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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, 26 Dec 2010 14:35:20 +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/26/t1293374137tk2fltcc1nmu1um.htm/, Retrieved Mon, 06 May 2024 17:35:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=115619, Retrieved Mon, 06 May 2024 17:35:36 +0000
QR Codes:

Original text written by user:Lamda= 1 d=1 D=0 p=0 q=0 P=2 Q=0
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact131

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 Backward Selection] [Unemployment] [2010-11-29 17:10:28] [b98453cac15ba1066b407e146608df68]
F   PD      [ARIMA Backward Selection] [Workshop 6 'Aanta...] [2010-12-14 18:47:02] [40c8b935cbad1b0be3c22a481f9723f7]
- R P         [ARIMA Backward Selection] [ARIMA backward se...] [2010-12-17 15:45:37] [75b8170d590d2aca2c97c1862bb2167f]
-   PD          [ARIMA Backward Selection] [ARIMA BACKWARD Se...] [2010-12-26 13:20:22] [c895532cb7349383dee5125244983cc8]
- RMP               [ARIMA Forecasting] [TUM Arima Forecas...] [2010-12-26 14:35:20] [9b9a58c480cb5dafe4bfceb51e1cfd12] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
10
10
10
10
10
9.94
10.06
10.06
10.06
10.06
10.06
10.06
10.06
10.06
10.06
10.06
10.06
10.06
10.06
10.06
9.94
9.94
9.94
9.94
9.94
9.94
10.06
10.06
9.94
10.06
10.06
10.06
10.18
10.28
10.28
10.18
10.28
10.28
10.28
10.18
10.28
10.28
10.18
10.18
10.18
10.28
10.28
10.18
10.18
10.18
10.18
10.18
10.18
10.28
10.28
10.28
10.18
10.18
10.18
10.28
10.18
10.18
10.28
10.18
10.18
10.18
10.28
10.28
10.28
10.28
10.28
10.28
10.18
10.18
10.18
10.18
10.18
10.18
10.18
10.18
10.18
10.28
10.28
10.28
10.28
10.28
10.28
10.28
10.28
10.18
10.28
10.28
10.28
10.28
10.18
10.28
10.28
10.28
10.18
10.18
10.28
10.28
10.28
10.28
10.28
10.28
10.28
10.18
10.28
10.28
10.28
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.34
10.34
10.34
10.42
10.42
10.42
10.42
10.34
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.34
10.34
10.34
10.34
10.42
10.42
10.34
10.34
10.34
10.42
10.42
10.42
10.34
10.34
10.34
10.34
10.34
10.42
10.42
10.42
10.34
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.34
10.34
10.34
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.34
10.34
10.42
10.42
10.55
10.55
10.55
10.55
10.55
10.55
10.55
10.55
10.55
10.55
10.49
10.49
10.55
10.55
10.55
10.55
10.55
10.55
10.49
10.55
10.55
10.55
10.55
10.55
10.55
10.49
10.49
10.55
10.55
10.55
10.49
10.55
10.55
10.55
10.55
10.55
10.55
10.55
10.55
10.55
10.49
10.49
10.49
10.55
10.55
10.55
10.49
10.55
10.55
10.55
10.63
10.63
10.63
10.63
10.63
10.57
10.63
10.63
10.63
10.63
10.57
10.57
10.57
10.63
10.63
10.63
10.63
10.57
10.63
10.63
10.57
10.63
10.63
10.63
10.63
10.57
10.63
10.6
10.7
10.7
10.7
10.7
10.7
10.7
10.7
10.7
10.7
10.7
10.6
10.6
10.6
10.6
10.7
10.7
10.6
10.7
10.7
10.7
10.7
10.7
10.7
10.7
