3544.5761[/C][C]4045.5801[/C][C]0.281[/C][C]0.3495[/C][C]0[/C][C]0.3495[/C][/ROW]
[ROW][C]51[/C][C]3674.4[/C][C]3786.1081[/C][C]3398.3538[/C][C]4173.8625[/C][C]0.2862[/C][C]0.629[/C][C]0.002[/C][C]0.384[/C][/ROW]
[ROW][C]52[/C][C]3857.62[/C][C]3784.4798[/C][C]3291.6202[/C][C]4277.3394[/C][C]0.3856[/C][C]0.6692[/C][C]7e-04[/C][C]0.4057[/C][/ROW]
[ROW][C]53[/C][C]3801.06[/C][C]3784.1842[/C][C]3204.1999[/C][C]4364.1685[/C][C]0.4773[/C][C]0.402[/C][C]0.001[/C][C]0.4193[/C][/ROW]
[ROW][C]54[/C][C]3504.37[/C][C]3784.1305[/C][C]3128.3719[/C][C]4439.8891[/C][C]0.2015[/C][C]0.4798[/C][C]0.0062[/C][C]0.4284[/C][/ROW]
[ROW][C]55[/C][C]3032.6[/C][C]3784.1208[/C][C]3060.4584[/C][C]4507.7831[/C][C]0.0209[/C][C]0.7757[/C][C]0.0175[/C][C]0.4351[/C][/ROW]
[ROW][C]56[/C][C]3047.03[/C][C]3784.119[/C][C]2998.3961[/C][C]4569.842[/C][C]0.033[/C][C]0.9696[/C][C]0.1483[/C][C]0.4401[/C][/ROW]
[ROW][C]57[/C][C]2962.34[/C][C]3784.1187[/C][C]2940.8899[/C][C]4627.3475[/C][C]0.0281[/C][C]0.9567[/C][C]0.1168[/C][C]0.4442[/C][/ROW]
[ROW][C]58[/C][C]2197.82[/C][C]3784.1186[/C][C]2887.0627[/C][C]4681.1746[/C][C]3e-04[/C][C]0.9637[/C][C]0.0774[/C][C]0.4475[/C][/ROW]
[ROW][C]59[/C][C]2014.45[/C][C]3784.1186[/C][C]2836.2875[/C][C]4731.9498[/C][C]1e-04[/C][C]0.9995[/C][C]0.2534[/C][C]0.4503[/C][/ROW]
[ROW][C]60[/C][C]1862.83[/C][C]3784.1186[/C][C]2788.0973[/C][C]4780.1399[/C][C]1e-04[/C][C]0.9998[/C][C]0.2564[/C][C]0.4527[/C][/ROW]
[ROW][C]61[/C][C]1905.41[/C][C]3784.1186[/C][C]2742.1335[/C][C]4826.1037[/C][C]2e-04[/C][C]0.9998[/C][C]0.4548[/C][C]0.4548[/C][/ROW]
[ROW][C]62[/C][C]1810.99[/C][C]3784.1186[/C][C]2698.1133[/C][C]4870.1239[/C][C]2e-04[/C][C]0.9997[/C][C]0.5454[/C][C]0.4566[/C][/ROW]
[ROW][C]63[/C][C]1670.07[/C][C]3784.1186[/C][C]2655.8092[/C][C]4912.428[/C][C]1e-04[/C][C]0.9997[/C][C]0.5756[/C][C]0.4582[/C][/ROW]
[ROW][C]64[/C][C]1864.44[/C][C]3784.1186[/C][C]2615.0349[/C][C]4953.2023[/C][C]6e-04[/C][C]0.9998[/C][C]0.451[/C][C]0.4597[/C][/ROW]
[ROW][C]65[/C][C]2052.02[/C][C]3784.1186[/C][C]2575.6356[/C][C]4992.6016[/C][C]0.0025[/C][C]0.9991[/C][C]0.489[/C][C]0.461[/C][/ROW]
[ROW][C]66[/C][C]2029.6[/C][C]3784.1186[/C][C]2537.4809[/C][C]5030.7564[/C][C]0.0029[/C][C]0.9968[/C][C]0.67[/C][C]0.4622[/C][/ROW]
[ROW][C]67[/C][C]2070.83[/C][C]3784.1186[/C][C]2500.4597[/C][C]5067.7776[/C][C]0.0044[/C][C]0.9963[/C][C]0.8744[/C][C]0.4633[/C][/ROW]
[ROW][C]68[/C][C]2293.41[/C][C]3784.1186[/C][C]2464.4767[/C][C]5103.7605[/C][C]0.0134[/C][C]0.9945[/C][C]0.8632[/C][C]0.4643[/C][/ROW]
[ROW][C]69[/C][C]2443.27[/C][C]3784.1186[/C][C]2429.4492[/C][C]5138.7881[/C][C]0.0262[/C][C]0.9845[/C][C]0.8828[/C][C]0.4652[/C][/ROW]
[ROW][C]70[/C][C]2513.17[/C][C]3784.1186[/C][C]2395.3048[/C][C]5172.9325[/C][C]0.0364[/C][C]0.9708[/C][C]0.9874[/C][C]0.4661[/C][/ROW]
[ROW][C]71[/C][C]2466.92[/C][C]3784.1186[/C][C]2361.9799[/C][C]5206.2573[/C][C]0.0347[/C][C]0.9601[/C][C]0.9926[/C][C]0.4668[/C][/ROW]
[ROW][C]72[/C][C]2502.66[/C][C]3784.1186[/C][C]2329.4183[/C][C]5238.8189[/C][C]0.0421[/C][C]0.962[/C][C]0.9952[/C][C]0.4676[/C][/ROW]
