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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_arimaforecasting.wasp
Title produced by softwareARIMA Forecasting
Date of computationSun, 19 Dec 2010 11:03:41 +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/19/t1292756763ls0rnbq4phssk9k.htm/, Retrieved Sat, 04 May 2024 21:06:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112284, Retrieved Sat, 04 May 2024 21:06:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact148

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Standard Deviation-Mean Plot] [Unemployment] [2010-11-29 10:34:47] [b98453cac15ba1066b407e146608df68]
- RMP     [ARIMA Forecasting] [Unemployment] [2010-11-29 20:46:45] [b98453cac15ba1066b407e146608df68]
- R PD      [ARIMA Forecasting] [ARIMA voorspelling] [2010-12-19 10:56:37] [46df8573ee32a55e1a6edcfb6691f406]
- R  D          [ARIMA Forecasting] [Voorspelling dali...] [2010-12-19 11:03:41] [109f5cd2d2b7c934778912c55604f6f1] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
126,64
126,81
125,84
126,77
124,34
124,4
120,48
118,54
117,66
116,97
120,11
119,16
116,9
116,11
114,98
113,65
115,82
117,59
118,57
118,07
114,98
114,04
115,02
114,28
115,04
116,7
119,21
118,39
116,5
115,46
117,59
117,33
116,2
116,83
118,99
118,62
121,09
122,4
123,76
125,33
123,23
122,52
123,64
124,67
124,71
122,53
124,4
125,45
125,35
124,3
127,03
128,51
128,1
128,94
129,67
129,87
131,12
132,68
132,24
133,63
129,91
127,93
131,17
130,86
133,48
134,08
136,02
132,8
132,37
133,05
132,57
130,7
130,5
129,67
127,8
126,82
126,85
128,28
128,3
126,82
125,08
128,53
130,34
131,52
132,59
131,17
132,72
133,36
132,82
132,9
130,9
129,41
128,67
129,28
130,91
131,06
130,84
131,41
133,22
132,06
132,48
134,38
135,22
134,89
136,09
136,33
136,32
137,48
136,53
136,8
138,03
137,39
137,55
136,08
134,78
133,28
133,57
134,84
133,02
133,49
133,77
134,34
134,5
134,03
135,51
136,53
135,95
134,32
132,44
133,61
131,02
130,05
128,21
129,03
130,34
131,57
132,63
132,06
134,44
134,1
132,49
134,23
134,92
135,61
134,53
133,86
133,89
135,33
135,86
136,22
137,38
137,31
136,89
138,01
136,72
135,77
137,52
135,61
132,94
134,12
132,55
134,11
134,19
135,57
135,05
134,32
133,61
134,75
133,1
133,26
131,63
132,47
132,45
133,33
133,57
134,13
133,92
132,62
132,3
133,26
132,6
134,38
134,17
135,46
135,09
134,96
133,85
132,59
131,15
130,91
131,07
130,78
129,95
131,41
131,21
130,68
130,46
131,12
132,99
133,02
133,39
134,07
135,6
135,66
135,53
135,82
136,9
137,97
138,09
136,91
134,76
135,13
134,66
132,95
132,25
134,3
134,3
134,76
134,81
134,51
135,11
134,32
133,51
134,02
132,76
133,39
132,05
131,87
133,03
132,57
132,1
130,7
129,2
129,77
131,02
131,55
133,17
133,08
133,24
130,74
129,91
130,03
131,13
129,55
130,22
130,61
129,27
129,68
130,1
130,83
130,95
131,73
131,86
132,44
132,35
133,16
133,62
132,54
132,69
133,5
133,36
134,23
132,41
133,02
132,88
130,76
130,33
129,79
128,65
129,14
127,35
127,74
126,31
125,95
126,36
126,15
125,6
126,2
126,73
125,68
122,49
122,07
123,4
123,01
123,03
122,33
122,42
122,68
124,69
123,3
124,17
124,38
123,19
122,16
120,66
120,92
120,67
120,68
121,1
120,86
121,48
123,48
121,72
123,16
123,84
124,57
124,3
124,22
124,43
123,33
122,86
121,25
122,16
122,62
123,44
124
124,75
124,8
125,93
126,28
126,04
125,04
123,76
125,34
126,99
126,34
127,42
126,18
125,3
123,5
125,32
124,65
124,03
125,11
125,46
124,7
124,48
124,76
125,81
124,95
123,66
122,66
119,34
117,84
120,97
117,38
118,06
116,99
115,55
114,17
115,32
112,49
111,93
112,08
111,63
109,53
111,35
110,79
113,06
112,62
110,65
112,36
113,74
111,73
109,86
109,32
109,99
109,84
111,13
112,43
111,77
112,15
112,89
112,12
113,1
111,09
110,76
109,59
109,99
110,25
108,31
108,79
108,14

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112284&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112284&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112284&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Univariate ARIMA Extrapolation Forecast
timeY[t]F[t]95% LB95% UBp-value(H0: Y[t] = F[t])P(F[t]>Y[t-1])P(F[t]>Y[t-s])P(F[t]>Y[338])
333124.03-------
334125.11-------
335125.46-------
336124.7-------
