## Free Statistics

of Irreproducible Research!

Author's title
Author*Unverified author*
R Software Modulerwasp_arimaforecasting.wasp
Title produced by softwareARIMA Forecasting
Date of computationThu, 19 Mar 2020 06:14:54 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2020/Mar/19/t158459505105vmaxhqfvd7cyx.htm/, Retrieved Wed, 21 Apr 2021 07:03:39 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=319093, Retrieved Wed, 21 Apr 2021 07:03:39 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsCorona Indo
Estimated Impact37
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [ARIMA Forecasting] [Corona Indo] [2020-03-19 05:14:54] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
2
0
0
0
2
0
2
13
8
7
0
35
27
21
17
38

 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 1 seconds R Server Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time1 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319093&T=0

[TABLE]
[ROW]
 Summary of computational transaction[/C][/ROW] [ROW] Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW] Raw Output[/C] view raw output of R engine [/C][/ROW] [ROW] Computing time[/C] 1 seconds[/C][/ROW] [ROW] R Server[/C] Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319093&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319093&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 Input view raw input (R code) Raw Output view raw output of R engine Computing time 1 seconds R Server Big Analytics Cloud Computing Center

 Univariate ARIMA Extrapolation Forecast 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[16]) 4 0 - - - - - - - 5 2 - - - - - - - 6 0 - - - - - - - 7 2 - - - - - - - 8 13 - - - - - - - 9 8 - - - - - - - 10 7 - - - - - - - 11 0 - - - - - - - 12 35 - - - - - - - 13 27 - - - - - - - 14 21 - - - - - - - 15 17 - - - - - - - 16 38 - - - - - - - 17 NA 40 15.3892 64.6108 NA 0.5633 0.9988 0.5633 18 NA 38 3.1951 72.8049 NA NA 0.9838 0.5 19 NA 40 -2.6272 82.6272 NA NA 0.9597 0.5366 20 NA 51 1.7784 100.2216 NA NA 0.9349 0.6977 21 NA 46 -9.0315 101.0315 NA NA 0.912 0.6122 22 NA 45 -15.2839 105.2839 NA NA 0.8917 0.59 23 NA 38 -27.1141 103.1141 NA NA 0.8737 0.5 24 NA 73 3.3901 142.6099 NA NA 0.8577 0.8378 25 NA 65 -8.8324 138.8324 NA NA 0.8435 0.7632 26 NA 59 -18.8262 136.8262 NA NA 0.8307 0.7016 27 NA 55 -26.6248 136.6248 NA NA 0.8192 0.6584 28 NA 76 -9.2544 161.2544 NA NA 0.8088 0.8088 29 NA 78 -20.4433 176.4433 NA NA NA 0.7871 30 NA 76 -34.0629 186.0629 NA NA NA 0.7507 31 NA 78 -42.5679 198.5679 NA NA NA 0.7422 32 NA 89 -41.2282 219.2282 NA NA NA 0.7786 33 NA 84 -55.2198 223.2198 NA NA NA 0.7414 34 NA 83 -64.6649 230.6649 NA NA NA 0.7248 35 NA 76 -79.6525 231.6525 NA NA NA 0.6839 36 NA 111 -52.2497 274.2497 NA NA NA 0.8096 37 NA 103 -67.5087 273.5087 NA NA NA 0.7725 38 NA 97 -80.4711 274.4711 NA NA NA 0.7427 39 NA 93 -91.1705 277.1705 NA NA NA 0.7208 40 NA 114 -76.6346 304.6346 NA NA NA 0.7827

