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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_arimaforecasting.wasp
Title produced by softwareARIMA Forecasting
Date of computationThu, 10 Dec 2009 11:58:40 -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/t12604715990exg65epzo23e1l.htm/, Retrieved Fri, 29 Mar 2024 10:23:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65729, Retrieved Fri, 29 Mar 2024 10:23:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsws 10 forcasting
Estimated Impact167
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [ARIMA Forecasting] [] [2009-12-07 09:54:52] [b98453cac15ba1066b407e146608df68]
-   PD    [ARIMA Forecasting] [ws 10 forcasting] [2009-12-10 18:58:40] [88e98f4c87ea17c4967db8279bda8533] [Current]
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Dataseries X:
8.2
8.0
7.5
6.8
6.5
6.6
7.6
8.0
8.1
7.7
7.5
7.6
7.8
7.8
7.8
7.5
7.5
7.1
7.5
7.5
7.6
7.7
7.7
7.9
8.1
8.2
8.2
8.2
7.9
7.3
6.9
6.6
6.7
6.9
7.0
7.1
7.2
7.1
6.9
7.0
6.8
6.4
6.7
6.6
6.4
6.3
6.2
6.5
6.8
6.8
6.4
6.1
5.8
6.1
7.2
7.3
6.9
6.1
5.8
6.2
7.1
7.7
7.9
7.7
7.4
7.5
8.0
8.1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65729&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65729&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65729&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'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[40])
288.2-------
297.9-------
307.3-------
316.9-------
326.6-------
336.7-------
346.9-------
357-------
367.1-------
377.2-------
387.1-------
396.9-------
407-------
416.86.96826.65477.28160.14650.421100.4211
426.47.04596.46547.62640.01460.79680.19550.5616
436.76.86775.98847.7470.35420.85140.47130.3841
446.66.70185.67747.72630.42280.50140.57720.2842
456.46.70155.62717.77590.29120.57340.50110.293
466.36.66095.57097.75080.25820.68050.33360.271
476.26.64435.54957.73910.21320.73120.26210.2621
486.56.60925.50777.71070.42290.76670.19130.2434
496.86.61835.50867.7280.37420.58280.15210.2501
506.86.51815.40027.6360.31060.31060.15380.1991
516.46.45795.33227.58360.45980.27570.22070.1726
526.16.55815.42497.69130.21410.60780.22230.2223
535.86.77325.57777.96880.05530.86510.48250.355
546.17.01645.71238.32040.08420.96620.82290.5098
557.27.07545.61498.53580.43360.90470.69280.5403
567.37.04635.48578.60690.3750.42350.71240.5232
576.97.01575.40668.62480.4440.36450.77340.5076
586.17.00445.36848.64030.13930.54970.80060.5021
595.87.00385.34778.65990.07710.85760.82930.5018
606.27.00525.33078.67960.1730.92080.72280.5024
617.17.00565.31358.69760.45650.82460.59410.5026
627.77.00535.29638.71430.21280.45680.59310.5024
637.97.0055.27968.73040.15460.21490.7540.5023
647.77.00485.26348.74630.2170.15680.84570.5022
657.47.00485.24758.7620.32970.2190.91050.5021
667.57.00485.23198.77760.2920.33110.84140.5021
6787.00485.21648.79310.13770.29360.41530.5021
688.17.00485.20118.80840.1170.13970.37420.5021

\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[40]) \tabularnewline
28 & 8.2 & - & - & - & - & - & - & - \tabularnewline
29 & 7.9 & - & - & - & - & - & - & - \tabularnewline