10.7
10.7
10.7
10.7
10.7
10.7
10.7
10.8
10.8
10.8
10.8
10.7
10.7
10.7
10.85
10.75
10.75
10.75
10.75
10.75
10.75
10.75
10.75
10.75
10.75
10.75
10.85
10.85
10.85
10.85
10.85
10.85
10.85
10.85
10.85
10.85
10.75
10.75
10.85
10.85
10.85
10.85
10.75
10.75
10.75
10.75
10.75

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=115619&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=115619&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115619&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[326])
31410.8-------
31510.7-------
31610.7-------
31710.7-------
31810.85-------
31910.75-------
32010.75-------
32110.75-------
32210.75-------
32310.75-------
32410.75-------
32510.75-------
32610.75-------
32710.7510.750310.666310.83440.49680.50320.87990.5032
32810.7510.750510.648110.85290.49610.50390.83310.5039
32910.7510.750610.63810.86310.49590.50410.81080.5041
33010.8510.750610.630810.87050.05210.50410.05210.5041
33110.8510.750610.624710.87650.0610.0610.5040.504
33210.8510.750610.619310.8820.06910.06910.50380.5038
33310.8510.750610.614210.88710.07670.07670.50370.5037
33410.8510.750710.609410.89190.0840.0840.50360.5036
33510.8510.750710.604810.89650.09090.09090.50350.5035
33610.8510.750710.600310.9010.09760.09760.50340.5034
33710.8510.750710.59610.90530.1040.1040.50330.5033
33810.8510.750710.591810.90950.11020.11020.50320.5032
33910.8510.750710.587710.91360.11610.11610.50310.5031
34010.7510.750710.583710.91760.49690.12180.50310.5031
34110.7510.750710.579810.92150.4970.5030.5030.503
34210.8510.750710.575910.92540.13250.50290.13250.5029
34310.8510.750710.572210.92910.13760.13760.13760.5029
34410.8510.750710.568510.93280.14250.14250.14250.5028
34510.8510.750710.56510.93630.14720.14720.14720.5027
34610.7510.750710.561410.93990.49730.15170.15170.5027
34710.7510.750710.55810.94330.49740.50260.15610.5026
34810.7510.750710.554610.94670.49740.50260.16030.5026
34910.7510.750710.551210.95010.49740.50260.16440.5026
35010.7510.750710.54810.95330.49750.50250.16840.5025

\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[326]) \tabularnewline
314 & 10.8 & - & - & - & - & - & - & - \tabularnewline
315 & 10.7 & - & - & - & - & - & - & - \tabularnewline
316 & 10.7 & - & - & - & - & - & - & - \tabularnewline
317 & 10.7 & - & - & - & - & - & - & - \tabularnewline
318 & 10.85 & - & - & - & - & - & - & - \tabularnewline
319 & 10.75 & - & - & - & - & - & - & - \tabularnewline
320 & 10.75 & - & - & - & - & - & - & - \tabularnewline
321 & 10.75 & - & - & - & - & - & - & - \tabularnewline
322 & 10.75 & - & - & - & - & - & - & - \tabularnewline
323 & 10.75 & - & - & - & - & - & - & - \tabularnewline
324 & 10.75 & - & - & - & - & - & - & - \tabularnewline
325 & 10.75 & - & - & - & - & - & - & - \tabularnewline
326 & 10.75 & - & - & - & - & - & - & - \tabularnewline