\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[16]) \tabularnewline
4 & 0 & - & - & - & - & - & - & - \tabularnewline
5 & 2 & - & - & - & - & - & - & - \tabularnewline
6 & 0 & - & - & - & - & - & - & - \tabularnewline
7 & 2 & - & - & - & - & - & - & - \tabularnewline
8 & 13 & - & - & - & - & - & - & - \tabularnewline
9 & 8 & - & - & - & - & - & - & - \tabularnewline
10 & 7 & - & - & - & - & - & - & - \tabularnewline
11 & 0 & - & - & - & - & - & - & - \tabularnewline
12 & 35 & - & - & - & - & - & - & - \tabularnewline
13 & 27 & - & - & - & - & - & - & - \tabularnewline
14 & 21 & - & - & - & - & - & - & - \tabularnewline
15 & 17 & - & - & - & - & - & - & - \tabularnewline
16 & 38 & - & - & - & - & - & - & - \tabularnewline
17 & NA & 40 & 15.3892 & 64.6108 & NA & 0.5633 & 0.9988 & 0.5633 \tabularnewline
18 & NA & 38 & 3.1951 & 72.8049 & NA & NA & 0.9838 & 0.5 \tabularnewline
19 & NA & 40 & -2.6272 & 82.6272 & NA & NA & 0.9597 & 0.5366 \tabularnewline
20 & NA & 51 & 1.7784 & 100.2216 & NA & NA & 0.9349 & 0.6977 \tabularnewline
21 & NA & 46 & -9.0315 & 101.0315 & NA & NA & 0.912 & 0.6122 \tabularnewline
22 & NA & 45 & -15.2839 & 105.2839 & NA & NA & 0.8917 & 0.59 \tabularnewline
23 & NA & 38 & -27.1141 & 103.1141 & NA & NA & 0.8737 & 0.5 \tabularnewline
24 & NA & 73 & 3.3901 & 142.6099 & NA & NA & 0.8577 & 0.8378 \tabularnewline
25 & NA & 65 & -8.8324 & 138.8324 & NA & NA & 0.8435 & 0.7632 \tabularnewline
26 & NA & 59 & -18.8262 & 136.8262 & NA & NA & 0.8307 & 0.7016 \tabularnewline
27 & NA & 55 & -26.6248 & 136.6248 & NA & NA & 0.8192 & 0.6584 \tabularnewline
28 & NA & 76 & -9.2544 & 161.2544 & NA & NA & 0.8088 & 0.8088 \tabularnewline
29 & NA & 78 & -20.4433 & 176.4433 & NA & NA & NA & 0.7871 \tabularnewline
30 & NA & 76 & -34.0629 & 186.0629 & NA & NA & NA & 0.7507 \tabularnewline
31 & NA & 78 & -42.5679 & 198.5679 & NA & NA & NA & 0.7422 \tabularnewline
32 & NA & 89 & -41.2282 & 219.2282 & NA & NA & NA & 0.7786 \tabularnewline
33 & NA & 84 & -55.2198 & 223.2198 & NA & NA & NA & 0.7414 \tabularnewline
34 & NA & 83 & -64.6649 & 230.6649 & NA & NA & NA & 0.7248 \tabularnewline
35 & NA & 76 & -79.6525 & 231.6525 & NA & NA & NA & 0.6839 \tabularnewline
36 & NA & 111 & -52.2497 & 274.2497 & NA & NA & NA & 0.8096 \tabularnewline
37 & NA & 103 & -67.5087 & 273.5087 & NA & NA & NA & 0.7725 \tabularnewline
38 & NA & 97 & -80.4711 & 274.4711 & NA & NA & NA & 0.7427 \tabularnewline
39 & NA & 93 & -91.1705 & 277.1705 & NA & NA & NA & 0.7208 \tabularnewline
40 & NA & 114 & -76.6346 & 304.6346 & NA & NA & NA & 0.7827 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319093&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[16])[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]5[/C][C]2[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]7[/C][C]2[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]8[/C][C]13[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]9[/C][C]8[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]10[/C][C]7[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]12[/C][C]35[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]13[/C][C]27[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]14[/C][C]21[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]16[/C][C]38[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]17[/C][C]NA[/C][C]40[/C][C]15.3892[/C][C]64.6108[/C][C]NA[/C][C]0.5633[/C][C]0.9988[/C][C]0.5633[/C][/ROW]
[ROW][C]18[/C][C]NA[/C][C]38[/C][C]3.1951[/C][C]72.8049[/C][C]NA[/C][C]NA[/C][C]0.9838[/C][C]0.5[/C][/ROW]
[ROW][C]19[/C][C]NA[/C][C]40[/C][C]-2.6272[/C][C]82.6272[/C][C]NA[/C][C]NA[/C][C]0.9597[/C][C]0.5366[/C][/ROW]
[ROW][C]20[/C][C]NA[/C][C]51[/C][C]1.7784[/C][C]100.2216[/C][C]NA[/C][C]NA[/C][C]0.9349[/C][C]0.6977[/C][/ROW]
[ROW][C]21[/C][C]NA[/C][C]46[/C][C]-9.0315[/C][C]101.0315[/C][C]NA[/C][C]NA[/C][C]0.912[/C][C]0.6122[/C][/ROW]
[ROW][C]22[/C][C]NA[/C][C]45[/C][C]-15.2839[/C][C]105.2839[/C][C]NA[/C][C]NA[/C][C]0.8917[/C][C]0.59[/C][/ROW]
[ROW][C]23[/C][C]NA[/C][C]38[/C][C]-27.1141[/C][C]103.1141[/C][C]NA[/C][C]NA[/C][C]0.8737[/C][C]0.5[/C][/ROW]
[ROW][C]24[/C][C]NA[/C][C]73[/C][C]3.3901[/C][C]142.6099[/C][C]NA[/C][C]NA[/C][C]0.8577[/C][C]0.8378[/C][/ROW]
[ROW][C]25[/C][C]NA[/C][C]65[/C][C]-8.8324[/C][C]138.8324[/C][C]NA[/C][C]NA[/C][C]0.8435[/C][C]0.7632[/C][/ROW]
[ROW][C]26[/C][C]NA[/C][C]59[/C][C]-18.8262[/C][C]136.8262[/C][C]NA[/C][C]NA[/C][C]0.8307[/C][C]0.7016[/C][/ROW]
[ROW][C]27[/C][C]NA[/C][C]55[/C][C]-26.6248[/C][C]136.6248[/C][C]NA[/C][C]NA[/C][C]0.8192[/C][C]0.6584[/C][/ROW]
[ROW][C]28[/C][C]NA[/C][C]76[/C][C]-9.2544[/C][C]161.2544[/C][C]NA[/C][C]NA[/C][C]0.8088[/C][C]0.8088[/C][/ROW]
[ROW][C]29[/C][C]NA[/C][C]78[/C][C]-20.4433[/C][C]176.4433[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]0.7871[/C][/ROW]
[ROW][C]30[/C][C]NA[/C][C]76[/C][C]-34.0629[/C][C]186.0629[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]0.7507[/C][/ROW]
[ROW][C]31[/C][C]NA[/C][C]78[/C][C]-42.5679[/C][C]198.5679[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]0.7422[/C][/ROW]
[ROW][C]32[/C][C]NA[/C][C]89[/C][C]-41.2282[/C][C]219.2282[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]0.7786[/C][/ROW]
[ROW][C]33[/C][C]NA[/C][C]84[/C][C]-55.2198[/C][C]223.2198[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]0.7414[/C][/ROW]
[ROW][C]34[/C][C]NA[/C][C]83[/C][C]-64.6649[/C][C]230.6649[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]0.7248[/C][/ROW]
[ROW][C]35[/C][C]NA[/C][C]76[/C][C]-79.6525[/C][C]231.6525[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]0.6839[/C][/ROW]
[ROW][C]36[/C][C]NA[/C][C]111[/C][C]-52.2497[/C][C]274.2497[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]0.8096[/C][/ROW]
[ROW][C]37[/C][C]NA[/C][C]103[/C][C]-67.5087[/C][C]273.5087[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]0.7725[/C][/ROW]
[ROW][C]38[/C][C]NA[/C][C]97[/C][C]-80.4711[/C][C]274.4711[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]0.7427[/C][/ROW]
[ROW][C]39[/C][C]NA[/C][C]93[/C][C]-91.1705[/C][C]277.1705[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]0.7208[/C][/ROW]
[ROW][C]40[/C][C]NA[/C][C]114[/C][C]-76.6346[/C][C]304.6346[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]0.7827[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319093&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319093&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 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[16]) 4 0 - - - - - - - 5 2 - - - - - - - 6 0 - - - - - - - 7 2 - - - - - - - 8 13 - - - - - - - 9 8 - - - - - - - 10 7 - - - - - - - 11 0 - - - - - - - 12 35 - - - - - - - 13 27 - - - - - - - 14 21 - - - - - - - 15 17 - - - - - - - 16 38 - - - - - - - 17 NA 40 15.3892 64.6108 NA 0.5633 0.9988 0.5633 18 NA 38 3.1951 72.8049 NA NA 0.9838 0.5 19 NA 40 -2.6272 82.6272 NA NA 0.9597 0.5366 20 NA 51 1.7784 100.2216 NA NA 0.9349 0.6977 21 NA 46 -9.0315 101.0315 NA NA 0.912 0.6122 22 NA 45 -15.2839 105.2839 NA NA 0.8917 0.59 23 NA 38 -27.1141 103.1141 NA NA 0.8737 0.5 24 NA 73 3.3901 142.6099 NA NA 0.8577 0.8378 25 NA 65 -8.8324 138.8324 NA NA 0.8435 0.7632 26 NA 59 -18.8262 136.8262 NA NA 0.8307 0.7016 27 NA 55 -26.6248 136.6248 NA NA 0.8192 0.6584 28 NA 76 -9.2544 161.2544 NA NA 0.8088 0.8088 29 NA 78 -20.4433 176.4433 NA NA NA 0.7871 30 NA 76 -34.0629 186.0629 NA NA NA 0.7507 31 NA 78 -42.5679 198.5679 NA NA NA 0.7422 32 NA 89 -41.2282 219.2282 NA NA NA 0.7786 33 NA 84 -55.2198 223.2198 NA NA NA 0.7414 34 NA 83 -64.6649 230.6649 NA NA NA 0.7248 35 NA 76 -79.6525 231.6525 NA NA NA 0.6839 36 NA 111 -52.2497 274.2497 NA NA NA 0.8096 37 NA 103 -67.5087 273.5087 NA NA NA 0.7725 38 NA 97 -80.4711 274.4711 NA NA NA 0.7427 39 NA 93 -91.1705 277.1705 NA NA NA 0.7208 40 NA 114 -76.6346 304.6346 NA NA NA 0.7827