30 & 7.3 & - & - & - & - & - & - & - \tabularnewline
31 & 6.9 & - & - & - & - & - & - & - \tabularnewline
32 & 6.6 & - & - & - & - & - & - & - \tabularnewline
33 & 6.7 & - & - & - & - & - & - & - \tabularnewline
34 & 6.9 & - & - & - & - & - & - & - \tabularnewline
35 & 7 & - & - & - & - & - & - & - \tabularnewline
36 & 7.1 & - & - & - & - & - & - & - \tabularnewline
37 & 7.2 & - & - & - & - & - & - & - \tabularnewline
38 & 7.1 & - & - & - & - & - & - & - \tabularnewline
39 & 6.9 & - & - & - & - & - & - & - \tabularnewline
40 & 7 & - & - & - & - & - & - & - \tabularnewline
41 & 6.8 & 6.9682 & 6.6547 & 7.2816 & 0.1465 & 0.4211 & 0 & 0.4211 \tabularnewline
42 & 6.4 & 7.0459 & 6.4654 & 7.6264 & 0.0146 & 0.7968 & 0.1955 & 0.5616 \tabularnewline
43 & 6.7 & 6.8677 & 5.9884 & 7.747 & 0.3542 & 0.8514 & 0.4713 & 0.3841 \tabularnewline
44 & 6.6 & 6.7018 & 5.6774 & 7.7263 & 0.4228 & 0.5014 & 0.5772 & 0.2842 \tabularnewline
45 & 6.4 & 6.7015 & 5.6271 & 7.7759 & 0.2912 & 0.5734 & 0.5011 & 0.293 \tabularnewline
46 & 6.3 & 6.6609 & 5.5709 & 7.7508 & 0.2582 & 0.6805 & 0.3336 & 0.271 \tabularnewline
47 & 6.2 & 6.6443 & 5.5495 & 7.7391 & 0.2132 & 0.7312 & 0.2621 & 0.2621 \tabularnewline
48 & 6.5 & 6.6092 & 5.5077 & 7.7107 & 0.4229 & 0.7667 & 0.1913 & 0.2434 \tabularnewline
49 & 6.8 & 6.6183 & 5.5086 & 7.728 & 0.3742 & 0.5828 & 0.1521 & 0.2501 \tabularnewline
50 & 6.8 & 6.5181 & 5.4002 & 7.636 & 0.3106 & 0.3106 & 0.1538 & 0.1991 \tabularnewline
51 & 6.4 & 6.4579 & 5.3322 & 7.5836 & 0.4598 & 0.2757 & 0.2207 & 0.1726 \tabularnewline
52 & 6.1 & 6.5581 & 5.4249 & 7.6913 & 0.2141 & 0.6078 & 0.2223 & 0.2223 \tabularnewline
53 & 5.8 & 6.7732 & 5.5777 & 7.9688 & 0.0553 & 0.8651 & 0.4825 & 0.355 \tabularnewline
54 & 6.1 & 7.0164 & 5.7123 & 8.3204 & 0.0842 & 0.9662 & 0.8229 & 0.5098 \tabularnewline
55 & 7.2 & 7.0754 & 5.6149 & 8.5358 & 0.4336 & 0.9047 & 0.6928 & 0.5403 \tabularnewline
56 & 7.3 & 7.0463 & 5.4857 & 8.6069 & 0.375 & 0.4235 & 0.7124 & 0.5232 \tabularnewline
57 & 6.9 & 7.0157 & 5.4066 & 8.6248 & 0.444 & 0.3645 & 0.7734 & 0.5076 \tabularnewline
58 & 6.1 & 7.0044 & 5.3684 & 8.6403 & 0.1393 & 0.5497 & 0.8006 & 0.5021 \tabularnewline
59 & 5.8 & 7.0038 & 5.3477 & 8.6599 & 0.0771 & 0.8576 & 0.8293 & 0.5018 \tabularnewline
60 & 6.2 & 7.0052 & 5.3307 & 8.6796 & 0.173 & 0.9208 & 0.7228 & 0.5024 \tabularnewline
61 & 7.1 & 7.0056 & 5.3135 & 8.6976 & 0.4565 & 0.8246 & 0.5941 & 0.5026 \tabularnewline
62 & 7.7 & 7.0053 & 5.2963 & 8.7143 & 0.2128 & 0.4568 & 0.5931 & 0.5024 \tabularnewline
63 & 7.9 & 7.005 & 5.2796 & 8.7304 & 0.1546 & 0.2149 & 0.754 & 0.5023 \tabularnewline
64 & 7.7 & 7.0048 & 5.2634 & 8.7463 & 0.217 & 0.1568 & 0.8457 & 0.5022 \tabularnewline
65 & 7.4 & 7.0048 & 5.2475 & 8.762 & 0.3297 & 0.219 & 0.9105 & 0.5021 \tabularnewline
66 & 7.5 & 7.0048 & 5.2319 & 8.7776 & 0.292 & 0.3311 & 0.8414 & 0.5021 \tabularnewline
67 & 8 & 7.0048 & 5.2164 & 8.7931 & 0.1377 & 0.2936 & 0.4153 & 0.5021 \tabularnewline
68 & 8.1 & 7.0048 & 5.2011 & 8.8084 & 0.117 & 0.1397 & 0.3742 & 0.5021 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65729&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[40])[/C][/ROW]
