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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 13:27:01 -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/t1260476862sxesh8m5lyiejsk.htm/, Retrieved Thu, 28 Mar 2024 14:19:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65790, Retrieved Thu, 28 Mar 2024 14:19:08 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact105
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] [] [2009-12-10 20:27:01] [0545e25c765ce26b196961216dc11e13] [Current]
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Dataseries X:
1,4
1,2
1
1,7
2,4
2
2,1
2
1,8
2,7
2,3
1,9
2
2,3
2,8
2,4
2,3
2,7
2,7
2,9
3
2,2
2,3
2,8
2,8
2,8
2,2
2,6
2,8
2,5
2,4
2,3
1,9
1,7
2
2,1
1,7
1,8
1,8
1,8
1,3
1,3
1,3
1,2
1,4
2,2
2,9
3,1
3,5
3,6
4,4
4,1
5,1
5,8
5,9
5,4
5,5
4,8
3,2
2,7




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=65790&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=65790&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65790&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[32])
202.9-------
213-------
222.2-------
232.3-------
242.8-------
252.8-------
262.8-------
272.2-------
282.6-------
292.8-------
302.5-------
312.4-------
322.3-------
331.92.47321.95362.99290.01530.74330.02350.7433
341.72.57031.8843.25660.00650.97220.85490.7799
3522.63371.91353.35390.04230.99450.81810.8181
362.12.46381.61613.31140.20020.85820.21840.6475
371.72.40141.38143.42140.08890.71880.22190.5772
381.82.46011.28983.63030.13450.89850.28460.6057
391.82.58791.36163.81420.1040.8960.73240.6773
401.82.57361.30463.84260.11610.88390.48380.6637
411.32.4741.13223.81570.04320.83760.31690.6003
421.32.41930.9633.87560.0660.9340.45670.5638
431.32.48920.94564.03290.06550.93450.54510.5949
441.22.5670.97584.15830.04610.94070.62890.6289
451.42.55130.92084.18180.08320.94790.78320.6187
462.22.47040.77654.16440.37720.89220.81360.5782
472.92.44450.6684.22090.30760.60630.68810.5633
483.12.50050.65844.34260.26180.33540.6650.5845
493.52.55430.67094.43770.16250.2850.8130.6043
503.62.5320.60944.45470.13810.16190.77220.5935
514.42.47340.4954.45180.02820.13220.74770.5682
524.12.46180.41764.50590.05810.03160.73710.5616
535.12.50790.41074.6050.00770.06840.87050.577
545.82.54220.40694.67750.00140.00940.87290.588
555.92.51960.34584.69350.00120.00150.86430.5785
565.42.47720.25334.70120.0050.00130.86980.5621
575.52.4750.19624.75380.00460.00590.82240.5598
584.82.51130.18724.83540.02680.00590.60350.5707
593.22.53240.17224.89260.28970.02980.38010.5765
602.72.51140.11364.90920.43870.28670.31520.5686

\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[32]) \tabularnewline
20 & 2.9 & - & - & - & - & - & - & - \tabularnewline
21 & 3 & - & - & - & - & - & - & - \tabularnewline
22 & 2.2 & - & - & - & - & - & - & - \tabularnewline