337124.48-------
338124.76-------
339125.81124.76122.3557127.16430.1960.50.38770.5
340124.95124.76121.3599128.16010.45640.27250.34330.5
341123.66124.76120.5957128.92430.30230.46440.51130.5
342122.66124.76119.9515129.56850.1960.67310.54540.5
343119.34124.76119.3839130.13610.02410.7780.50.5
344117.84124.76118.8708130.64920.01060.96440.36340.5
345120.97124.76118.3989131.12110.12140.98350.47670.5
346117.38124.76117.9597131.56030.01670.86270.62440.5
347118.06124.76117.5472131.97280.03430.97750.71590.5
348116.99124.76117.1571132.36290.02260.95790.91880.5
349115.55124.76116.786132.7340.01180.97190.95550.5
350114.17124.76116.4314133.08860.00630.98490.81380.5
351115.32124.76116.0913133.42870.01640.99170.95240.5
352112.49124.76115.7641133.75590.00380.98010.92780.5
353111.93124.76115.4483134.07170.00350.99510.9490.5
354112.08124.76115.143134.3770.00490.99550.96970.5
355111.63124.76114.847134.6730.00470.99390.98190.5
356109.53124.76114.5596134.96040.00170.99420.96520.5
357111.35124.76114.2801135.23990.00610.99780.98910.5
358110.79124.76114.0078135.51220.00540.99270.99030.5
359113.06124.76113.7423135.77770.01870.99350.9880.5
360112.62124.76113.483136.0370.01740.9790.98880.5
361110.65124.76113.2296136.29040.00820.98050.99520.5
362112.36124.76112.9816136.53840.01950.99060.98720.5
363113.74124.76112.7387136.78130.03620.97840.98860.5
364111.73124.76112.5006137.01940.01860.9610.96930.5
365109.86124.76112.2671137.25290.00970.97950.97160.5
366109.32124.76112.0378137.48220.00870.98910.98510.5
367109.99124.76111.8127137.70730.01270.99030.96980.5
368109.84124.76111.5913137.92870.01320.9860.94950.5
369111.13124.76111.3736138.14640.0230.98550.97180.5
370112.43124.76111.1594138.36060.03780.97520.98410.5
371111.77124.76110.9486138.57140.03260.95990.98580.5
372112.15124.76110.7409138.77910.0390.96530.98050.5
373112.89124.76110.5362138.98380.0510.95890.98010.5
374112.12124.76110.3344139.18560.0430.94660.9680.5
375113.1124.76110.1354139.38460.05910.95490.95080.5
376111.09124.76109.9391139.58090.03530.93850.95710.5
377110.76124.76109.7454139.77460.03380.96280.95010.5
378109.59124.76109.5541139.96590.02530.96440.9370.5
379109.99124.76109.3652140.15480.030.97330.94620.5
380110.25124.76109.1786140.34140.0340.96840.92880.5
381108.31124.76108.9942140.52580.02040.96440.95540.5
382108.79124.76108.8119140.70810.02480.97840.95730.5
383108.14124.76108.6317140.88830.02170.97390.96740.5

\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[338]) \tabularnewline
333 & 124.03 & - & - & - & - & - & - & - \tabularnewline
334 & 125.11 & - & - & - & - & - & - & - \tabularnewline
335 & 125.46 & - & - & - & - & - & - & - \tabularnewline
336 & 124.7 & - & - & - & - & - & - & - \tabularnewline
337 & 124.48 & - & - & - & - & - & - & - \tabularnewline
338 & 124.76 & - & - & - & - & - & - & - \tabularnewline
339 & 125.81 & 124.76 & 122.3557 & 127.1643 & 0.196 & 0.5 & 0.3877 & 0.5 \tabularnewline
340 & 124.95 & 124.76 & 121.3599 & 128.1601 & 0.4564 & 0.2725 & 0.3433 & 0.5 \tabularnewline
341 & 123.66 & 124.76 & 120.5957 & 128.9243 & 0.3023 & 0.4644 & 0.5113 & 0.5 \tabularnewline
342 & 122.66 & 124.76 & 119.9515 & 129.5685 & 0.196 & 0.6731 & 0.5454 & 0.5 \tabularnewline
343 & 119.34 & 124.76 & 119.3839 & 130.1361 & 0.0241 & 0.778 & 0.5 & 0.5 \tabularnewline
344 & 117.84 & 124.76 & 118.8708 & 130.6492 & 0.0106 & 0.9644 & 0.3634 & 0.5 \tabularnewline
345 & 120.97 & 124.76 & 118.3989 & 131.1211 & 0.1214 & 0.9835 & 0.4767 & 0.5 \tabularnewline
346 & 117.38 & 124.76 & 117.9597 & 131.5603 & 0.0167 & 0.8627 & 0.6244 & 0.5 \tabularnewline
347 & 118.06 & 124.76 & 117.5472 & 131.9728 & 0.0343 & 0.9775 & 0.7159 & 0.5 \tabularnewline
348 & 116.99 & 124.76 & 117.1571 & 132.3629 & 0.0226 & 0.9579 & 0.9188 & 0.5 \tabularnewline
349 & 115.55 & 124.76 & 116.786 & 132.734 & 0.0118 & 0.9719 & 0.9555 & 0.5 \tabularnewline
350 & 114.17 & 124.76 & 116.4314 & 133.0886 & 0.0063 & 0.9849 & 0.8138 & 0.5 \tabularnewline
351 & 115.32 & 124.76 & 116.0913 & 133.4287 & 0.0164 & 0.9917 & 0.9524 & 0.5 \tabularnewline
352 & 112.49 & 124.76 & 115.7641 & 133.7559 & 0.0038 & 0.9801 & 0.9278 & 0.5 \tabularnewline