[ROW][C]28[/C][C]8.2[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]29[/C][C]7.9[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]30[/C][C]7.3[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]31[/C][C]6.9[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]32[/C][C]6.6[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]33[/C][C]6.7[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]34[/C][C]6.9[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]35[/C][C]7[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]36[/C][C]7.1[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]37[/C][C]7.2[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]38[/C][C]7.1[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]39[/C][C]6.9[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]40[/C][C]7[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]41[/C][C]6.8[/C][C]6.9682[/C][C]6.6547[/C][C]7.2816[/C][C]0.1465[/C][C]0.4211[/C][C]0[/C][C]0.4211[/C][/ROW]
[ROW][C]42[/C][C]6.4[/C][C]7.0459[/C][C]6.4654[/C][C]7.6264[/C][C]0.0146[/C][C]0.7968[/C][C]0.1955[/C][C]0.5616[/C][/ROW]
[ROW][C]43[/C][C]6.7[/C][C]6.8677[/C][C]5.9884[/C][C]7.747[/C][C]0.3542[/C][C]0.8514[/C][C]0.4713[/C][C]0.3841[/C][/ROW]
[ROW][C]44[/C][C]6.6[/C][C]6.7018[/C][C]5.6774[/C][C]7.7263[/C][C]0.4228[/C][C]0.5014[/C][C]0.5772[/C][C]0.2842[/C][/ROW]
[ROW][C]45[/C][C]6.4[/C][C]6.7015[/C][C]5.6271[/C][C]7.7759[/C][C]0.2912[/C][C]0.5734[/C][C]0.5011[/C][C]0.293[/C][/ROW]
[ROW][C]46[/C][C]6.3[/C][C]6.6609[/C][C]5.5709[/C][C]7.7508[/C][C]0.2582[/C][C]0.6805[/C][C]0.3336[/C][C]0.271[/C][/ROW]
[ROW][C]47[/C][C]6.2[/C][C]6.6443[/C][C]5.5495[/C][C]7.7391[/C][C]0.2132[/C][C]0.7312[/C][C]0.2621[/C][C]0.2621[/C][/ROW]
[ROW][C]48[/C][C]6.5[/C][C]6.6092[/C][C]5.5077[/C][C]7.7107[/C][C]0.4229[/C][C]0.7667[/C][C]0.1913[/C][C]0.2434[/C][/ROW]
[ROW][C]49[/C][C]6.8[/C][C]6.6183[/C][C]5.5086[/C][C]7.728[/C][C]0.3742[/C][C]0.5828[/C][C]0.1521[/C][C]0.2501[/C][/ROW]
[ROW][C]50[/C][C]6.8[/C][C]6.5181[/C][C]5.4002[/C][C]7.636[/C][C]0.3106[/C][C]0.3106[/C][C]0.1538[/C][C]0.1991[/C][/ROW]
[ROW][C]51[/C][C]6.4[/C][C]6.4579[/C][C]5.3322[/C][C]7.5836[/C][C]0.4598[/C][C]0.2757[/C][C]0.2207[/C][C]0.1726[/C][/ROW]
[ROW][C]52[/C][C]6.1[/C][C]6.5581[/C][C]5.4249[/C][C]7.6913[/C][C]0.2141[/C][C]0.6078[/C][C]0.2223[/C][C]0.2223[/C][/ROW]
[ROW][C]53[/C][C]5.8[/C][C]6.7732[/C][C]5.5777[/C][C]7.9688[/C][C]0.0553[/C][C]0.8651[/C][C]0.4825[/C][C]0.355[/C][/ROW]
[ROW][C]54[/C][C]6.1[/C][C]7.0164[/C][C]5.7123[/C][C]8.3204[/C][C]0.0842[/C][C]0.9662[/C][C]0.8229[/C][C]0.5098[/C][/ROW]
[ROW][C]55[/C][C]7.2[/C][C]7.0754[/C][C]5.6149[/C][C]8.5358[/C][C]0.4336[/C][C]0.9047[/C][C]0.6928[/C][C]0.5403[/C][/ROW]
[ROW][C]56[/C][C]7.3[/C][C]7.0463[/C][C]5.4857[/C][C]8.6069[/C][C]0.375[/C][C]0.4235[/C][C]0.7124[/C][C]0.5232[/C][/ROW]
[ROW][C]57[/C][C]6.9[/C][C]7.0157[/C][C]5.4066[/C][C]8.6248[/C][C]0.444[/C][C]0.3645[/C][C]0.7734[/C][C]0.5076[/C][/ROW]
[ROW][C]58[/C][C]6.1[/C][C]7.0044[/C][C]5.3684[/C][C]8.6403[/C][C]0.1393[/C][C]0.5497[/C][C]0.8006[/C][C]0.5021[/C][/ROW]