23 & 2.3 & - & - & - & - & - & - & - \tabularnewline
24 & 2.8 & - & - & - & - & - & - & - \tabularnewline
25 & 2.8 & - & - & - & - & - & - & - \tabularnewline
26 & 2.8 & - & - & - & - & - & - & - \tabularnewline
27 & 2.2 & - & - & - & - & - & - & - \tabularnewline
28 & 2.6 & - & - & - & - & - & - & - \tabularnewline
29 & 2.8 & - & - & - & - & - & - & - \tabularnewline
30 & 2.5 & - & - & - & - & - & - & - \tabularnewline
31 & 2.4 & - & - & - & - & - & - & - \tabularnewline
32 & 2.3 & - & - & - & - & - & - & - \tabularnewline
33 & 1.9 & 2.4732 & 1.9536 & 2.9929 & 0.0153 & 0.7433 & 0.0235 & 0.7433 \tabularnewline
34 & 1.7 & 2.5703 & 1.884 & 3.2566 & 0.0065 & 0.9722 & 0.8549 & 0.7799 \tabularnewline
35 & 2 & 2.6337 & 1.9135 & 3.3539 & 0.0423 & 0.9945 & 0.8181 & 0.8181 \tabularnewline
36 & 2.1 & 2.4638 & 1.6161 & 3.3114 & 0.2002 & 0.8582 & 0.2184 & 0.6475 \tabularnewline
37 & 1.7 & 2.4014 & 1.3814 & 3.4214 & 0.0889 & 0.7188 & 0.2219 & 0.5772 \tabularnewline
38 & 1.8 & 2.4601 & 1.2898 & 3.6303 & 0.1345 & 0.8985 & 0.2846 & 0.6057 \tabularnewline
39 & 1.8 & 2.5879 & 1.3616 & 3.8142 & 0.104 & 0.896 & 0.7324 & 0.6773 \tabularnewline
40 & 1.8 & 2.5736 & 1.3046 & 3.8426 & 0.1161 & 0.8839 & 0.4838 & 0.6637 \tabularnewline
41 & 1.3 & 2.474 & 1.1322 & 3.8157 & 0.0432 & 0.8376 & 0.3169 & 0.6003 \tabularnewline
42 & 1.3 & 2.4193 & 0.963 & 3.8756 & 0.066 & 0.934 & 0.4567 & 0.5638 \tabularnewline
43 & 1.3 & 2.4892 & 0.9456 & 4.0329 & 0.0655 & 0.9345 & 0.5451 & 0.5949 \tabularnewline
44 & 1.2 & 2.567 & 0.9758 & 4.1583 & 0.0461 & 0.9407 & 0.6289 & 0.6289 \tabularnewline
45 & 1.4 & 2.5513 & 0.9208 & 4.1818 & 0.0832 & 0.9479 & 0.7832 & 0.6187 \tabularnewline
46 & 2.2 & 2.4704 & 0.7765 & 4.1644 & 0.3772 & 0.8922 & 0.8136 & 0.5782 \tabularnewline
47 & 2.9 & 2.4445 & 0.668 & 4.2209 & 0.3076 & 0.6063 & 0.6881 & 0.5633 \tabularnewline
48 & 3.1 & 2.5005 & 0.6584 & 4.3426 & 0.2618 & 0.3354 & 0.665 & 0.5845 \tabularnewline
49 & 3.5 & 2.5543 & 0.6709 & 4.4377 & 0.1625 & 0.285 & 0.813 & 0.6043 \tabularnewline
50 & 3.6 & 2.532 & 0.6094 & 4.4547 & 0.1381 & 0.1619 & 0.7722 & 0.5935 \tabularnewline
51 & 4.4 & 2.4734 & 0.495 & 4.4518 & 0.0282 & 0.1322 & 0.7477 & 0.5682 \tabularnewline
52 & 4.1 & 2.4618 & 0.4176 & 4.5059 & 0.0581 & 0.0316 & 0.7371 & 0.5616 \tabularnewline
53 & 5.1 & 2.5079 & 0.4107 & 4.605 & 0.0077 & 0.0684 & 0.8705 & 0.577 \tabularnewline
54 & 5.8 & 2.5422 & 0.4069 & 4.6775 & 0.0014 & 0.0094 & 0.8729 & 0.588 \tabularnewline
55 & 5.9 & 2.5196 & 0.3458 & 4.6935 & 0.0012 & 0.0015 & 0.8643 & 0.5785 \tabularnewline
56 & 5.4 & 2.4772 & 0.2533 & 4.7012 & 0.005 & 0.0013 & 0.8698 & 0.5621 \tabularnewline
57 & 5.5 & 2.475 & 0.1962 & 4.7538 & 0.0046 & 0.0059 & 0.8224 & 0.5598 \tabularnewline
58 & 4.8 & 2.5113 & 0.1872 & 4.8354 & 0.0268 & 0.0059 & 0.6035 & 0.5707 \tabularnewline