353 & 111.93 & 124.76 & 115.4483 & 134.0717 & 0.0035 & 0.9951 & 0.949 & 0.5 \tabularnewline
354 & 112.08 & 124.76 & 115.143 & 134.377 & 0.0049 & 0.9955 & 0.9697 & 0.5 \tabularnewline
355 & 111.63 & 124.76 & 114.847 & 134.673 & 0.0047 & 0.9939 & 0.9819 & 0.5 \tabularnewline
356 & 109.53 & 124.76 & 114.5596 & 134.9604 & 0.0017 & 0.9942 & 0.9652 & 0.5 \tabularnewline
357 & 111.35 & 124.76 & 114.2801 & 135.2399 & 0.0061 & 0.9978 & 0.9891 & 0.5 \tabularnewline
358 & 110.79 & 124.76 & 114.0078 & 135.5122 & 0.0054 & 0.9927 & 0.9903 & 0.5 \tabularnewline
359 & 113.06 & 124.76 & 113.7423 & 135.7777 & 0.0187 & 0.9935 & 0.988 & 0.5 \tabularnewline
360 & 112.62 & 124.76 & 113.483 & 136.037 & 0.0174 & 0.979 & 0.9888 & 0.5 \tabularnewline
361 & 110.65 & 124.76 & 113.2296 & 136.2904 & 0.0082 & 0.9805 & 0.9952 & 0.5 \tabularnewline
362 & 112.36 & 124.76 & 112.9816 & 136.5384 & 0.0195 & 0.9906 & 0.9872 & 0.5 \tabularnewline
363 & 113.74 & 124.76 & 112.7387 & 136.7813 & 0.0362 & 0.9784 & 0.9886 & 0.5 \tabularnewline
364 & 111.73 & 124.76 & 112.5006 & 137.0194 & 0.0186 & 0.961 & 0.9693 & 0.5 \tabularnewline
365 & 109.86 & 124.76 & 112.2671 & 137.2529 & 0.0097 & 0.9795 & 0.9716 & 0.5 \tabularnewline
366 & 109.32 & 124.76 & 112.0378 & 137.4822 & 0.0087 & 0.9891 & 0.9851 & 0.5 \tabularnewline
367 & 109.99 & 124.76 & 111.8127 & 137.7073 & 0.0127 & 0.9903 & 0.9698 & 0.5 \tabularnewline
368 & 109.84 & 124.76 & 111.5913 & 137.9287 & 0.0132 & 0.986 & 0.9495 & 0.5 \tabularnewline
369 & 111.13 & 124.76 & 111.3736 & 138.1464 & 0.023 & 0.9855 & 0.9718 & 0.5 \tabularnewline
370 & 112.43 & 124.76 & 111.1594 & 138.3606 & 0.0378 & 0.9752 & 0.9841 & 0.5 \tabularnewline
371 & 111.77 & 124.76 & 110.9486 & 138.5714 & 0.0326 & 0.9599 & 0.9858 & 0.5 \tabularnewline
372 & 112.15 & 124.76 & 110.7409 & 138.7791 & 0.039 & 0.9653 & 0.9805 & 0.5 \tabularnewline
373 & 112.89 & 124.76 & 110.5362 & 138.9838 & 0.051 & 0.9589 & 0.9801 & 0.5 \tabularnewline
374 & 112.12 & 124.76 & 110.3344 & 139.1856 & 0.043 & 0.9466 & 0.968 & 0.5 \tabularnewline
375 & 113.1 & 124.76 & 110.1354 & 139.3846 & 0.0591 & 0.9549 & 0.9508 & 0.5 \tabularnewline
376 & 111.09 & 124.76 & 109.9391 & 139.5809 & 0.0353 & 0.9385 & 0.9571 & 0.5 \tabularnewline
377 & 110.76 & 124.76 & 109.7454 & 139.7746 & 0.0338 & 0.9628 & 0.9501 & 0.5 \tabularnewline
378 & 109.59 & 124.76 & 109.5541 & 139.9659 & 0.0253 & 0.9644 & 0.937 & 0.5 \tabularnewline
379 & 109.99 & 124.76 & 109.3652 & 140.1548 & 0.03 & 0.9733 & 0.9462 & 0.5 \tabularnewline
380 & 110.25 & 124.76 & 109.1786 & 140.3414 & 0.034 & 0.9684 & 0.9288 & 0.5 \tabularnewline
381 & 108.31 & 124.76 & 108.9942 & 140.5258 & 0.0204 & 0.9644 & 0.9554 & 0.5 \tabularnewline
382 & 108.79 & 124.76 & 108.8119 & 140.7081 & 0.0248 & 0.9784 & 0.9573 & 0.5 \tabularnewline
383 & 108.14 & 124.76 & 108.6317 & 140.8883 & 0.0217 & 0.9739 & 0.9674 & 0.5 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112284&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[338])[/C][/ROW]
[ROW][C]333[/C][C]124.03[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]334[/C][C]125.11[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]335[/C][C]125.46[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]336[/C][C]124.7[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]337[/C][C]124.48[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]338[/C][C]124.76[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]339[/C][C]125.81[/C][C]124.76[/C][C]122.3557[/C][C]127.1643[/C][C]0.196[/C][C]0.5[/C][C]0.3877[/C][C]0.5[/C][/ROW]
[ROW][C]340[/C][C]124.95[/C][C]124.76[/C][C]121.3599[/C][C]128.1601[/C][C]0.4564[/C][C]0.2725[/C][C]0.3433[/C][C]0.5[/C][/ROW]
[ROW][C]341[/C][C]123.66[/C][C]124.76[/C][C]120.5957[/C][C]128.9243[/C][C]0.3023[/C][C]0.4644[/C][C]0.5113[/C][C]0.5[/C][/ROW]
[ROW][C]342[/C][C]122.66[/C][C]124.76[/C][C]119.9515[/C][C]129.5685[/C][C]0.196[/C][C]0.6731[/C][C]0.5454[/C][C]0.5[/C][/ROW]
[ROW][C]343[/C][C]119.34[/C][C]124.76[/C][C]119.3839[/C][C]130.1361[/C][C]0.0241[/C][C]0.778[/C][C]0.5[/C][C]0.5[/C][/ROW]
[ROW][C]344[/C][C]117.84[/C][C]124.76[/C][C]118.8708[/C][C]130.6492[/C][C]0.0106[/C][C]0.9644[/C][C]0.3634[/C][C]0.5[/C][/ROW]