[ROW][C]73[/C][C]2539.91[/C][C]3784.1186[/C][C]2297.5697[/C][C]5270.6675[/C][C]0.0505[/C][C]0.9544[/C][C]0.9934[/C][C]0.4683[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113903&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113903&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[49])
374443.91-------
384502.64-------
394356.98-------
404591.27-------
414696.96-------
424621.4-------
434562.84-------
444202.52-------
454296.49-------
464435.23-------
474105.18-------
484116.68-------
493844.49-------
503720.983795.07813544.57614045.58010.2810.349500.3495
513674.43786.10813398.35384173.86250.28620.6290.0020.384
523857.623784.47983291.62024277.33940.38560.66927e-040.4057
533801.063784.18423204.19994364.16850.47730.4020.0010.4193
543504.373784.13053128.37194439.88910.20150.47980.00620.4284
553032.63784.12083060.45844507.78310.02090.77570.01750.4351
563047.033784.1192998.39614569.8420.0330.96960.14830.4401
572962.343784.11872940.88994627.34750.02810.95670.11680.4442
582197.823784.11862887.06274681.17463e-040.96370.07740.4475
592014.453784.11862836.28754731.94981e-040.99950.25340.4503
601862.833784.11862788.09734780.13991e-040.99980.25640.4527
611905.413784.11862742.13354826.10372e-040.99980.45480.4548
621810.993784.11862698.11334870.12392e-040.99970.54540.4566
631670.073784.11862655.80924912.4281e-040.99970.57560.4582
641864.443784.11862615.03494953.20236e-040.99980.4510.4597
652052.023784.11862575.63564992.60160.00250.99910.4890.461
662029.63784.11862537.48095030.75640.00290.99680.670.4622
672070.833784.11862500.45975067.77760.00440.99630.87440.4633
682293.413784.11862464.47675103.76050.01340.99450.86320.4643
692443.273784.11862429.44925138.78810.02620.98450.88280.4652
702513.173784.11862395.30485172.93250.03640.97080.98740.4661
712466.923784.11862361.97995206.25730.03470.96010.99260.4668
722502.663784.11862329.41835238.81890.04210.9620.99520.4676
732539.913784.11862297.56975270.66750.05050.95440.99340.4683







Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
500.0337-0.019505490.530200
510.0523-0.02950.024512478.71028984.620294.7872
520.06640.01930.02285349.49057772.910388.1641
530.07820.00450.0182284.79315900.88176.8172
540.0884-0.07390.029378265.950420373.8949142.7372
550.0976-0.19860.0576564783.4855111108.8267333.3299
560.1059-0.19480.0772543300.2137172850.4534415.7529
570.1137-0.21720.0947675320.2192235659.1741485.4474
580.1209-0.41920.13072516343.3565489068.5277699.3343
590.1278-0.46770.16443131727.037753334.3786867.9484
600.1343-0.50770.19563691349.96741020426.70491010.1617
610.1405-0.49650.22073529546.08351229519.98641108.8372
620.1464-0.52140.24383893236.55571434421.2611197.6733
630.1521-0.55870.26634469201.57261651191.28331284.9869
640.1576-0.50730.28243685166.00851786789.59831336.7085
650.1629-0.45770.29333000165.63341862625.60051364.7804
660.1681-0.46370.30343078335.5921934137.95291390.7329
670.1731-0.45280.31172935357.89941989761.28331410.589
680.1779-0.39390.3162222212.19322001995.54171414.9189
690.1826-0.35430.31791797875.02491991789.51591411.3077
700.1873-0.33590.31881615310.39761973861.93881404.942
710.1917-0.34810.32011735012.20761963005.13281401.0729
720.1961-0.33860.32091642136.19771949054.30961396.0854
730.2004-0.32880.32121548055.0931932346.00891390.0885

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