 Univariate ARIMA Extrapolation Forecast Performance time % S.E. PE MAPE sMAPE Sq.E MSE RMSE ScaledE MASE 17 0.3139 NA NA NA NA 0 0 NA NA 18 0.4673 NA NA NA NA NA NA NA NA 19 0.5437 NA NA NA NA NA NA NA NA 20 0.4924 NA NA NA NA NA NA NA NA 21 0.6104 NA NA NA NA NA NA NA NA 22 0.6835 NA NA NA NA NA NA NA NA 23 0.8742 NA NA NA NA NA NA NA NA 24 0.4865 NA NA NA NA NA NA NA NA 25 0.5795 NA NA NA NA NA NA NA NA 26 0.673 NA NA NA NA NA NA NA NA 27 0.7572 NA NA NA NA NA NA NA NA 28 0.5723 NA NA NA NA NA NA NA NA 29 0.6439 NA NA NA NA NA NA NA NA 30 0.7389 NA NA NA NA NA NA NA NA 31 0.7886 NA NA NA NA NA NA NA NA 32 0.7466 NA NA NA NA NA NA NA NA 33 0.8456 NA NA NA NA NA NA NA NA 34 0.9077 NA NA NA NA NA NA NA NA 35 1.0449 NA NA NA NA NA NA NA NA 36 0.7504 NA NA NA NA NA NA NA NA 37 0.8446 NA NA NA NA NA NA NA NA 38 0.9335 NA NA NA NA NA NA NA NA 39 1.0104 NA NA NA NA NA NA NA NA 40 0.8532 NA NA NA NA NA NA NA NA