[ROW][C]345[/C][C]120.97[/C][C]124.76[/C][C]118.3989[/C][C]131.1211[/C][C]0.1214[/C][C]0.9835[/C][C]0.4767[/C][C]0.5[/C][/ROW]
[ROW][C]346[/C][C]117.38[/C][C]124.76[/C][C]117.9597[/C][C]131.5603[/C][C]0.0167[/C][C]0.8627[/C][C]0.6244[/C][C]0.5[/C][/ROW]
[ROW][C]347[/C][C]118.06[/C][C]124.76[/C][C]117.5472[/C][C]131.9728[/C][C]0.0343[/C][C]0.9775[/C][C]0.7159[/C][C]0.5[/C][/ROW]
[ROW][C]348[/C][C]116.99[/C][C]124.76[/C][C]117.1571[/C][C]132.3629[/C][C]0.0226[/C][C]0.9579[/C][C]0.9188[/C][C]0.5[/C][/ROW]
[ROW][C]349[/C][C]115.55[/C][C]124.76[/C][C]116.786[/C][C]132.734[/C][C]0.0118[/C][C]0.9719[/C][C]0.9555[/C][C]0.5[/C][/ROW]
[ROW][C]350[/C][C]114.17[/C][C]124.76[/C][C]116.4314[/C][C]133.0886[/C][C]0.0063[/C][C]0.9849[/C][C]0.8138[/C][C]0.5[/C][/ROW]
[ROW][C]351[/C][C]115.32[/C][C]124.76[/C][C]116.0913[/C][C]133.4287[/C][C]0.0164[/C][C]0.9917[/C][C]0.9524[/C][C]0.5[/C][/ROW]
[ROW][C]352[/C][C]112.49[/C][C]124.76[/C][C]115.7641[/C][C]133.7559[/C][C]0.0038[/C][C]0.9801[/C][C]0.9278[/C][C]0.5[/C][/ROW]
[ROW][C]353[/C][C]111.93[/C][C]124.76[/C][C]115.4483[/C][C]134.0717[/C][C]0.0035[/C][C]0.9951[/C][C]0.949[/C][C]0.5[/C][/ROW]
[ROW][C]354[/C][C]112.08[/C][C]124.76[/C][C]115.143[/C][C]134.377[/C][C]0.0049[/C][C]0.9955[/C][C]0.9697[/C][C]0.5[/C][/ROW]
[ROW][C]355[/C][C]111.63[/C][C]124.76[/C][C]114.847[/C][C]134.673[/C][C]0.0047[/C][C]0.9939[/C][C]0.9819[/C][C]0.5[/C][/ROW]
[ROW][C]356[/C][C]109.53[/C][C]124.76[/C][C]114.5596[/C][C]134.9604[/C][C]0.0017[/C][C]0.9942[/C][C]0.9652[/C][C]0.5[/C][/ROW]
[ROW][C]357[/C][C]111.35[/C][C]124.76[/C][C]114.2801[/C][C]135.2399[/C][C]0.0061[/C][C]0.9978[/C][C]0.9891[/C][C]0.5[/C][/ROW]
[ROW][C]358[/C][C]110.79[/C][C]124.76[/C][C]114.0078[/C][C]135.5122[/C][C]0.0054[/C][C]0.9927[/C][C]0.9903[/C][C]0.5[/C][/ROW]
[ROW][C]359[/C][C]113.06[/C][C]124.76[/C][C]113.7423[/C][C]135.7777[/C][C]0.0187[/C][C]0.9935[/C][C]0.988[/C][C]0.5[/C][/ROW]
[ROW][C]360[/C][C]112.62[/C][C]124.76[/C][C]113.483[/C][C]136.037[/C][C]0.0174[/C][C]0.979[/C][C]0.9888[/C][C]0.5[/C][/ROW]
[ROW][C]361[/C][C]110.65[/C][C]124.76[/C][C]113.2296[/C][C]136.2904[/C][C]0.0082[/C][C]0.9805[/C][C]0.9952[/C][C]0.5[/C][/ROW]
[ROW][C]362[/C][C]112.36[/C][C]124.76[/C][C]112.9816[/C][C]136.5384[/C][C]0.0195[/C][C]0.9906[/C][C]0.9872[/C][C]0.5[/C][/ROW]
[ROW][C]363[/C][C]113.74[/C][C]124.76[/C][C]112.7387[/C][C]136.7813[/C][C]0.0362[/C][C]0.9784[/C][C]0.9886[/C][C]0.5[/C][/ROW]
[ROW][C]364[/C][C]111.73[/C][C]124.76[/C][C]112.5006[/C][C]137.0194[/C][C]0.0186[/C][C]0.961[/C][C]0.9693[/C][C]0.5[/C][/ROW]
[ROW][C]365[/C][C]109.86[/C][C]124.76[/C][C]112.2671[/C][C]137.2529[/C][C]0.0097[/C][C]0.9795[/C][C]0.9716[/C][C]0.5[/C][/ROW]
[ROW][C]366[/C][C]109.32[/C][C]124.76[/C][C]112.0378[/C][C]137.4822[/C][C]0.0087[/C][C]0.9891[/C][C]0.9851[/C][C]0.5[/C][/ROW]
[ROW][C]367[/C][C]109.99[/C][C]124.76[/C][C]111.8127[/C][C]137.7073[/C][C]0.0127[/C][C]0.9903[/C][C]0.9698[/C][C]0.5[/C][/ROW]
[ROW][C]368[/C][C]109.84[/C][C]124.76[/C][C]111.5913[/C][C]137.9287[/C][C]0.0132[/C][C]0.986[/C][C]0.9495[/C][C]0.5[/C][/ROW]
[ROW][C]369[/C][C]111.13[/C][C]124.76[/C][C]111.3736[/C][C]138.1464[/C][C]0.023[/C][C]0.9855[/C][C]0.9718[/C][C]0.5[/C][/ROW]
[ROW][C]370[/C][C]112.43[/C][C]124.76[/C][C]111.1594[/C][C]138.3606[/C][C]0.0378[/C][C]0.9752[/C][C]0.9841[/C][C]0.5[/C][/ROW]
[ROW][C]371[/C][C]111.77[/C][C]124.76[/C][C]110.9486[/C][C]138.5714[/C][C]0.0326[/C][C]0.9599[/C][C]0.9858[/C][C]0.5[/C][/ROW]
[ROW][C]372[/C][C]112.15[/C][C]124.76[/C][C]110.7409[/C][C]138.7791[/C][C]0.039[/C][C]0.9653[/C][C]0.9805[/C][C]0.5[/C][/ROW]
[ROW][C]373[/C][C]112.89[/C][C]124.76[/C][C]110.5362[/C][C]138.9838[/C][C]0.051[/C][C]0.9589[/C][C]0.9801[/C][C]0.5[/C][/ROW]
[ROW][C]374[/C][C]112.12[/C][C]124.76[/C][C]110.3344[/C][C]139.1856[/C][C]0.043[/C][C]0.9466[/C][C]0.968[/C][C]0.5[/C][/ROW]
[ROW][C]375[/C][C]113.1[/C][C]124.76[/C][C]110.1354[/C][C]139.3846[/C][C]0.0591[/C][C]0.9549[/C][C]0.9508[/C][C]0.5[/C][/ROW]
[ROW][C]376[/C][C]111.09[/C][C]124.76[/C][C]109.9391[/C][C]139.5809[/C][C]0.0353[/C][C]0.9385[/C][C]0.9571[/C][C]0.5[/C][/ROW]