59 & 3.2 & 2.5324 & 0.1722 & 4.8926 & 0.2897 & 0.0298 & 0.3801 & 0.5765 \tabularnewline
60 & 2.7 & 2.5114 & 0.1136 & 4.9092 & 0.4387 & 0.2867 & 0.3152 & 0.5686 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65790&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[32])[/C][/ROW]
[ROW][C]20[/C][C]2.9[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]21[/C][C]3[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]22[/C][C]2.2[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]23[/C][C]2.3[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]24[/C][C]2.8[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]25[/C][C]2.8[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]26[/C][C]2.8[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]27[/C][C]2.2[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]28[/C][C]2.6[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]29[/C][C]2.8[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]30[/C][C]2.5[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]31[/C][C]2.4[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]32[/C][C]2.3[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]33[/C][C]1.9[/C][C]2.4732[/C][C]1.9536[/C][C]2.9929[/C][C]0.0153[/C][C]0.7433[/C][C]0.0235[/C][C]0.7433[/C][/ROW]
[ROW][C]34[/C][C]1.7[/C][C]2.5703[/C][C]1.884[/C][C]3.2566[/C][C]0.0065[/C][C]0.9722[/C][C]0.8549[/C][C]0.7799[/C][/ROW]
[ROW][C]35[/C][C]2[/C][C]2.6337[/C][C]1.9135[/C][C]3.3539[/C][C]0.0423[/C][C]0.9945[/C][C]0.8181[/C][C]0.8181[/C][/ROW]
[ROW][C]36[/C][C]2.1[/C][C]2.4638[/C][C]1.6161[/C][C]3.3114[/C][C]0.2002[/C][C]0.8582[/C][C]0.2184[/C][C]0.6475[/C][/ROW]
[ROW][C]37[/C][C]1.7[/C][C]2.4014[/C][C]1.3814[/C][C]3.4214[/C][C]0.0889[/C][C]0.7188[/C][C]0.2219[/C][C]0.5772[/C][/ROW]
[ROW][C]38[/C][C]1.8[/C][C]2.4601[/C][C]1.2898[/C][C]3.6303[/C][C]0.1345[/C][C]0.8985[/C][C]0.2846[/C][C]0.6057[/C][/ROW]
[ROW][C]39[/C][C]1.8[/C][C]2.5879[/C][C]1.3616[/C][C]3.8142[/C][C]0.104[/C][C]0.896[/C][C]0.7324[/C][C]0.6773[/C][/ROW]
[ROW][C]40[/C][C]1.8[/C][C]2.5736[/C][C]1.3046[/C][C]3.8426[/C][C]0.1161[/C][C]0.8839[/C][C]0.4838[/C][C]0.6637[/C][/ROW]
[ROW][C]41[/C][C]1.3[/C][C]2.474[/C][C]1.1322[/C][C]3.8157[/C][C]0.0432[/C][C]0.8376[/C][C]0.3169[/C][C]0.6003[/C][/ROW]
[ROW][C]42[/C][C]1.3[/C][C]2.4193[/C][C]0.963[/C][C]3.8756[/C][C]0.066[/C][C]0.934[/C][C]0.4567[/C][C]0.5638[/C][/ROW]
[ROW][C]43[/C][C]1.3[/C][C]2.4892[/C][C]0.9456[/C][C]4.0329[/C][C]0.0655[/C][C]0.9345[/C][C]0.5451[/C][C]0.5949[/C][/ROW]
[ROW][C]44[/C][C]1.2[/C][C]2.567[/C][C]0.9758[/C][C]4.1583[/C][C]0.0461[/C][C]0.9407[/C][C]0.6289[/C][C]0.6289[/C][/ROW]
[ROW][C]45[/C][C]1.4[/C][C]2.5513[/C][C]0.9208[/C][C]4.1818[/C][C]0.0832[/C][C]0.9479[/C][C]0.7832[/C][C]0.6187[/C][/ROW]