[ROW][C]59[/C][C]5.8[/C][C]7.0038[/C][C]5.3477[/C][C]8.6599[/C][C]0.0771[/C][C]0.8576[/C][C]0.8293[/C][C]0.5018[/C][/ROW]
[ROW][C]60[/C][C]6.2[/C][C]7.0052[/C][C]5.3307[/C][C]8.6796[/C][C]0.173[/C][C]0.9208[/C][C]0.7228[/C][C]0.5024[/C][/ROW]
[ROW][C]61[/C][C]7.1[/C][C]7.0056[/C][C]5.3135[/C][C]8.6976[/C][C]0.4565[/C][C]0.8246[/C][C]0.5941[/C][C]0.5026[/C][/ROW]
[ROW][C]62[/C][C]7.7[/C][C]7.0053[/C][C]5.2963[/C][C]8.7143[/C][C]0.2128[/C][C]0.4568[/C][C]0.5931[/C][C]0.5024[/C][/ROW]
[ROW][C]63[/C][C]7.9[/C][C]7.005[/C][C]5.2796[/C][C]8.7304[/C][C]0.1546[/C][C]0.2149[/C][C]0.754[/C][C]0.5023[/C][/ROW]
[ROW][C]64[/C][C]7.7[/C][C]7.0048[/C][C]5.2634[/C][C]8.7463[/C][C]0.217[/C][C]0.1568[/C][C]0.8457[/C][C]0.5022[/C][/ROW]
[ROW][C]65[/C][C]7.4[/C][C]7.0048[/C][C]5.2475[/C][C]8.762[/C][C]0.3297[/C][C]0.219[/C][C]0.9105[/C][C]0.5021[/C][/ROW]
[ROW][C]66[/C][C]7.5[/C][C]7.0048[/C][C]5.2319[/C][C]8.7776[/C][C]0.292[/C][C]0.3311[/C][C]0.8414[/C][C]0.5021[/C][/ROW]
[ROW][C]67[/C][C]8[/C][C]7.0048[/C][C]5.2164[/C][C]8.7931[/C][C]0.1377[/C][C]0.2936[/C][C]0.4153[/C][C]0.5021[/C][/ROW]
[ROW][C]68[/C][C]8.1[/C][C]7.0048[/C][C]5.2011[/C][C]8.8084[/C][C]0.117[/C][C]0.1397[/C][C]0.3742[/C][C]0.5021[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65729&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65729&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[40])
288.2-------
297.9-------
307.3-------
316.9-------
326.6-------
336.7-------
346.9-------
357-------
367.1-------
377.2-------
387.1-------
396.9-------
407-------
416.86.96826.65477.28160.14650.421100.4211
426.47.04596.46547.62640.01460.79680.19550.5616
436.76.86775.98847.7470.35420.85140.47130.3841
446.66.70185.67747.72630.42280.50140.57720.2842
456.46.70155.62717.77590.29120.57340.50110.293
466.36.66095.57097.75080.25820.68050.33360.271
476.26.64435.54957.73910.21320.73120.26210.2621
486.56.60925.50777.71070.42290.76670.19130.2434
496.86.61835.50867.7280.37420.58280.15210.2501
506.86.51815.40027.6360.31060.31060.15380.1991
516.46.45795.33227.58360.45980.27570.22070.1726
526.16.55815.42497.69130.21410.60780.22230.2223
535.86.77325.57777.96880.05530.86510.48250.355
546.17.01645.71238.32040.08420.96620.82290.5098
557.27.07545.61498.53580.43360.90470.69280.5403
567.37.04635.48578.60690.3750.42350.71240.5232
576.97.01575.40668.62480.4440.36450.77340.5076
586.17.00445.36848.64030.13930.54970.80060.5021
595.87.00385.34778.65990.07710.85760.82930.5018
606.27.00525.33078.67960.1730.92080.72280.5024
617.17.00565.31358.69760.45650.82460.59410.5026
627.77.00535.29638.71430.21280.45680.59310.5024
637.97.0055.27968.73040.15460.21490.7540.5023
647.77.00485.26348.74630.2170.15680.84570.5022
657.47.00485.24758.7620.32970.2190.91050.5021
667.57.00485.23198.77760.2920.33110.84140.5021
6787.00485.21648.79310.13770.29360.41530.5021
688.17.00485.20118.80840.1170.13970.37420.5021







Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
410.023-0.024100.028300
420.042-0.09170.05790.41720.22270.4719
430.0653-0.02440.04670.02810.15790.3973
440.078-0.01520.03890.01040.1210.3478
450.0818-0.0450.04010.09090.1150.3391