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & sMAPE & Sq.E & MSE & RMSE & ScaledE & MASE \tabularnewline
17 & 0.3139 & NA & NA & NA & NA & 0 & 0 & NA & NA \tabularnewline
18 & 0.4673 & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
19 & 0.5437 & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
20 & 0.4924 & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
21 & 0.6104 & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
22 & 0.6835 & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
23 & 0.8742 & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
24 & 0.4865 & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
25 & 0.5795 & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
26 & 0.673 & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
27 & 0.7572 & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
28 & 0.5723 & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
29 & 0.6439 & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
30 & 0.7389 & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
31 & 0.7886 & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
32 & 0.7466 & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
33 & 0.8456 & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
34 & 0.9077 & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
35 & 1.0449 & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
36 & 0.7504 & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
37 & 0.8446 & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
38 & 0.9335 & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
39 & 1.0104 & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
40 & 0.8532 & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319093&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]sMAPE[/C][C]Sq.E[/C][C]MSE[/C][C]RMSE[/C][C]ScaledE[/C][C]MASE[/C][/ROW]
[ROW][C]17[/C][C]0.3139[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]0[/C][C]0[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]18[/C][C]0.4673[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]19[/C][C]0.5437[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]20[/C][C]0.4924[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]21[/C][C]0.6104[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]22[/C][C]0.6835[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]23[/C][C]0.8742[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]24[/C][C]0.4865[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]25[/C][C]0.5795[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]26[/C][C]0.673[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]27[/C][C]0.7572[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]28[/C][C]0.5723[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]29[/C][C]0.6439[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]30[/C][C]0.7389[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]31[/C][C]0.7886[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]32[/C][C]0.7466[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]33[/C][C]0.8456[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]34[/C][C]0.9077[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]35[/C][C]1.0449[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]36[/C][C]0.7504[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]37[/C][C]0.8446[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]38[/C][C]0.9335[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]39[/C][C]1.0104[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]40[/C][C]0.8532[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319093&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319093&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. PE MAPE sMAPE Sq.E MSE RMSE ScaledE MASE 17 0.3139 NA NA NA NA 0 0 NA NA 18 0.4673 NA NA NA NA NA NA NA NA 19 0.5437 NA NA NA NA NA NA NA NA 20 0.4924 NA NA NA NA NA NA NA NA 21 0.6104 NA NA NA NA NA NA NA NA 22 0.6835 NA NA NA NA NA NA NA NA 23 0.8742 NA NA NA NA NA NA NA NA 24 0.4865 NA NA NA NA NA NA NA NA 25 0.5795 NA NA NA NA NA NA NA NA 26 0.673 NA NA NA NA NA NA NA NA 27 0.7572 NA NA NA NA NA NA NA NA 28 0.5723 NA NA NA NA NA NA NA NA 29 0.6439 NA NA NA NA NA NA NA NA 30 0.7389 NA NA NA NA NA NA NA NA 31 0.7886 NA NA NA NA NA NA NA NA 32 0.7466 NA NA NA NA NA NA NA NA 33 0.8456 NA NA NA NA NA NA NA NA 34 0.9077 NA NA NA NA NA NA NA NA 35 1.0449 NA NA NA NA NA NA NA NA 36 0.7504 NA NA NA NA NA NA NA NA 37 0.8446 NA NA NA NA NA NA NA NA 38 0.9335 NA NA NA NA NA NA NA NA 39 1.0104 NA NA NA NA NA NA NA NA 40 0.8532 NA NA NA NA NA NA NA NA