327 & 10.75 & 10.7503 & 10.6663 & 10.8344 & 0.4968 & 0.5032 & 0.8799 & 0.5032 \tabularnewline
328 & 10.75 & 10.7505 & 10.6481 & 10.8529 & 0.4961 & 0.5039 & 0.8331 & 0.5039 \tabularnewline
329 & 10.75 & 10.7506 & 10.638 & 10.8631 & 0.4959 & 0.5041 & 0.8108 & 0.5041 \tabularnewline
330 & 10.85 & 10.7506 & 10.6308 & 10.8705 & 0.0521 & 0.5041 & 0.0521 & 0.5041 \tabularnewline
331 & 10.85 & 10.7506 & 10.6247 & 10.8765 & 0.061 & 0.061 & 0.504 & 0.504 \tabularnewline
332 & 10.85 & 10.7506 & 10.6193 & 10.882 & 0.0691 & 0.0691 & 0.5038 & 0.5038 \tabularnewline
333 & 10.85 & 10.7506 & 10.6142 & 10.8871 & 0.0767 & 0.0767 & 0.5037 & 0.5037 \tabularnewline
334 & 10.85 & 10.7507 & 10.6094 & 10.8919 & 0.084 & 0.084 & 0.5036 & 0.5036 \tabularnewline
335 & 10.85 & 10.7507 & 10.6048 & 10.8965 & 0.0909 & 0.0909 & 0.5035 & 0.5035 \tabularnewline
336 & 10.85 & 10.7507 & 10.6003 & 10.901 & 0.0976 & 0.0976 & 0.5034 & 0.5034 \tabularnewline
337 & 10.85 & 10.7507 & 10.596 & 10.9053 & 0.104 & 0.104 & 0.5033 & 0.5033 \tabularnewline
338 & 10.85 & 10.7507 & 10.5918 & 10.9095 & 0.1102 & 0.1102 & 0.5032 & 0.5032 \tabularnewline
339 & 10.85 & 10.7507 & 10.5877 & 10.9136 & 0.1161 & 0.1161 & 0.5031 & 0.5031 \tabularnewline
340 & 10.75 & 10.7507 & 10.5837 & 10.9176 & 0.4969 & 0.1218 & 0.5031 & 0.5031 \tabularnewline
341 & 10.75 & 10.7507 & 10.5798 & 10.9215 & 0.497 & 0.503 & 0.503 & 0.503 \tabularnewline
342 & 10.85 & 10.7507 & 10.5759 & 10.9254 & 0.1325 & 0.5029 & 0.1325 & 0.5029 \tabularnewline
343 & 10.85 & 10.7507 & 10.5722 & 10.9291 & 0.1376 & 0.1376 & 0.1376 & 0.5029 \tabularnewline
344 & 10.85 & 10.7507 & 10.5685 & 10.9328 & 0.1425 & 0.1425 & 0.1425 & 0.5028 \tabularnewline
345 & 10.85 & 10.7507 & 10.565 & 10.9363 & 0.1472 & 0.1472 & 0.1472 & 0.5027 \tabularnewline
346 & 10.75 & 10.7507 & 10.5614 & 10.9399 & 0.4973 & 0.1517 & 0.1517 & 0.5027 \tabularnewline
347 & 10.75 & 10.7507 & 10.558 & 10.9433 & 0.4974 & 0.5026 & 0.1561 & 0.5026 \tabularnewline
348 & 10.75 & 10.7507 & 10.5546 & 10.9467 & 0.4974 & 0.5026 & 0.1603 & 0.5026 \tabularnewline
349 & 10.75 & 10.7507 & 10.5512 & 10.9501 & 0.4974 & 0.5026 & 0.1644 & 0.5026 \tabularnewline
350 & 10.75 & 10.7507 & 10.548 & 10.9533 & 0.4975 & 0.5025 & 0.1684 & 0.5025 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115619&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[326])[/C][/ROW]
[ROW][C]314[/C][C]10.8[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]315[/C][C]10.7[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]316[/C][C]10.7[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]317[/C][C]10.7[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]318[/C][C]10.85[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]319[/C][C]10.75[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]320[/C][C]10.75[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]321