460.0835-0.05420.04240.13020.11750.3428
470.0841-0.06690.04590.19740.12890.3591
480.085-0.01650.04220.01190.11430.3381
490.08550.02740.04060.0330.10530.3245
500.08750.04320.04090.07950.10270.3205
510.0889-0.0090.0380.00340.09370.306
520.0882-0.06990.04060.20990.10330.3215
530.0901-0.14370.04860.94720.16830.4102
540.0948-0.13060.05440.83970.21620.465
550.10530.01760.0520.01550.20280.4504
560.1130.0360.0510.06440.19420.4407
570.117-0.01650.04890.01340.18350.4284
580.1192-0.12910.05340.81790.21880.4677
590.1206-0.17190.05961.44920.28350.5325
600.122-0.11490.06240.64830.30180.5493
610.12320.01350.06010.00890.28780.5365
620.12450.09920.06180.48260.29670.5447
630.12570.12780.06470.80110.31860.5645
640.12680.09920.06610.48330.32550.5705
650.1280.05640.06580.15620.31870.5645
660.12910.07070.06590.24520.31590.562
670.13030.14210.06880.99050.34090.5838
680.13140.15640.07191.19950.37150.6095

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
41 & 0.023 & -0.0241 & 0 & 0.0283 & 0 & 0 \tabularnewline
42 & 0.042 & -0.0917 & 0.0579 & 0.4172 & 0.2227 & 0.4719 \tabularnewline
43 & 0.0653 & -0.0244 & 0.0467 & 0.0281 & 0.1579 & 0.3973 \tabularnewline
44 & 0.078 & -0.0152 & 0.0389 & 0.0104 & 0.121 & 0.3478 \tabularnewline
45 & 0.0818 & -0.045 & 0.0401 & 0.0909 & 0.115 & 0.3391 \tabularnewline
46 & 0.0835 & -0.0542 & 0.0424 & 0.1302 & 0.1175 & 0.3428 \tabularnewline
47 & 0.0841 & -0.0669 & 0.0459 & 0.1974 & 0.1289 & 0.3591 \tabularnewline
48 & 0.085 & -0.0165 & 0.0422 & 0.0119 & 0.1143 & 0.3381 \tabularnewline
49 & 0.0855 & 0.0274 & 0.0406 & 0.033 & 0.1053 & 0.3245 \tabularnewline
50 & 0.0875 & 0.0432 & 0.0409 & 0.0795 & 0.1027 & 0.3205 \tabularnewline
51 & 0.0889 & -0.009 & 0.038 & 0.0034 & 0.0937 & 0.306 \tabularnewline
52 & 0.0882 & -0.0699 & 0.0406 & 0.2099 & 0.1033 & 0.3215 \tabularnewline
53 & 0.0901 & -0.1437 & 0.0486 & 0.9472 & 0.1683 & 0.4102 \tabularnewline
54 & 0.0948 & -0.1306 & 0.0544 & 0.8397 & 0.2162 & 0.465 \tabularnewline
55 & 0.1053 & 0.0176 & 0.052 & 0.0155 & 0.2028 & 0.4504 \tabularnewline
56 & 0.113 & 0.036 & 0.051 & 0.0644 & 0.1942 & 0.4407 \tabularnewline
57 & 0.117 & -0.0165 & 0.0489 & 0.0134 & 0.1835 & 0.4284 \tabularnewline
58 & 0.1192 & -0.1291 & 0.0534 & 0.8179 & 0.2188 & 0.4677 \tabularnewline
59 & 0.1206 & -0.1719 & 0.0596 & 1.4492 & 0.2835 & 0.5325 \tabularnewline
60 & 0.122 & -0.1149 & 0.0624 & 0.6483 & 0.3018 & 0.5493 \tabularnewline
61 & 0.1232 & 0.0135 & 0.0601 & 0.0089 & 0.2878 & 0.5365 \tabularnewline
62 & 0.1245 & 0.0992 & 0.0618 & 0.4826 & 0.2967 & 0.5447 \tabularnewline
63 & 0.1257 & 0.1278 & 0.0647 & 0.8011 & 0.3186 & 0.5645 \tabularnewline
64 & 0.1268 & 0.0992 & 0.0661 & 0.4833 & 0.3255 & 0.5705 \tabularnewline
65 & 0.128 & 0.0564 & 0.0658 & 0.1562 & 0.3187 & 0.5645 \tabularnewline
66 & 0.1291 & 0.0707 & 0.0659 & 0.2452 & 0.3159 & 0.562 \tabularnewline
67 & 0.1303 & 0.1421 & 0.0688 & 0.9905 & 0.3409 & 0.5838 \tabularnewline
68 & 0.1314 & 0.1564 & 0.0719 & 1.1995 & 0.3715 & 0.6095 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65729&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]41[/C][C]0.023[/C][C]-0.0241[/C][C]0[/C][C]0.0283[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]42[/C][C]0.042[/C][C]-0.0917[/C][C]0.0579[/C][C]0.4172[/C][C]0.2227[/C][C]0.4719[/C][/ROW]