[ROW][C]377[/C][C]110.76[/C][C]124.76[/C][C]109.7454[/C][C]139.7746[/C][C]0.0338[/C][C]0.9628[/C][C]0.9501[/C][C]0.5[/C][/ROW]
[ROW][C]378[/C][C]109.59[/C][C]124.76[/C][C]109.5541[/C][C]139.9659[/C][C]0.0253[/C][C]0.9644[/C][C]0.937[/C][C]0.5[/C][/ROW]
[ROW][C]379[/C][C]109.99[/C][C]124.76[/C][C]109.3652[/C][C]140.1548[/C][C]0.03[/C][C]0.9733[/C][C]0.9462[/C][C]0.5[/C][/ROW]
[ROW][C]380[/C][C]110.25[/C][C]124.76[/C][C]109.1786[/C][C]140.3414[/C][C]0.034[/C][C]0.9684[/C][C]0.9288[/C][C]0.5[/C][/ROW]
[ROW][C]381[/C][C]108.31[/C][C]124.76[/C][C]108.9942[/C][C]140.5258[/C][C]0.0204[/C][C]0.9644[/C][C]0.9554[/C][C]0.5[/C][/ROW]
[ROW][C]382[/C][C]108.79[/C][C]124.76[/C][C]108.8119[/C][C]140.7081[/C][C]0.0248[/C][C]0.9784[/C][C]0.9573[/C][C]0.5[/C][/ROW]
[ROW][C]383[/C][C]108.14[/C][C]124.76[/C][C]108.6317[/C][C]140.8883[/C][C]0.0217[/C][C]0.9739[/C][C]0.9674[/C][C]0.5[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112284&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112284&T=1

As an alternative you can also use a QR Code:  
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[338])
333124.03-------
334125.11-------
335125.46-------
336124.7-------
337124.48-------
338124.76-------
339125.81124.76122.3557127.16430.1960.50.38770.5
340124.95124.76121.3599128.16010.45640.27250.34330.5
341123.66124.76120.5957128.92430.30230.46440.51130.5
342122.66124.76119.9515129.56850.1960.67310.54540.5
343119.34124.76119.3839130.13610.02410.7780.50.5
344117.84124.76118.8708130.64920.01060.96440.36340.5
345120.97124.76118.3989131.12110.12140.98350.47670.5
346117.38124.76117.9597131.56030.01670.86270.62440.5
347118.06124.76117.5472131.97280.03430.97750.71590.5
348116.99124.76117.1571132.36290.02260.95790.91880.5
349115.55124.76116.786132.7340.01180.97190.95550.5
350114.17124.76116.4314133.08860.00630.98490.81380.5
351115.32124.76116.0913133.42870.01640.99170.95240.5
352112.49124.76115.7641133.75590.00380.98010.92780.5
353111.93124.76115.4483134.07170.00350.99510.9490.5
354112.08124.76115.143134.3770.00490.99550.96970.5
355111.63124.76114.847134.6730.00470.99390.98190.5
356109.53124.76114.5596134.96040.00170.99420.96520.5
357111.35124.76114.2801135.23990.00610.99780.98910.5
358110.79124.76114.0078135.51220.00540.99270.99030.5
359113.06124.76113.7423135.77770.01870.99350.9880.5
360112.62124.76113.483136.0370.01740.9790.98880.5
361110.65124.76113.2296136.29040.00820.98050.99520.5
362112.36124.76112.9816136.53840.01950.99060.98720.5
363113.74124.76112.7387136.78130.03620.97840.98860.5
364111.73124.76112.5006137.01940.01860.9610.96930.5
365109.86124.76112.2671137.25290.00970.97950.97160.5
366109.32124.76112.0378137.48220.00870.98910.98510.5
367109.99124.76111.8127137.70730.01270.99030.96980.5
368109.84124.76111.5913137.92870.01320.9860.94950.5
369111.13124.76111.3736138.14640.0230.98550.97180.5
370112.43124.76111.1594138.36060.03780.97520.98410.5
371111.77124.76110.9486138.57140.03260.95990.98580.5
372112.15124.76110.7409138.77910.0390.96530.98050.5
373112.89124.76110.5362138.98380.0510.95890.98010.5
374112.12124.76110.3344139.18560.0430.94660.9680.5
375113.1124.76110.1354139.38460.05910.95490.95080.5
376111.09124.76109.9391139.58090.03530.93850.95710.5
377110.76124.76109.7454139.77460.03380.96280.95010.5
378109.59124.76109.5541139.96590.02530.96440.9370.5
379109.99124.76109.3652140.15480.030.97330.94620.5
380110.25124.76109.1786140.34140.0340.96840.92880.5
381108.31124.76108.9942140.52580.02040.96440.95540.5
382108.79124.76108.8119140.70810.02480.97840.95730.5
383108.14124.76108.6317140.88830.02170.97390.96740.5






Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
3390.00980.008401.102500
3400.01390.00150.0050.03610.56930.7545
3410.017-0.00880.00631.210.78290.8848
3420.0197-0.01680.00894.411.68971.2999
3430.022-0.04340.015829.37647.2272.6883
3440.0241-0.05550.022447.886414.00363.7421
3450.026-0.03040.023614.364114.05513.749
3460.0278-0.05920.02854.464419.10624.3711
3470.0295-0.05370.030944.8921.97114.6873
3480.0311-0.06230.03460.372925.81135.0805
3490.0326-0.07380.037684.824131.17615.5836
3500.0341-0.08490.0416112.148137.92386.1582
3510.0355-0.07570.044289.113641.86146.47