[ROW][C]46[/C][C]2.2[/C][C]2.4704[/C][C]0.7765[/C][C]4.1644[/C][C]0.3772[/C][C]0.8922[/C][C]0.8136[/C][C]0.5782[/C][/ROW]
[ROW][C]47[/C][C]2.9[/C][C]2.4445[/C][C]0.668[/C][C]4.2209[/C][C]0.3076[/C][C]0.6063[/C][C]0.6881[/C][C]0.5633[/C][/ROW]
[ROW][C]48[/C][C]3.1[/C][C]2.5005[/C][C]0.6584[/C][C]4.3426[/C][C]0.2618[/C][C]0.3354[/C][C]0.665[/C][C]0.5845[/C][/ROW]
[ROW][C]49[/C][C]3.5[/C][C]2.5543[/C][C]0.6709[/C][C]4.4377[/C][C]0.1625[/C][C]0.285[/C][C]0.813[/C][C]0.6043[/C][/ROW]
[ROW][C]50[/C][C]3.6[/C][C]2.532[/C][C]0.6094[/C][C]4.4547[/C][C]0.1381[/C][C]0.1619[/C][C]0.7722[/C][C]0.5935[/C][/ROW]
[ROW][C]51[/C][C]4.4[/C][C]2.4734[/C][C]0.495[/C][C]4.4518[/C][C]0.0282[/C][C]0.1322[/C][C]0.7477[/C][C]0.5682[/C][/ROW]
[ROW][C]52[/C][C]4.1[/C][C]2.4618[/C][C]0.4176[/C][C]4.5059[/C][C]0.0581[/C][C]0.0316[/C][C]0.7371[/C][C]0.5616[/C][/ROW]
[ROW][C]53[/C][C]5.1[/C][C]2.5079[/C][C]0.4107[/C][C]4.605[/C][C]0.0077[/C][C]0.0684[/C][C]0.8705[/C][C]0.577[/C][/ROW]
[ROW][C]54[/C][C]5.8[/C][C]2.5422[/C][C]0.4069[/C][C]4.6775[/C][C]0.0014[/C][C]0.0094[/C][C]0.8729[/C][C]0.588[/C][/ROW]
[ROW][C]55[/C][C]5.9[/C][C]2.5196[/C][C]0.3458[/C][C]4.6935[/C][C]0.0012[/C][C]0.0015[/C][C]0.8643[/C][C]0.5785[/C][/ROW]
[ROW][C]56[/C][C]5.4[/C][C]2.4772[/C][C]0.2533[/C][C]4.7012[/C][C]0.005[/C][C]0.0013[/C][C]0.8698[/C][C]0.5621[/C][/ROW]
[ROW][C]57[/C][C]5.5[/C][C]2.475[/C][C]0.1962[/C][C]4.7538[/C][C]0.0046[/C][C]0.0059[/C][C]0.8224[/C][C]0.5598[/C][/ROW]
[ROW][C]58[/C][C]4.8[/C][C]2.5113[/C][C]0.1872[/C][C]4.8354[/C][C]0.0268[/C][C]0.0059[/C][C]0.6035[/C][C]0.5707[/C][/ROW]
[ROW][C]59[/C][C]3.2[/C][C]2.5324[/C][C]0.1722[/C][C]4.8926[/C][C]0.2897[/C][C]0.0298[/C][C]0.3801[/C][C]0.5765[/C][/ROW]
[ROW][C]60[/C][C]2.7[/C][C]2.5114[/C][C]0.1136[/C][C]4.9092[/C][C]0.4387[/C][C]0.2867[/C][C]0.3152[/C][C]0.5686[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65790&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65790&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[32])
202.9-------
213-------
222.2-------
232.3-------
242.8-------
252.8-------
262.8-------
272.2-------
282.6-------
292.8-------
302.5-------
312.4-------
322.3-------
331.92.47321.95362.99290.01530.74330.02350.7433
341.72.57031.8843.25660.00650.97220.85490.7799
3522.63371.91353.35390.04230.99450.81810.8181
362.12.46381.61613.31140.20020.85820.21840.6475
371.72.40141.38143.42140.08890.71880.22190.5772
381.82.46011.28983.63030.13450.89850.28460.6057
391.82.58791.36163.81420.1040.8960.73240.6773
401.82.57361.30463.84260.11610.88390.48380.6637
411.32.4741.13223.81570.04320.83760.31690.6003
421.32.41930.9633.87560.0660.9340.45670.5638
431.32.48920.94564.03290.06550.93450.54510.5949
441.22.5670.97584.15830.04610.94070.62890.6289
451.42.55130.92084.18180.08320.94790.78320.6187
462.22.47040.77654.16440.37720.89220.81360.5782