[/C][C]10.75[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]322[/C][C]10.75[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]323[/C][C]10.75[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]324[/C][C]10.75[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]325[/C][C]10.75[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]326[/C][C]10.75[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]327[/C][C]10.75[/C][C]10.7503[/C][C]10.6663[/C][C]10.8344[/C][C]0.4968[/C][C]0.5032[/C][C]0.8799[/C][C]0.5032[/C][/ROW]
[ROW][C]328[/C][C]10.75[/C][C]10.7505[/C][C]10.6481[/C][C]10.8529[/C][C]0.4961[/C][C]0.5039[/C][C]0.8331[/C][C]0.5039[/C][/ROW]
[ROW][C]329[/C][C]10.75[/C][C]10.7506[/C][C]10.638[/C][C]10.8631[/C][C]0.4959[/C][C]0.5041[/C][C]0.8108[/C][C]0.5041[/C][/ROW]
[ROW][C]330[/C][C]10.85[/C][C]10.7506[/C][C]10.6308[/C][C]10.8705[/C][C]0.0521[/C][C]0.5041[/C][C]0.0521[/C][C]0.5041[/C][/ROW]
[ROW][C]331[/C][C]10.85[/C][C]10.7506[/C][C]10.6247[/C][C]10.8765[/C][C]0.061[/C][C]0.061[/C][C]0.504[/C][C]0.504[/C][/ROW]
[ROW][C]332[/C][C]10.85[/C][C]10.7506[/C][C]10.6193[/C][C]10.882[/C][C]0.0691[/C][C]0.0691[/C][C]0.5038[/C][C]0.5038[/C][/ROW]
[ROW][C]333[/C][C]10.85[/C][C]10.7506[/C][C]10.6142[/C][C]10.8871[/C][C]0.0767[/C][C]0.0767[/C][C]0.5037[/C][C]0.5037[/C][/ROW]
[ROW][C]334[/C][C]10.85[/C][C]10.7507[/C][C]10.6094[/C][C]10.8919[/C][C]0.084[/C][C]0.084[/C][C]0.5036[/C][C]0.5036[/C][/ROW]
[ROW][C]335[/C][C]10.85[/C][C]10.7507[/C][C]10.6048[/C][C]10.8965[/C][C]0.0909[/C][C]0.0909[/C][C]0.5035[/C][C]0.5035[/C][/ROW]
[ROW][C]336[/C][C]10.85[/C][C]10.7507[/C][C]10.6003[/C][C]10.901[/C][C]0.0976[/C][C]0.0976[/C][C]0.5034[/C][C]0.5034[/C][/ROW]
[ROW][C]337[/C][C]10.85[/C][C]10.7507[/C][C]10.596[/C][C]10.9053[/C][C]0.104[/C][C]0.104[/C][C]0.5033[/C][C]0.5033[/C][/ROW]
[ROW][C]338[/C][C]10.85[/C][C]10.7507[/C][C]10.5918[/C][C]10.9095[/C][C]0.1102[/C][C]0.1102[/C][C]0.5032[/C][C]0.5032[/C][/ROW]
[ROW][C]339[/C][C]10.85[/C][C]10.7507[/C][C]10.5877[/C][C]10.9136[/C][C]0.1161[/C][C]0.1161[/C][C]0.5031[/C][C]0.5031[/C][/ROW]
[ROW][C]340[/C][C]10.75[/C][C]10.7507[/C][C]10.5837[/C][C]10.9176[/C][C]0.4969[/C][C]0.1218[/C][C]0.5031[/C][C]0.5031[/C][/ROW]
[ROW][C]341[/C][C]10.75[/C][C]10.7507[/C][C]10.5798[/C][C]10.9215[/C][C]0.497[/C][C]0.503[/C][C]0.503[/C][C]0.503[/C][/ROW]
[ROW][C]342[/C][C]10.85[/C][C]10.7507[/C][C]10.5759[/C][C]10.9254[/C][C]0.1325[/C][C]0.5029[/C][C]0.1325[/C][C]0.5029[/C][/ROW]
[ROW][C]343[/C][C]10.85[/C][C]10.7507[/C][C]10.5722[/C][C]10.9291[/C][C]0.1376[/C][C]0.1376[/C][C]0.1376[/C][C]0.5029[/C][/ROW]
[ROW][C]344[/C][C]10.85[/C][C]10.7507[/C][C]10.5685[/C][C]10.9328[/C][C]0.1425[/C][C]0.1425[/C][C]0.1425[/C][C]0.5028[/C][/ROW]
[ROW][C]345[/C][C]10.85[/C][C]10.7507[/C][C]10.565[/C][C]10.9363[/C][C]0.1472[/C][C]0.1472[/C][C]0.1472[/C][C]0.5027[/C][/ROW]