3520.0368-0.09830.0481150.552949.62517.0445
3530.0381-0.10280.0517164.608957.29077.5691
3540.0393-0.10160.0548160.782463.75897.9849
3550.0405-0.10520.0578172.396970.14948.3755
3560.0417-0.12210.0614231.952979.13858.896
3570.0429-0.10750.0638179.828184.43799.189
3580.044-0.1120.0662195.160989.97419.4855
3590.0451-0.09380.0675136.8992.20829.6025
3600.0461-0.09730.0689147.379694.7169.7322
3610.0472-0.11310.0708199.092199.25419.9626
3620.0482-0.09940.072153.76101.525110.076
3630.0492-0.08830.0726121.4404102.321710.1154
3640.0501-0.10440.0739169.7809104.916310.2429
3650.0511-0.11940.0755222.01109.253110.4524
3660.052-0.12380.0773238.3936113.865310.6708
3670.0529-0.11840.0787218.1529117.461410.838
3680.0539-0.11960.08222.6064120.966310.9985
3690.0547-0.10920.081185.7769123.056911.0931
3700.0556-0.09880.0815152.0289123.962311.1338
3710.0565-0.10410.0822168.7401125.319211.1946
3720.0573-0.10110.0828159.0121126.310211.2388
3730.0582-0.09510.0831140.8969126.726911.2573
3740.059-0.10130.0836159.7696127.644811.298
3750.0598-0.09350.0839135.9556127.869411.3079
3760.0606-0.10960.0846186.8689129.42211.3764
3770.0614-0.11220.0853196131.129111.4512
3780.0622-0.12160.0862230.1289133.604111.5587
3790.063-0.11840.087218.1529135.666311.6476
3800.0637-0.11630.0877210.5401137.44911.7239
3810.0645-0.13190.0887270.6025140.545611.8552
3820.0652-0.1280.0896255.0409143.147811.9644
3830.066-0.13320.0906276.2244146.10512.0874

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
339 & 0.0098 & 0.0084 & 0 & 1.1025 & 0 & 0 \tabularnewline
340 & 0.0139 & 0.0015 & 0.005 & 0.0361 & 0.5693 & 0.7545 \tabularnewline
341 & 0.017 & -0.0088 & 0.0063 & 1.21 & 0.7829 & 0.8848 \tabularnewline
342 & 0.0197 & -0.0168 & 0.0089 & 4.41 & 1.6897 & 1.2999 \tabularnewline
343 & 0.022 & -0.0434 & 0.0158 & 29.3764 & 7.227 & 2.6883 \tabularnewline
344 & 0.0241 & -0.0555 & 0.0224 & 47.8864 & 14.0036 & 3.7421 \tabularnewline
345 & 0.026 & -0.0304 & 0.0236 & 14.3641 & 14.0551 & 3.749 \tabularnewline
346 & 0.0278 & -0.0592 & 0.028 & 54.4644 & 19.1062 & 4.3711 \tabularnewline
347 & 0.0295 & -0.0537 & 0.0309 & 44.89 & 21.9711 & 4.6873 \tabularnewline
348 & 0.0311 & -0.0623 & 0.034 & 60.3729 & 25.8113 & 5.0805 \tabularnewline
349 & 0.0326 & -0.0738 & 0.0376 & 84.8241 & 31.1761 & 5.5836 \tabularnewline
350 & 0.0341 & -0.0849 & 0.0416 & 112.1481 & 37.9238 & 6.1582 \tabularnewline
351 & 0.0355 & -0.0757 & 0.0442 & 89.1136 & 41.8614 & 6.47 \tabularnewline
352 & 0.0368 & -0.0983 & 0.0481 & 150.5529 & 49.6251 & 7.0445 \tabularnewline
353 & 0.0381 & -0.1028 & 0.0517 & 164.6089 & 57.2907 & 7.5691 \tabularnewline
354 & 0.0393 & -0.1016 & 0.0548 & 160.7824 & 63.7589 & 7.9849 \tabularnewline
355 & 0.0405 & -0.1052 & 0.0578 & 172.3969 & 70.1494 & 8.3755 \tabularnewline
356 & 0.0417 & -0.1221 & 0.0614 & 231.9529 & 79.1385 & 8.896 \tabularnewline
357 & 0.0429 & -0.1075 & 0.0638 & 179.8281 & 84.4379 & 9.189 \tabularnewline
358 & 0.044 & -0.112 & 0.0662 & 195.1609 & 89.9741 & 9.4855 \tabularnewline
359 & 0.0451 & -0.0938 & 0.0675 & 136.89 & 92.2082 & 9.6025 \tabularnewline
360 & 0.0461 & -0.0973 & 0.0689 & 147.3796 & 94.716 & 9.7322 \tabularnewline
361 & 0.0472 & -0.1131 & 0.0708 & 199.0921 & 99.2541 & 9.9626 \tabularnewline
362 & 0.0482 & -0.0994 & 0.072 & 153.76 & 101.5251 & 10.076 \tabularnewline
363 & 0.0492 & -0.0883 & 0.0726 & 121.4404 & 102.3217 & 10.1154 \tabularnewline
364 & 0.0501 & -0.1044 & 0.0739 & 169.7809 & 104.9163 & 10.2429 \tabularnewline
365 & 0.0511 & -0.1194 & 0.0755 & 222.01 & 109.2531 & 10.4524 \tabularnewline
366 & 0.052 & -0.1238 & 0.0773 & 238.3936 & 113.8653 & 10.6708 \tabularnewline
367 & 0.0529 & -0.1184 & 0.0787 & 218.1529 & 117.4614 & 10.838 \tabularnewline
368 & 0.0539 & -0.1196 & 0.08 & 222.6064 & 120.9663 & 10.9985 \tabularnewline
369 & 0.0547 & -0.1092 & 0.081 & 185.7769 & 123.0569 & 11.0931 \tabularnewline
370 & 0.0556 & -0.0988 & 0.0815 & 152.0289 & 123.9623 & 11.1338 \tabularnewline
371 & 0.0565 & -0.1041 & 0.0822 & 168.7401 & 125.3192 & 11.1946 \tabularnewline
372 & 0.0573 & -0.1011 & 0.0828 & 159.0121 & 126.3102 & 11.2388 \tabularnewline