50 & 0.0337 & -0.0195 & 0 & 5490.5302 & 0 & 0 \tabularnewline
51 & 0.0523 & -0.0295 & 0.0245 & 12478.7102 & 8984.6202 & 94.7872 \tabularnewline
52 & 0.0664 & 0.0193 & 0.0228 & 5349.4905 & 7772.9103 & 88.1641 \tabularnewline
53 & 0.0782 & 0.0045 & 0.0182 & 284.7931 & 5900.881 & 76.8172 \tabularnewline
54 & 0.0884 & -0.0739 & 0.0293 & 78265.9504 & 20373.8949 & 142.7372 \tabularnewline
55 & 0.0976 & -0.1986 & 0.0576 & 564783.4855 & 111108.8267 & 333.3299 \tabularnewline
56 & 0.1059 & -0.1948 & 0.0772 & 543300.2137 & 172850.4534 & 415.7529 \tabularnewline
57 & 0.1137 & -0.2172 & 0.0947 & 675320.2192 & 235659.1741 & 485.4474 \tabularnewline
58 & 0.1209 & -0.4192 & 0.1307 & 2516343.3565 & 489068.5277 & 699.3343 \tabularnewline
59 & 0.1278 & -0.4677 & 0.1644 & 3131727.037 & 753334.3786 & 867.9484 \tabularnewline
60 & 0.1343 & -0.5077 & 0.1956 & 3691349.9674 & 1020426.7049 & 1010.1617 \tabularnewline
61 & 0.1405 & -0.4965 & 0.2207 & 3529546.0835 & 1229519.9864 & 1108.8372 \tabularnewline
62 & 0.1464 & -0.5214 & 0.2438 & 3893236.5557 & 1434421.261 & 1197.6733 \tabularnewline
63 & 0.1521 & -0.5587 & 0.2663 & 4469201.5726 & 1651191.2833 & 1284.9869 \tabularnewline
64 & 0.1576 & -0.5073 & 0.2824 & 3685166.0085 & 1786789.5983 & 1336.7085 \tabularnewline
65 & 0.1629 & -0.4577 & 0.2933 & 3000165.6334 & 1862625.6005 & 1364.7804 \tabularnewline
66 & 0.1681 & -0.4637 & 0.3034 & 3078335.592 & 1934137.9529 & 1390.7329 \tabularnewline
67 & 0.1731 & -0.4528 & 0.3117 & 2935357.8994 & 1989761.2833 & 1410.589 \tabularnewline
68 & 0.1779 & -0.3939 & 0.316 & 2222212.1932 & 2001995.5417 & 1414.9189 \tabularnewline
69 & 0.1826 & -0.3543 & 0.3179 & 1797875.0249 & 1991789.5159 & 1411.3077 \tabularnewline
70 & 0.1873 & -0.3359 & 0.3188 & 1615310.3976 & 1973861.9388 & 1404.942 \tabularnewline
71 & 0.1917 & -0.3481 & 0.3201 & 1735012.2076 & 1963005.1328 & 1401.0729 \tabularnewline
72 & 0.1961 & -0.3386 & 0.3209 & 1642136.1977 & 1949054.3096 & 1396.0854 \tabularnewline
73 & 0.2004 & -0.3288 & 0.3212 & 1548055.093 & 1932346.0089 & 1390.0885 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113903&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]50[/C][C]0.0337[/C][C]-0.0195[/C][C]0[/C][C]5490.5302[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]51[/C][C]0.0523[/C][C]-0.0295[/C][C]0.0245[/C][C]12478.7102[/C][C]8984.6202[/C][C]94.7872[/C][/ROW]
[ROW][C]52[/C][C]0.0664[/C][C]0.0193[/C][C]0.0228[/C][C]5349.4905[/C][C]7772.9103[/C][C]88.1641[/C][/ROW]
[ROW][C]53[/C][C]0.0782[/C][C]0.0045[/C][C]0.0182[/C][C]284.7931[/C][C]5900.881[/C][C]76.8172[/C][/ROW]
[ROW][C]54[/C][C]0.0884[/C][C]-0.0739[/C][C]0.0293[/C][C]78265.9504[/C][C]20373.8949[/C][C]142.7372[/C][/ROW]
[ROW][C]55[/C][C]0.0976[/C][C]-0.1986[/C][C]0.0576[/C][C]564783.4855[/C][C]111108.8267[/C][C]333.3299[/C][/ROW]
[ROW][C]56[/C][C]0.1059[/C][C]-0.1948[/C][C]0.0772[/C][C]543300.2137[/C][C]172850.4534[/C][C]415.7529[/C][/ROW]
[ROW][C]57[/C][C]0.1137[/C][C]-0.2172[/C][C]0.0947[/C][C]675320.2192[/C][C]235659.1741[/C][C]485.4474[/C][/ROW]
[ROW][C]58[/C][C]0.1209[/C][C]-0.4192[/C][C]0.1307[/C][C]2516343.3565[/C][C]489068.5277[/C][C]699.3343[/C][/ROW]