[ROW][C]346[/C][C]10.75[/C][C]10.7507[/C][C]10.5614[/C][C]10.9399[/C][C]0.4973[/C][C]0.1517[/C][C]0.1517[/C][C]0.5027[/C][/ROW]
[ROW][C]347[/C][C]10.75[/C][C]10.7507[/C][C]10.558[/C][C]10.9433[/C][C]0.4974[/C][C]0.5026[/C][C]0.1561[/C][C]0.5026[/C][/ROW]
[ROW][C]348[/C][C]10.75[/C][C]10.7507[/C][C]10.5546[/C][C]10.9467[/C][C]0.4974[/C][C]0.5026[/C][C]0.1603[/C][C]0.5026[/C][/ROW]
[ROW][C]349[/C][C]10.75[/C][C]10.7507[/C][C]10.5512[/C][C]10.9501[/C][C]0.4974[/C][C]0.5026[/C][C]0.1644[/C][C]0.5026[/C][/ROW]
[ROW][C]350[/C][C]10.75[/C][C]10.7507[/C][C]10.548[/C][C]10.9533[/C][C]0.4975[/C][C]0.5025[/C][C]0.1684[/C][C]0.5025[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115619&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115619&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[326])
31410.8-------
31510.7-------
31610.7-------
31710.7-------
31810.85-------
31910.75-------
32010.75-------
32110.75-------
32210.75-------
32310.75-------
32410.75-------
32510.75-------
32610.75-------
32710.7510.750310.666310.83440.49680.50320.87990.5032
32810.7510.750510.648110.85290.49610.50390.83310.5039
32910.7510.750610.63810.86310.49590.50410.81080.5041
33010.8510.750610.630810.87050.05210.50410.05210.5041
33110.8510.750610.624710.87650.0610.0610.5040.504
33210.8510.750610.619310.8820.06910.06910.50380.5038
33310.8510.750610.614210.88710.07670.07670.50370.5037
33410.8510.750710.609410.89190.0840.0840.50360.5036
33510.8510.750710.604810.89650.09090.09090.50350.5035
33610.8510.750710.600310.9010.09760.09760.50340.5034
33710.8510.750710.59610.90530.1040.1040.50330.5033
33810.8510.750710.591810.90950.11020.11020.50320.5032
33910.8510.750710.587710.91360.11610.11610.50310.5031
34010.7510.750710.583710.91760.49690.12180.50310.5031
34110.7510.750710.579810.92150.4970.5030.5030.503
34210.8510.750710.575910.92540.13250.50290.13250.5029
34310.8510.750710.572210.92910.13760.13760.13760.5029
34410.8510.750710.568510.93280.14250.14250.14250.5028
34510.8510.750710.56510.93630.14720.14720.14720.5027
34610.7510.750710.561410.93990.49730.15170.15170.5027
34710.7510.750710.55810.94330.49740.50260.15610.5026
34810.7510.750710.554610.94670.49740.50260.16030.5026
34910.7510.750710.551210.95010.49740.50260.16440.5026
35010.7510.750710.54810.95330.49750.50250.16840.5025






Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
3270.00400000
3280.004900004e-04
3290.0053-1e-040005e-04
3300.00570.00920.00230.00990.00250.0497
3310.0060.00920.00370.00990.00390.0628
3320.00620.00920.00460.00990.00490.0703
3330.00650.00920.00530.00990.00560.0751
3340.00670.00920.00580.00990.00620.0786
3350.00690.00920.00620.00990.00660.0811
3360.00710.00920.00650.00990.00690.0831
3370.00730.00920.00670.00990.00720.0847
3380.00750.00920.00690.00990.00740.086
3390.00770.00920.00710.00990.00760.0871