373 & 0.0582 & -0.0951 & 0.0831 & 140.8969 & 126.7269 & 11.2573 \tabularnewline
374 & 0.059 & -0.1013 & 0.0836 & 159.7696 & 127.6448 & 11.298 \tabularnewline
375 & 0.0598 & -0.0935 & 0.0839 & 135.9556 & 127.8694 & 11.3079 \tabularnewline
376 & 0.0606 & -0.1096 & 0.0846 & 186.8689 & 129.422 & 11.3764 \tabularnewline
377 & 0.0614 & -0.1122 & 0.0853 & 196 & 131.1291 & 11.4512 \tabularnewline
378 & 0.0622 & -0.1216 & 0.0862 & 230.1289 & 133.6041 & 11.5587 \tabularnewline
379 & 0.063 & -0.1184 & 0.087 & 218.1529 & 135.6663 & 11.6476 \tabularnewline
380 & 0.0637 & -0.1163 & 0.0877 & 210.5401 & 137.449 & 11.7239 \tabularnewline
381 & 0.0645 & -0.1319 & 0.0887 & 270.6025 & 140.5456 & 11.8552 \tabularnewline
382 & 0.0652 & -0.128 & 0.0896 & 255.0409 & 143.1478 & 11.9644 \tabularnewline
383 & 0.066 & -0.1332 & 0.0906 & 276.2244 & 146.105 & 12.0874 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112284&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]339[/C][C]0.0098[/C][C]0.0084[/C][C]0[/C][C]1.1025[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]340[/C][C]0.0139[/C][C]0.0015[/C][C]0.005[/C][C]0.0361[/C][C]0.5693[/C][C]0.7545[/C][/ROW]
[ROW][C]341[/C][C]0.017[/C][C]-0.0088[/C][C]0.0063[/C][C]1.21[/C][C]0.7829[/C][C]0.8848[/C][/ROW]
[ROW][C]342[/C][C]0.0197[/C][C]-0.0168[/C][C]0.0089[/C][C]4.41[/C][C]1.6897[/C][C]1.2999[/C][/ROW]
[ROW][C]343[/C][C]0.022[/C][C]-0.0434[/C][C]0.0158[/C][C]29.3764[/C][C]7.227[/C][C]2.6883[/C][/ROW]
[ROW][C]344[/C][C]0.0241[/C][C]-0.0555[/C][C]0.0224[/C][C]47.8864[/C][C]14.0036[/C][C]3.7421[/C][/ROW]
[ROW][C]345[/C][C]0.026[/C][C]-0.0304[/C][C]0.0236[/C][C]14.3641[/C][C]14.0551[/C][C]3.749[/C][/ROW]
[ROW][C]346[/C][C]0.0278[/C][C]-0.0592[/C][C]0.028[/C][C]54.4644[/C][C]19.1062[/C][C]4.3711[/C][/ROW]
[ROW][C]347[/C][C]0.0295[/C][C]-0.0537[/C][C]0.0309[/C][C]44.89[/C][C]21.9711[/C][C]4.6873[/C][/ROW]
[ROW][C]348[/C][C]0.0311[/C][C]-0.0623[/C][C]0.034[/C][C]60.3729[/C][C]25.8113[/C][C]5.0805[/C][/ROW]
[ROW][C]349[/C][C]0.0326[/C][C]-0.0738[/C][C]0.0376[/C][C]84.8241[/C][C]31.1761[/C][C]5.5836[/C][/ROW]
[ROW][C]350[/C][C]0.0341[/C][C]-0.0849[/C][C]0.0416[/C][C]112.1481[/C][C]37.9238[/C][C]6.1582[/C][/ROW]
[ROW][C]351[/C][C]0.0355[/C][C]-0.0757[/C][C]0.0442[/C][C]89.1136[/C][C]41.8614[/C][C]6.47[/C][/ROW]
[ROW][C]352[/C][C]0.0368[/C][C]-0.0983[/C][C]0.0481[/C][C]150.5529[/C][C]49.6251[/C][C]7.0445[/C][/ROW]
[ROW][C]353[/C][C]0.0381[/C][C]-0.1028[/C][C]0.0517[/C][C]164.6089[/C][C]57.2907[/C][C]7.5691[/C][/ROW]
[ROW][C]354[/C][C]0.0393[/C][C]-0.1016[/C][C]0.0548[/C][C]160.7824[/C][C]63.7589[/C][C]7.9849[/C][/ROW]
[ROW][C]355[/C][C]0.0405[/C][C]-0.1052[/C][C]0.0578[/C][C]172.3969[/C][C]70.1494[/C][C]8.3755[/C][/ROW]
[ROW][C]356[/C][C]0.0417[/C][C]-0.1221[/C][C]0.0614[/C][C]231.9529[/C][C]79.1385[/C][C]8.896[/C][/ROW]
[ROW][C]357[/C][C]0.0429[/C][C]-0.1075[/C][C]0.0638[/C][C]179.8281[/C][C]84.4379[/C][C]9.189[/C][/ROW]
[ROW][C]358[/C][C]0.044[/C][C]-0.112[/C][C]0.0662[/C][C]195.1609[/C][C]89.9741[/C][C]9.4855[/C][/ROW]
[ROW][C]359[/C][C]0.0451[/C][C]-0.0938[/C][C]0.0675[/C][C]136.89[/C][C]92.2082[/C][C]9.6025[/C][/ROW]
[ROW][C]360[/C][C]0.0461[/C][C]-0.0973[/C][C]0.0689[/C][C]147.3796[/C][C]94.716[/C][C]9.7322[/C][/ROW]
[ROW][C]361[/C][C]0.0472[/C][C]-0.1131[/C][C]0.0708[/C][C]199.0921[/C][C]99.2541[/C][C]9.9626[/C][/ROW]
[ROW][C]362[/C][C]0.0482[/C][C]-0.0994[/C][C]0.072[/C][C]153.76[/C][C]101.5251[/C][C]10.076[/C][/ROW]
[ROW][C]363[/C][C]0.0492[/C][C]-0.0883[/C][C]0.0726[/C][C]121.4404[/C][C]102.3217[/C][C]10.1154[/C][/ROW]
[ROW][C]364[/C][C]0.0501[/C][C]-0.1044[/C][C]0.0739[/C][C]169.7809[/C][C]104.9163[/C][C]10.2429[/C][/ROW]
[ROW][C]365[/C][C]0.0511[/C][C]-0.1194[/C][C]0.0755[/C][C]222.01[/C][C]109.2531[/C][C]10.4524[/C][/ROW]
[ROW][C]366[/C][C]0.052[/C][C]-0.1238[/C][C]0.0773[/C][C]238.3936[/C][C]113.8653[/C][C]10.6708[/C][/ROW]
[ROW][C]367[/C][C]0.0529[/C][C]-0.1184[/C][C]0.0787[/C][C]218.1529[/C][C]117.4614[/C][C]10.838[/C][/ROW]
[ROW][C]368[/C][C]0.0539[/C][C]-0.1196[/C][C]0.08[/C][C]222.6064[/C][C]120.9663[/C][C]10.9985[/C][/ROW]
[ROW][C]369[/C][C]0.0547[/C][C]-0.1092[/C][C]0.081[/C][C]185.7769[/C][C]123.0569[/C][C]11.0931[/C][/ROW]