472.92.44450.6684.22090.30760.60630.68810.5633
483.12.50050.65844.34260.26180.33540.6650.5845
493.52.55430.67094.43770.16250.2850.8130.6043
503.62.5320.60944.45470.13810.16190.77220.5935
514.42.47340.4954.45180.02820.13220.74770.5682
524.12.46180.41764.50590.05810.03160.73710.5616
535.12.50790.41074.6050.00770.06840.87050.577
545.82.54220.40694.67750.00140.00940.87290.588
555.92.51960.34584.69350.00120.00150.86430.5785
565.42.47720.25334.70120.0050.00130.86980.5621
575.52.4750.19624.75380.00460.00590.82240.5598
584.82.51130.18724.83540.02680.00590.60350.5707
593.22.53240.17224.89260.28970.02980.38010.5765
602.72.51140.11364.90920.43870.28670.31520.5686







Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
330.1072-0.231800.328600
340.1362-0.33860.28520.75740.5430.7369
350.1395-0.24060.27030.40160.49590.7042
360.1755-0.14760.23970.13230.4050.6364
370.2167-0.29210.25010.4920.42240.6499
380.2427-0.26830.25320.43570.42460.6516
390.2418-0.30450.26050.62080.45260.6728
400.2516-0.30060.26550.59850.47080.6862
410.2767-0.47450.28871.37820.57170.7561
420.3071-0.46260.30611.25280.63980.7999
430.3164-0.47780.32171.41430.71020.8427
440.3163-0.53250.33931.86870.80670.8982
450.3261-0.45130.34791.32540.84660.9201
460.3498-0.10950.33090.07310.79140.8896
470.37080.18640.32120.20750.75250.8674
480.37590.23980.31610.35940.72790.8532
490.37620.37030.31930.89440.73770.8589
500.38740.42180.3251.14050.76010.8718
510.40810.77890.34893.71180.91540.9568
520.42370.66550.36472.68391.00381.0019
530.42661.03360.39666.71921.2761.1296
540.42851.28150.436810.61331.70041.304
550.44021.34160.476211.42692.12331.4572
560.4581.17990.50558.54262.39081.5462
570.46981.22220.53419.15072.66121.6313
580.47220.91140.54875.23832.76031.6614
590.47550.26360.53810.44572.67461.6354
600.48710.07510.52160.03562.58031.6063

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
33 & 0.1072 & -0.2318 & 0 & 0.3286 & 0 & 0 \tabularnewline
34 & 0.1362 & -0.3386 & 0.2852 & 0.7574 & 0.543 & 0.7369 \tabularnewline
35 & 0.1395 & -0.2406 & 0.2703 & 0.4016 & 0.4959 & 0.7042 \tabularnewline
36 & 0.1755 & -0.1476 & 0.2397 & 0.1323 & 0.405 & 0.6364 \tabularnewline
37 & 0.2167 & -0.2921 & 0.2501 & 0.492 & 0.4224 & 0.6499 \tabularnewline
38 & 0.2427 & -0.2683 & 0.2532 & 0.4357 & 0.4246 & 0.6516 \tabularnewline
39 & 0.2418 & -0.3045 & 0.2605 & 0.6208 & 0.4526 & 0.6728 \tabularnewline
40 & 0.2516 & -0.3006 & 0.2655 & 0.5985 & 0.4708 & 0.6862 \tabularnewline
41 & 0.2767 & -0.4745 & 0.2887 & 1.3782 & 0.5717 & 0.7561 \tabularnewline
42 & 0.3071 & -0.4626 & 0.3061 & 1.2528 & 0.6398 & 0.7999 \tabularnewline
43 & 0.3164 & -0.4778 & 0.3217 & 1.4143 & 0.7102 & 0.8427 \tabularnewline
44 & 0.3163 & -0.5325 & 0.3393 & 1.8687 & 0.8067 & 0.8982 \tabularnewline