[ROW][C]43[/C][C]0.0653[/C][C]-0.0244[/C][C]0.0467[/C][C]0.0281[/C][C]0.1579[/C][C]0.3973[/C][/ROW]
[ROW][C]44[/C][C]0.078[/C][C]-0.0152[/C][C]0.0389[/C][C]0.0104[/C][C]0.121[/C][C]0.3478[/C][/ROW]
[ROW][C]45[/C][C]0.0818[/C][C]-0.045[/C][C]0.0401[/C][C]0.0909[/C][C]0.115[/C][C]0.3391[/C][/ROW]
[ROW][C]46[/C][C]0.0835[/C][C]-0.0542[/C][C]0.0424[/C][C]0.1302[/C][C]0.1175[/C][C]0.3428[/C][/ROW]
[ROW][C]47[/C][C]0.0841[/C][C]-0.0669[/C][C]0.0459[/C][C]0.1974[/C][C]0.1289[/C][C]0.3591[/C][/ROW]
[ROW][C]48[/C][C]0.085[/C][C]-0.0165[/C][C]0.0422[/C][C]0.0119[/C][C]0.1143[/C][C]0.3381[/C][/ROW]
[ROW][C]49[/C][C]0.0855[/C][C]0.0274[/C][C]0.0406[/C][C]0.033[/C][C]0.1053[/C][C]0.3245[/C][/ROW]
[ROW][C]50[/C][C]0.0875[/C][C]0.0432[/C][C]0.0409[/C][C]0.0795[/C][C]0.1027[/C][C]0.3205[/C][/ROW]
[ROW][C]51[/C][C]0.0889[/C][C]-0.009[/C][C]0.038[/C][C]0.0034[/C][C]0.0937[/C][C]0.306[/C][/ROW]
[ROW][C]52[/C][C]0.0882[/C][C]-0.0699[/C][C]0.0406[/C][C]0.2099[/C][C]0.1033[/C][C]0.3215[/C][/ROW]
[ROW][C]53[/C][C]0.0901[/C][C]-0.1437[/C][C]0.0486[/C][C]0.9472[/C][C]0.1683[/C][C]0.4102[/C][/ROW]
[ROW][C]54[/C][C]0.0948[/C][C]-0.1306[/C][C]0.0544[/C][C]0.8397[/C][C]0.2162[/C][C]0.465[/C][/ROW]
[ROW][C]55[/C][C]0.1053[/C][C]0.0176[/C][C]0.052[/C][C]0.0155[/C][C]0.2028[/C][C]0.4504[/C][/ROW]
[ROW][C]56[/C][C]0.113[/C][C]0.036[/C][C]0.051[/C][C]0.0644[/C][C]0.1942[/C][C]0.4407[/C][/ROW]
[ROW][C]57[/C][C]0.117[/C][C]-0.0165[/C][C]0.0489[/C][C]0.0134[/C][C]0.1835[/C][C]0.4284[/C][/ROW]
[ROW][C]58[/C][C]0.1192[/C][C]-0.1291[/C][C]0.0534[/C][C]0.8179[/C][C]0.2188[/C][C]0.4677[/C][/ROW]
[ROW][C]59[/C][C]0.1206[/C][C]-0.1719[/C][C]0.0596[/C][C]1.4492[/C][C]0.2835[/C][C]0.5325[/C][/ROW]
[ROW][C]60[/C][C]0.122[/C][C]-0.1149[/C][C]0.0624[/C][C]0.6483[/C][C]0.3018[/C][C]0.5493[/C][/ROW]
[ROW][C]61[/C][C]0.1232[/C][C]0.0135[/C][C]0.0601[/C][C]0.0089[/C][C]0.2878[/C][C]0.5365[/C][/ROW]
[ROW][C]62[/C][C]0.1245[/C][C]0.0992[/C][C]0.0618[/C][C]0.4826[/C][C]0.2967[/C][C]0.5447[/C][/ROW]
[ROW][C]63[/C][C]0.1257[/C][C]0.1278[/C][C]0.0647[/C][C]0.8011[/C][C]0.3186[/C][C]0.5645[/C][/ROW]
[ROW][C]64[/C][C]0.1268[/C][C]0.0992[/C][C]0.0661[/C][C]0.4833[/C][C]0.3255[/C][C]0.5705[/C][/ROW]
[ROW][C]65[/C][C]0.128[/C][C]0.0564[/C][C]0.0658[/C][C]0.1562[/C][C]0.3187[/C][C]0.5645[/C][/ROW]
[ROW][C]66[/C][C]0.1291[/C][C]0.0707[/C][C]0.0659[/C][C]0.2452[/C][C]0.3159[/C][C]0.562[/C][/ROW]
[ROW][C]67[/C][C]0.1303[/C][C]0.1421[/C][C]0.0688[/C][C]0.9905[/C][C]0.3409[/C][C]0.5838[/C][/ROW]
[ROW][C]68[/C][C]0.1314[/C][C]0.1564[/C][C]0.0719[/C][C]1.1995[/C][C]0.3715[/C][C]0.6095[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65729&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65729&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
410.023-0.024100.028300
420.042-0.09170.05790.41720.22270.4719
430.0653-0.02440.04670.02810.15790.3973
440.078-0.01520.03890.01040.1210.3478
450.0818-0.0450.04010.09090.1150.3391
460.0835-0.05420.04240.13020.11750.3428