[ROW][C]370[/C][C]0.0556[/C][C]-0.0988[/C][C]0.0815[/C][C]152.0289[/C][C]123.9623[/C][C]11.1338[/C][/ROW]
[ROW][C]371[/C][C]0.0565[/C][C]-0.1041[/C][C]0.0822[/C][C]168.7401[/C][C]125.3192[/C][C]11.1946[/C][/ROW]
[ROW][C]372[/C][C]0.0573[/C][C]-0.1011[/C][C]0.0828[/C][C]159.0121[/C][C]126.3102[/C][C]11.2388[/C][/ROW]
[ROW][C]373[/C][C]0.0582[/C][C]-0.0951[/C][C]0.0831[/C][C]140.8969[/C][C]126.7269[/C][C]11.2573[/C][/ROW]
[ROW][C]374[/C][C]0.059[/C][C]-0.1013[/C][C]0.0836[/C][C]159.7696[/C][C]127.6448[/C][C]11.298[/C][/ROW]
[ROW][C]375[/C][C]0.0598[/C][C]-0.0935[/C][C]0.0839[/C][C]135.9556[/C][C]127.8694[/C][C]11.3079[/C][/ROW]
[ROW][C]376[/C][C]0.0606[/C][C]-0.1096[/C][C]0.0846[/C][C]186.8689[/C][C]129.422[/C][C]11.3764[/C][/ROW]
[ROW][C]377[/C][C]0.0614[/C][C]-0.1122[/C][C]0.0853[/C][C]196[/C][C]131.1291[/C][C]11.4512[/C][/ROW]
[ROW][C]378[/C][C]0.0622[/C][C]-0.1216[/C][C]0.0862[/C][C]230.1289[/C][C]133.6041[/C][C]11.5587[/C][/ROW]
[ROW][C]379[/C][C]0.063[/C][C]-0.1184[/C][C]0.087[/C][C]218.1529[/C][C]135.6663[/C][C]11.6476[/C][/ROW]
[ROW][C]380[/C][C]0.0637[/C][C]-0.1163[/C][C]0.0877[/C][C]210.5401[/C][C]137.449[/C][C]11.7239[/C][/ROW]
[ROW][C]381[/C][C]0.0645[/C][C]-0.1319[/C][C]0.0887[/C][C]270.6025[/C][C]140.5456[/C][C]11.8552[/C][/ROW]
[ROW][C]382[/C][C]0.0652[/C][C]-0.128[/C][C]0.0896[/C][C]255.0409[/C][C]143.1478[/C][C]11.9644[/C][/ROW]
[ROW][C]383[/C][C]0.066[/C][C]-0.1332[/C][C]0.0906[/C][C]276.2244[/C][C]146.105[/C][C]12.0874[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112284&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112284&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
3390.00980.008401.102500
3400.01390.00150.0050.03610.56930.7545
3410.017-0.00880.00631.210.78290.8848
3420.0197-0.01680.00894.411.68971.2999
3430.022-0.04340.015829.37647.2272.6883
3440.0241-0.05550.022447.886414.00363.7421
3450.026-0.03040.023614.364114.05513.749
3460.0278-0.05920.02854.464419.10624.3711
3470.0295-0.05370.030944.8921.97114.6873
3480.0311-0.06230.03460.372925.81135.0805
3490.0326-0.07380.037684.824131.17615.5836
3500.0341-0.08490.0416112.148137.92386.1582
3510.0355-0.07570.044289.113641.86146.47
3520.0368-0.09830.0481150.552949.62517.0445
3530.0381-0.10280.0517164.608957.29077.5691
3540.0393-0.10160.0548160.782463.75897.9849
3550.0405-0.10520.0578172.396970.14948.3755
3560.0417-0.12210.0614231.952979.13858.896
3570.0429-0.10750.0638179.828184.43799.189
3580.044-0.1120.0662195.160989.97419.4855
3590.0451-0.09380.0675136.8992.20829.6025
3600.0461-0.09730.0689147.379694.7169.7322
3610.0472-0.11310.0708199.092199.25419.9626
3620.0482-0.09940.072153.76101.525110.076
3630.0492-0.08830.0726121.4404102.321710.1154
3640.0501-0.10440.0739169.7809104.916310.2429
3650.0511-0.11940.0755222.01109.253110.4524
3660.052-0.12380.0773238.3936113.865310.6708
3670.0529-0.11840.0787218.1529117.461410.838
3680.0539-0.11960.08222.6064120.966310.9985
3690.0547-0.10920.081185.7769123.056911.0931
3700.0556-0.09880.0815152.0289123.962311.1338
3710.0565-0.10410.0822168.7401125.319211.1946
3720.0573-0.10110.0828159.0121126.310211.2388
3730.0582-0.09510.0831140.8969126.726911.2573
3740.059-0.10130.0836159.7696127.644811.298
3750.0598-0.09350.0839135.9556127.869411.3079
3760.0606-0.10960.0846186.8689129.42211.3764
3770.0614-0.11220.0853196131.129111.4512
3780.0622-0.12160.0862230.1289133.604111.5587
3790.063-0.11840.087218.1529135.666311.6476
3800.0637-0.11630.0877210.5401137.44911.7239
3810.0645-0.13190.0887270.6025140.545611.8552
3820.0652-0.1280.0896255.0409143.147811.9644
3830.066-0.13320.0906276.2244146.10512.0874


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 15 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 1 ; par6 = 0 ; par7 = 0 ; par8 = 0 ; par9 = 0 ; par10 = FALSE ;
Parameters (R input):
par1 = 15 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 1 ; par6 = 0 ; par7 = 0 ; par8 = 0 ; par9 = 0 ; par10 = FALSE ;
R code (references can be found in the software module):
par1 <- 45 #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 <- 5 #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')