45 & 0.3261 & -0.4513 & 0.3479 & 1.3254 & 0.8466 & 0.9201 \tabularnewline
46 & 0.3498 & -0.1095 & 0.3309 & 0.0731 & 0.7914 & 0.8896 \tabularnewline
47 & 0.3708 & 0.1864 & 0.3212 & 0.2075 & 0.7525 & 0.8674 \tabularnewline
48 & 0.3759 & 0.2398 & 0.3161 & 0.3594 & 0.7279 & 0.8532 \tabularnewline
49 & 0.3762 & 0.3703 & 0.3193 & 0.8944 & 0.7377 & 0.8589 \tabularnewline
50 & 0.3874 & 0.4218 & 0.325 & 1.1405 & 0.7601 & 0.8718 \tabularnewline
51 & 0.4081 & 0.7789 & 0.3489 & 3.7118 & 0.9154 & 0.9568 \tabularnewline
52 & 0.4237 & 0.6655 & 0.3647 & 2.6839 & 1.0038 & 1.0019 \tabularnewline
53 & 0.4266 & 1.0336 & 0.3966 & 6.7192 & 1.276 & 1.1296 \tabularnewline
54 & 0.4285 & 1.2815 & 0.4368 & 10.6133 & 1.7004 & 1.304 \tabularnewline
55 & 0.4402 & 1.3416 & 0.4762 & 11.4269 & 2.1233 & 1.4572 \tabularnewline
56 & 0.458 & 1.1799 & 0.5055 & 8.5426 & 2.3908 & 1.5462 \tabularnewline
57 & 0.4698 & 1.2222 & 0.5341 & 9.1507 & 2.6612 & 1.6313 \tabularnewline
58 & 0.4722 & 0.9114 & 0.5487 & 5.2383 & 2.7603 & 1.6614 \tabularnewline
59 & 0.4755 & 0.2636 & 0.5381 & 0.4457 & 2.6746 & 1.6354 \tabularnewline
60 & 0.4871 & 0.0751 & 0.5216 & 0.0356 & 2.5803 & 1.6063 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65790&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]33[/C][C]0.1072[/C][C]-0.2318[/C][C]0[/C][C]0.3286[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]34[/C][C]0.1362[/C][C]-0.3386[/C][C]0.2852[/C][C]0.7574[/C][C]0.543[/C][C]0.7369[/C][/ROW]
[ROW][C]35[/C][C]0.1395[/C][C]-0.2406[/C][C]0.2703[/C][C]0.4016[/C][C]0.4959[/C][C]0.7042[/C][/ROW]
[ROW][C]36[/C][C]0.1755[/C][C]-0.1476[/C][C]0.2397[/C][C]0.1323[/C][C]0.405[/C][C]0.6364[/C][/ROW]
[ROW][C]37[/C][C]0.2167[/C][C]-0.2921[/C][C]0.2501[/C][C]0.492[/C][C]0.4224[/C][C]0.6499[/C][/ROW]
[ROW][C]38[/C][C]0.2427[/C][C]-0.2683[/C][C]0.2532[/C][C]0.4357[/C][C]0.4246[/C][C]0.6516[/C][/ROW]
[ROW][C]39[/C][C]0.2418[/C][C]-0.3045[/C][C]0.2605[/C][C]0.6208[/C][C]0.4526[/C][C]0.6728[/C][/ROW]
[ROW][C]40[/C][C]0.2516[/C][C]-0.3006[/C][C]0.2655[/C][C]0.5985[/C][C]0.4708[/C][C]0.6862[/C][/ROW]
[ROW][C]41[/C][C]0.2767[/C][C]-0.4745[/C][C]0.2887[/C][C]1.3782[/C][C]0.5717[/C][C]0.7561[/C][/ROW]
[ROW][C]42[/C][C]0.3071[/C][C]-0.4626[/C][C]0.3061[/C][C]1.2528[/C][C]0.6398[/C][C]0.7999[/C][/ROW]
[ROW][C]43[/C][C]0.3164[/C][C]-0.4778[/C][C]0.3217[/C][C]1.4143[/C][C]0.7102[/C][C]0.8427[/C][/ROW]
[ROW][C]44[/C][C]0.3163[/C][C]-0.5325[/C][C]0.3393[/C][C]1.8687[/C][C]0.8067[/C][C]0.8982[/C][/ROW]
[ROW][C]45[/C][C]0.3261[/C][C]-0.4513[/C][C]0.3479[/C][C]1.3254[/C][C]0.8466[/C][C]0.9201[/C][/ROW]
[ROW][C]46[/C][C]0.3498[/C][C]-0.1095[/C][C]0.3309[/C][C]0.0731[/C][C]0.7914[/C][C]0.8896[/C][/ROW]
[ROW][C]47[/C][C]0.3708[/C][C]0.1864[/C][C]0.3212[/C][C]0.2075[/C][C]0.7525[/C][C]0.8674[/C][/ROW]