470.0841-0.06690.04590.19740.12890.3591
480.085-0.01650.04220.01190.11430.3381
490.08550.02740.04060.0330.10530.3245
500.08750.04320.04090.07950.10270.3205
510.0889-0.0090.0380.00340.09370.306
520.0882-0.06990.04060.20990.10330.3215
530.0901-0.14370.04860.94720.16830.4102
540.0948-0.13060.05440.83970.21620.465
550.10530.01760.0520.01550.20280.4504
560.1130.0360.0510.06440.19420.4407
570.117-0.01650.04890.01340.18350.4284
580.1192-0.12910.05340.81790.21880.4677
590.1206-0.17190.05961.44920.28350.5325
600.122-0.11490.06240.64830.30180.5493
610.12320.01350.06010.00890.28780.5365
620.12450.09920.06180.48260.29670.5447
630.12570.12780.06470.80110.31860.5645
640.12680.09920.06610.48330.32550.5705
650.1280.05640.06580.15620.31870.5645
660.12910.07070.06590.24520.31590.562
670.13030.14210.06880.99050.34090.5838
680.13140.15640.07191.19950.37150.6095



Parameters (Session):
par1 = 24 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 0 ; par8 = 0 ; par9 = 1 ; par10 = FALSE ;
Parameters (R input):
par1 = 24 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 0 ; par8 = 0 ; par9 = 1 ; par10 = FALSE ;
R code (references can be found in the software module):
par1 <- as.numeric(par1) #cut off periods
par1 <- 28
par2 <- as.numeric(par2) #lambda
par3 <- as.numeric(par3) #degree of non-seasonal differencing
par4 <- as.numeric(par4) #degree of seasonal differencing
par5 <- as.numeric(par5) #seasonal period
par6 <- as.numeric(par6) #p
par6 <- 3
par7 <- as.numeric(par7) #q
par7 <- 3
par8 <- as.numeric(par8) #P
par9 <- as.numeric(par9) #Q
if (par10 == 'TRUE') par10 <- TRUE
if (par10 == 'FALSE') par10 <- FALSE
if (par2 == 0) x <- log(x)
if (par2 != 0) x <- x^par2
lx <- length(x)
first <- lx - 2*par1
nx <- lx - par1
nx1 <- nx + 1
fx <- lx - nx
if (fx < 1) {
fx <- par5
nx1 <- lx + fx - 1
first <- lx - 2*fx
}
first <- 1
if (fx < 3) fx <- round(lx/10,0)
(arima.out <- arima(x[1:nx], order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5), include.mean=par10, method='ML'))
(forecast <- predict(arima.out,par1))
(lb <- forecast$pred - 1.96 * forecast$se)
(ub <- forecast$pred + 1.96 * forecast$se)
if (par2 == 0) {
x <- exp(x)
forecast$pred <- exp(forecast$pred)
lb <- exp(lb)
ub <- exp(ub)
}
if (par2 != 0) {
x <- x^(1/par2)
forecast$pred <- forecast$pred^(1/par2)
lb <- lb^(1/par2)
ub <- ub^(1/par2)
}
if (par2 < 0) {
olb <- lb
lb <- ub
ub <- olb
}
(actandfor <- c(x[1:nx], forecast$pred))
(perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
bitmap(file='test1.png')
opar <- par(mar=c(4,4,2,2),las=1)
ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
plot(x,ylim=ylim,type='n',xlim=c(first,lx))
usr <- par('usr')
rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
abline(h= (-3:3)*2 , col ='gray', lty =3)
polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
lines(nx1:lx, lb , lty=2)
lines(nx1:lx, ub , lty=2)
lines(x, lwd=2)
lines(nx1:lx, forecast$pred , lwd=2 , col ='white')
box()
par(opar)
dev.off()
prob.dec <- array(NA, dim=fx)
prob.sdec <- array(NA, dim=fx)
prob.ldec <- array(NA, dim=fx)
prob.pval <- array(NA, dim=fx)