[ROW][C]59[/C][C]0.1278[/C][C]-0.4677[/C][C]0.1644[/C][C]3131727.037[/C][C]753334.3786[/C][C]867.9484[/C][/ROW]
[ROW][C]60[/C][C]0.1343[/C][C]-0.5077[/C][C]0.1956[/C][C]3691349.9674[/C][C]1020426.7049[/C][C]1010.1617[/C][/ROW]
[ROW][C]61[/C][C]0.1405[/C][C]-0.4965[/C][C]0.2207[/C][C]3529546.0835[/C][C]1229519.9864[/C][C]1108.8372[/C][/ROW]
[ROW][C]62[/C][C]0.1464[/C][C]-0.5214[/C][C]0.2438[/C][C]3893236.5557[/C][C]1434421.261[/C][C]1197.6733[/C][/ROW]
[ROW][C]63[/C][C]0.1521[/C][C]-0.5587[/C][C]0.2663[/C][C]4469201.5726[/C][C]1651191.2833[/C][C]1284.9869[/C][/ROW]
[ROW][C]64[/C][C]0.1576[/C][C]-0.5073[/C][C]0.2824[/C][C]3685166.0085[/C][C]1786789.5983[/C][C]1336.7085[/C][/ROW]
[ROW][C]65[/C][C]0.1629[/C][C]-0.4577[/C][C]0.2933[/C][C]3000165.6334[/C][C]1862625.6005[/C][C]1364.7804[/C][/ROW]
[ROW][C]66[/C][C]0.1681[/C][C]-0.4637[/C][C]0.3034[/C][C]3078335.592[/C][C]1934137.9529[/C][C]1390.7329[/C][/ROW]
[ROW][C]67[/C][C]0.1731[/C][C]-0.4528[/C][C]0.3117[/C][C]2935357.8994[/C][C]1989761.2833[/C][C]1410.589[/C][/ROW]
[ROW][C]68[/C][C]0.1779[/C][C]-0.3939[/C][C]0.316[/C][C]2222212.1932[/C][C]2001995.5417[/C][C]1414.9189[/C][/ROW]
[ROW][C]69[/C][C]0.1826[/C][C]-0.3543[/C][C]0.3179[/C][C]1797875.0249[/C][C]1991789.5159[/C][C]1411.3077[/C][/ROW]
[ROW][C]70[/C][C]0.1873[/C][C]-0.3359[/C][C]0.3188[/C][C]1615310.3976[/C][C]1973861.9388[/C][C]1404.942[/C][/ROW]
[ROW][C]71[/C][C]0.1917[/C][C]-0.3481[/C][C]0.3201[/C][C]1735012.2076[/C][C]1963005.1328[/C][C]1401.0729[/C][/ROW]
[ROW][C]72[/C][C]0.1961[/C][C]-0.3386[/C][C]0.3209[/C][C]1642136.1977[/C][C]1949054.3096[/C][C]1396.0854[/C][/ROW]
[ROW][C]73[/C][C]0.2004[/C][C]-0.3288[/C][C]0.3212[/C][C]1548055.093[/C][C]1932346.0089[/C][C]1390.0885[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113903&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113903&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
500.0337-0.019505490.530200
510.0523-0.02950.024512478.71028984.620294.7872
520.06640.01930.02285349.49057772.910388.1641
530.07820.00450.0182284.79315900.88176.8172
540.0884-0.07390.029378265.950420373.8949142.7372
550.0976-0.19860.0576564783.4855111108.8267333.3299
560.1059-0.19480.0772543300.2137172850.4534415.7529
570.1137-0.21720.0947675320.2192235659.1741485.4474
580.1209-0.41920.13072516343.3565489068.5277699.3343
590.1278-0.46770.16443131727.037753334.3786867.9484
600.1343-0.50770.19563691349.96741020426.70491010.1617
610.1405-0.49650.22073529546.08351229519.98641108.8372
620.1464-0.52140.24383893236.55571434421.2611197.6733
630.1521-0.55870.26634469201.57261651191.28331284.9869
640.1576-0.50730.28243685166.00851786789.59831336.7085
650.1629-0.45770.29333000165.63341862625.60051364.7804
660.1681-0.46370.30343078335.5921934137.95291390.7329
670.1731-0.45280.31172935357.89941989761.28331410.589
680.1779-0.39390.3162222212.19322001995.54171414.9189
690.1826-0.35430.31791797875.02491991789.51591411.3077
700.1873-0.33590.31881615310.39761973861.93881404.942
710.1917-0.34810.32011735012.20761963005.13281401.0729
720.1961-0.33860.32091642136.19771949054.30961396.0854
730.2004-0.32880.32121548055.0931932346.00891390.0885



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