[ROW][C]48[/C][C]0.3759[/C][C]0.2398[/C][C]0.3161[/C][C]0.3594[/C][C]0.7279[/C][C]0.8532[/C][/ROW]
[ROW][C]49[/C][C]0.3762[/C][C]0.3703[/C][C]0.3193[/C][C]0.8944[/C][C]0.7377[/C][C]0.8589[/C][/ROW]
[ROW][C]50[/C][C]0.3874[/C][C]0.4218[/C][C]0.325[/C][C]1.1405[/C][C]0.7601[/C][C]0.8718[/C][/ROW]
[ROW][C]51[/C][C]0.4081[/C][C]0.7789[/C][C]0.3489[/C][C]3.7118[/C][C]0.9154[/C][C]0.9568[/C][/ROW]
[ROW][C]52[/C][C]0.4237[/C][C]0.6655[/C][C]0.3647[/C][C]2.6839[/C][C]1.0038[/C][C]1.0019[/C][/ROW]
[ROW][C]53[/C][C]0.4266[/C][C]1.0336[/C][C]0.3966[/C][C]6.7192[/C][C]1.276[/C][C]1.1296[/C][/ROW]
[ROW][C]54[/C][C]0.4285[/C][C]1.2815[/C][C]0.4368[/C][C]10.6133[/C][C]1.7004[/C][C]1.304[/C][/ROW]
[ROW][C]55[/C][C]0.4402[/C][C]1.3416[/C][C]0.4762[/C][C]11.4269[/C][C]2.1233[/C][C]1.4572[/C][/ROW]
[ROW][C]56[/C][C]0.458[/C][C]1.1799[/C][C]0.5055[/C][C]8.5426[/C][C]2.3908[/C][C]1.5462[/C][/ROW]
[ROW][C]57[/C][C]0.4698[/C][C]1.2222[/C][C]0.5341[/C][C]9.1507[/C][C]2.6612[/C][C]1.6313[/C][/ROW]
[ROW][C]58[/C][C]0.4722[/C][C]0.9114[/C][C]0.5487[/C][C]5.2383[/C][C]2.7603[/C][C]1.6614[/C][/ROW]
[ROW][C]59[/C][C]0.4755[/C][C]0.2636[/C][C]0.5381[/C][C]0.4457[/C][C]2.6746[/C][C]1.6354[/C][/ROW]
[ROW][C]60[/C][C]0.4871[/C][C]0.0751[/C][C]0.5216[/C][C]0.0356[/C][C]2.5803[/C][C]1.6063[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65790&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65790&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
330.1072-0.231800.328600
340.1362-0.33860.28520.75740.5430.7369
350.1395-0.24060.27030.40160.49590.7042
360.1755-0.14760.23970.13230.4050.6364
370.2167-0.29210.25010.4920.42240.6499
380.2427-0.26830.25320.43570.42460.6516
390.2418-0.30450.26050.62080.45260.6728
400.2516-0.30060.26550.59850.47080.6862
410.2767-0.47450.28871.37820.57170.7561
420.3071-0.46260.30611.25280.63980.7999
430.3164-0.47780.32171.41430.71020.8427
440.3163-0.53250.33931.86870.80670.8982
450.3261-0.45130.34791.32540.84660.9201
460.3498-0.10950.33090.07310.79140.8896
470.37080.18640.32120.20750.75250.8674
480.37590.23980.31610.35940.72790.8532
490.37620.37030.31930.89440.73770.8589
500.38740.42180.3251.14050.76010.8718
510.40810.77890.34893.71180.91540.9568
520.42370.66550.36472.68391.00381.0019
530.42661.03360.39666.71921.2761.1296
540.42851.28150.436810.61331.70041.304
550.44021.34160.476211.42692.12331.4572
560.4581.17990.50558.54262.39081.5462
570.46981.22220.53419.15072.66121.6313
580.47220.91140.54875.23832.76031.6614
590.47550.26360.53810.44572.67461.6354
600.48710.07510.52160.03562.58031.6063



Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 0 ; par8 = 0 ; par9 = 0 ;
Parameters (R input):
par1 = 24 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 0 ; 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
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')