perf.pe <- array(0, dim=fx)
perf.mape <- array(0, dim=fx)
perf.mape1 <- array(0, dim=fx)
perf.se <- array(0, dim=fx)
perf.mse <- array(0, dim=fx)
perf.mse1 <- array(0, dim=fx)
perf.rmse <- array(0, dim=fx)
for (i in 1:fx) {
locSD <- (ub[i] - forecast$pred[i]) / 1.96
perf.pe[i] = (x[nx+i] - forecast$pred[i]) / forecast$pred[i]
perf.se[i] = (x[nx+i] - forecast$pred[i])^2
prob.dec[i] = pnorm((x[nx+i-1] - forecast$pred[i]) / locSD)
prob.sdec[i] = pnorm((x[nx+i-par5] - forecast$pred[i]) / locSD)
prob.ldec[i] = pnorm((x[nx] - forecast$pred[i]) / locSD)
prob.pval[i] = pnorm(abs(x[nx+i] - forecast$pred[i]) / locSD)
}
perf.mape[1] = abs(perf.pe[1])
perf.mse[1] = abs(perf.se[1])
for (i in 2:fx) {
perf.mape[i] = perf.mape[i-1] + abs(perf.pe[i])
perf.mape1[i] = perf.mape[i] / i
perf.mse[i] = perf.mse[i-1] + perf.se[i]
perf.mse1[i] = perf.mse[i] / i
}
perf.rmse = sqrt(perf.mse1)
bitmap(file='test2.png')
plot(forecast$pred, pch=19, type='b',main='ARIMA Extrapolation Forecast', ylab='Forecast and 95% CI', xlab='time',ylim=c(min(lb),max(ub)))
dum <- forecast$pred
dum[1:par1] <- x[(nx+1):lx]
lines(dum, lty=1)
lines(ub,lty=3)
lines(lb,lty=3)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Univariate ARIMA Extrapolation Forecast',9,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'time',1,header=TRUE)
a<-table.element(a,'Y[t]',1,header=TRUE)
a<-table.element(a,'F[t]',1,header=TRUE)
a<-table.element(a,'95% LB',1,header=TRUE)
a<-table.element(a,'95% UB',1,header=TRUE)
a<-table.element(a,'p-value
(H0: Y[t] = F[t])',1,header=TRUE)
a<-table.element(a,'P(F[t]>Y[t-1])',1,header=TRUE)
a<-table.element(a,'P(F[t]>Y[t-s])',1,header=TRUE)
mylab <- paste('P(F[t]>Y[',nx,sep='')
mylab <- paste(mylab,'])',sep='')
a<-table.element(a,mylab,1,header=TRUE)
a<-table.row.end(a)
for (i in (nx-par5):nx) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.row.end(a)
}
for (i in 1:fx) {
a<-table.row.start(a)
a<-table.element(a,nx+i,header=TRUE)
a<-table.element(a,round(x[nx+i],4))
a<-table.element(a,round(forecast$pred[i],4))
a<-table.element(a,round(lb[i],4))
a<-table.element(a,round(ub[i],4))
a<-table.element(a,round((1-prob.pval[i]),4))
a<-table.element(a,round((1-prob.dec[i]),4))
a<-table.element(a,round((1-prob.sdec[i]),4))
a<-table.element(a,round((1-prob.ldec[i]),4))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Univariate ARIMA Extrapolation Forecast Performance',7,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'time',1,header=TRUE)
a<-table.element(a,'% S.E.',1,header=TRUE)
a<-table.element(a,'PE',1,header=TRUE)
a<-table.element(a,'MAPE',1,header=TRUE)
a<-table.element(a,'Sq.E',1,header=TRUE)
a<-table.element(a,'MSE',1,header=TRUE)
a<-table.element(a,'RMSE',1,header=TRUE)
a<-table.row.end(a)
for (i in 1:fx) {
a<-table.row.start(a)
a<-table.element(a,nx+i,header=TRUE)
a<-table.element(a,round(perc.se[i],4))
a<-table.element(a,round(perf.pe[i],4))
a<-table.element(a,round(perf.mape1[i],4))
a<-table.element(a,round(perf.se[i],4))
a<-table.element(a,round(perf.mse1[i],4))
a<-table.element(a,round(perf.rmse[i],4))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')