3400.0079-1e-040.006600.00710.084
3410.0081-1e-040.006200.00660.0811
3420.00830.00920.00640.00990.00680.0824
3430.00850.00920.00650.00990.0070.0835
3440.00860.00920.00670.00990.00710.0844
3450.00880.00920.00680.00990.00730.0853
3460.009-1e-040.006500.00690.0831
3470.0091-1e-040.006200.00660.0811
3480.0093-1e-040.005900.00630.0793
3490.0095-1e-040.005600.0060.0775
3500.0096-1e-040.005400.00580.0759

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
327 & 0.004 & 0 & 0 & 0 & 0 & 0 \tabularnewline
328 & 0.0049 & 0 & 0 & 0 & 0 & 4e-04 \tabularnewline
329 & 0.0053 & -1e-04 & 0 & 0 & 0 & 5e-04 \tabularnewline
330 & 0.0057 & 0.0092 & 0.0023 & 0.0099 & 0.0025 & 0.0497 \tabularnewline
331 & 0.006 & 0.0092 & 0.0037 & 0.0099 & 0.0039 & 0.0628 \tabularnewline
332 & 0.0062 & 0.0092 & 0.0046 & 0.0099 & 0.0049 & 0.0703 \tabularnewline
333 & 0.0065 & 0.0092 & 0.0053 & 0.0099 & 0.0056 & 0.0751 \tabularnewline
334 & 0.0067 & 0.0092 & 0.0058 & 0.0099 & 0.0062 & 0.0786 \tabularnewline
335 & 0.0069 & 0.0092 & 0.0062 & 0.0099 & 0.0066 & 0.0811 \tabularnewline
336 & 0.0071 & 0.0092 & 0.0065 & 0.0099 & 0.0069 & 0.0831 \tabularnewline
337 & 0.0073 & 0.0092 & 0.0067 & 0.0099 & 0.0072 & 0.0847 \tabularnewline
338 & 0.0075 & 0.0092 & 0.0069 & 0.0099 & 0.0074 & 0.086 \tabularnewline
339 & 0.0077 & 0.0092 & 0.0071 & 0.0099 & 0.0076 & 0.0871 \tabularnewline
340 & 0.0079 & -1e-04 & 0.0066 & 0 & 0.0071 & 0.084 \tabularnewline
341 & 0.0081 & -1e-04 & 0.0062 & 0 & 0.0066 & 0.0811 \tabularnewline
342 & 0.0083 & 0.0092 & 0.0064 & 0.0099 & 0.0068 & 0.0824 \tabularnewline
343 & 0.0085 & 0.0092 & 0.0065 & 0.0099 & 0.007 & 0.0835 \tabularnewline
344 & 0.0086 & 0.0092 & 0.0067 & 0.0099 & 0.0071 & 0.0844 \tabularnewline
345 & 0.0088 & 0.0092 & 0.0068 & 0.0099 & 0.0073 & 0.0853 \tabularnewline
346 & 0.009 & -1e-04 & 0.0065 & 0 & 0.0069 & 0.0831 \tabularnewline
347 & 0.0091 & -1e-04 & 0.0062 & 0 & 0.0066 & 0.0811 \tabularnewline
348 & 0.0093 & -1e-04 & 0.0059 & 0 & 0.0063 & 0.0793 \tabularnewline
349 & 0.0095 & -1e-04 & 0.0056 & 0 & 0.006 & 0.0775 \tabularnewline
350 & 0.0096 & -1e-04 & 0.0054 & 0 & 0.0058 & 0.0759 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115619&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]327[/C][C]0.004[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]328[/C][C]0.0049[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]4e-04[/C][/ROW]
[ROW][C]329[/C][C]0.0053[/C][C]-1e-04[/C][C]0[/C][C]0[/C][C]0[/C][C]5e-04[/C][/ROW]
[ROW][C]330[/C][C]0.0057[/C][C]0.0092[/C][C]0.0023[/C][C]0.0099[/C][C]0.0025[/C][C]0.0497[/C][/ROW]
[ROW][C]331[/C][C]0.006[/C][C]0.0092[/C][C]0.0037[/C][C]0.0099[/C][C]0.0039[/C][C]0.0628[/C][/ROW]
[ROW][C]332[/C][C]0.0062[/C][C]0.0092[/C][C]0.0046[/C][C]0.0099[/C][C]0.0049[/C][C]0.0703[/C][/ROW]
[ROW][C]333[/C][C]0.0065[/C][C]0.0092[/C][C]0.0053[/C][C]0.0099[/C][C]0.0056[/C][C]0.0751[/C][/ROW]
[ROW][C]334[/C][C]0.0067[/C][C]0.0092[/C][C]0.0058[/C][C]0.0099[/C][C]0.0062[/C][C]0.0786[/C][/ROW]
[ROW][C]335[/C][C]0.0069[/C][C]0.0092[/C][C]0.0062[/C][C]0.0099[/C][C]0.0066[/C][C]0.0811[/C][/ROW]
[ROW][C]336[/C][C]0.0071[/C][C]0.0092[/C][C]0.0065[/C][C]0.0099[/C][C]0.0069[/C][C]0.0831[/C][/ROW]
[ROW][C]337[/C][C]0.0073[/C][C]0.0092[/C][C]0.0067[/C][C]0.0099[/C][C]0.0072[/C][C]0.0847[/C][/ROW]
[ROW][C]338[/C][C]0.0075[/C][C]0.0092[/C][C]0.0069[/C][C]0.0099[/C][C]0.0074[/C][C]0.086[/C][/ROW]
[ROW][C]339[/C][C]0.0077[/C][C]0.0092[/C][C]0.0071[/C][C]0.0099[/C][C]0.0076[/C][C]0.0871[/C][/ROW]
[ROW][C]340[/C][C]0.0079[/C][C]-1e-04[/C][C]0.0066[/C][C]0[/C][C]0.0071[/C][C]0.084[/C][/ROW]
[ROW][C]341[/C][C]0.0081[/C][C]-1e-04[/C][C]0.0062[/C][C]0[/C][C]0.0066[/C][C]0.0811[/C][/ROW]
[ROW][C]342[/C][C]0.0083[/C][C]0.0092[/C][C]0.0064[/C][C]0.0099[/C][C]0.0068[/C][C]0.0824[/C][/ROW]
[ROW][C]343[/C][C]0.0085[/C][C]0.0092[/C][C]0.0065[/C][C]0.0099[/C][C]0.007[/C][C]0.0835[/C][/ROW]
[ROW][C]344[/C][C]0.0086[/C][C]0.0092[/C][C]0.0067[/C][C]0.0099[/C][C]0.0071[/C][C]0.0844[/C][/ROW]
[ROW][C]345[/C][C]0.0088[/C][C]0.0092[/C][C]0.0068[/C][C]0.0099[/C][C]0.0073[/C][C]0.0853[/C][/ROW]
[ROW][C]346[/C][C]0.009[/C][C]-1e-04[/C][C]0.0065[/C][C]0[/C][C]0.0069[/C][C]0.0831[/C][/ROW]
[ROW][C]347[/C][C]0.0091[/C][C]-1e-04[/C][C]0.0062[/C][C]0[/C][C]0.0066[/C][C]0.0811[/C][/ROW]
[ROW][C]348[/C][C]0.0093[/C][C]-1e-04[/C][C]0.0059[/C][C]0[/C][C]0.0063[/C][C]0.0793[/C][/ROW]
[ROW][C]349[/C][C]0.0095[/C][C]-1e-04[/C][C]0.0056[/C][C]0[/C][C]0.006[/C][C]0.0775[/C][/ROW]
[ROW][C]350[/C][C]0.0096[/C][C]-1e-04[/C][C]0.0054[/C][C]0[/C][C]0.0058[/C][C]0.0759[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115619&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115619&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
3270.00400000
3280.004900004e-04
3290.0053-1e-040005e-04
3300.00570.00920.00230.00990.00250.0497
3310.0060.00920.00370.00990.00390.0628
3320.00620.00920.00460.00990.00490.0703
3330.00650.00920.00530.00990.00560.0751
3340.00670.00920.00580.00990.00620.0786
3350.00690.00920.00620.00990.00660.0811
3360.00710.00920.00650.00990.00690.0831
3370.00730.00920.00670.00990.00720.0847
3380.00750.00920.00690.00990.00740.086
3390.00770.00920.00710.00990.00760.0871
3400.0079-1e-040.006600.00710.084
3410.0081-1e-040.006200.00660.0811
3420.00830.00920.00640.00990.00680.0824
3430.00850.00920.00650.00990.0070.0835
3440.00860.00920.00670.00990.00710.0844
3450.00880.00920.00680.00990.00730.0853
3460.009-1e-040.006500.00690.0831
3470.0091-1e-040.006200.00660.0811
3480.0093-1e-040.005900.00630.0793
3490.0095-1e-040.005600.0060.0775
3500.0096-1e-040.005400.00580.0759


Figures (Output of Computation)

Input Parameters & R Code

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