par10 <- 'FALSE'par9 <- '0'par8 <- '0'par7 <- '0'par6 <- '0'par5 <- '12'par4 <- '1'par3 <- '1'par2 <- '1'par1 <- '17'par1 <- as.numeric(par1) #cut off periodspar2 <- as.numeric(par2) #lambdapar3 <- as.numeric(par3) #degree of non-seasonal differencingpar4 <- as.numeric(par4) #degree of seasonal differencingpar5 <- as.numeric(par5) #seasonal periodpar6 <- as.numeric(par6) #ppar7 <- as.numeric(par7) #qpar8 <- as.numeric(par8) #Ppar9 <- as.numeric(par9) #Qif (par10 == 'TRUE') par10 <- TRUEif (par10 == 'FALSE') par10 <- FALSEif (par2 == 0) x <- log(x)if (par2 != 0) x <- x^par2lx <- length(x)first <- lx - 2*par1nx <- lx - par1nx1 <- nx + 1fx <- lx - nxif (fx < 1) {fx <- par5*2nx1 <- lx + fx - 1first <- lx - 2*fx}first <- 1if (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,fx))(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 <- lblb <- ubub <- 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.spe <- array(0, dim=fx)perf.scalederr <- array(0, dim=fx)perf.mase <- array(0, dim=fx)perf.mase1 <- array(0, dim=fx)perf.mape <- array(0, dim=fx)perf.smape <- array(0, dim=fx)perf.mape1 <- array(0, dim=fx)perf.smape1 <- 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)perf.scaleddenom <- 0for (i in 2:fx) {perf.scaleddenom = perf.scaleddenom + abs(x[nx+i] - x[nx+i-1])}perf.scaleddenom = perf.scaleddenom / (fx-1)for (i in 1:fx) {locSD <- (ub[i] - forecast$pred[i]) / 1.96perf.scalederr[i] = (x[nx+i] - forecast$pred[i]) / perf.scaleddenomperf.pe[i] = (x[nx+i] - forecast$pred[i]) / x[nx+i]perf.spe[i] = 2*(x[nx+i] - forecast$pred[i]) / (x[nx+i] + forecast$pred[i])perf.se[i] = (x[nx+i] - forecast$pred[i])^2prob.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.smape[1] = abs(perf.spe[1])perf.mape1[1] = perf.mape[1]perf.smape1[1] = perf.smape[1]perf.mse[1] = perf.se[1]perf.mase[1] = abs(perf.scalederr[1])perf.mase1[1] = perf.mase[1]for (i in 2:fx) {perf.mape[i] = perf.mape[i-1] + abs(perf.pe[i])perf.mape1[i] = perf.mape[i] / iperf.smape[i] = perf.smape[i-1] + abs(perf.spe[i])perf.smape1[i] = perf.smape[i] / iperf.mse[i] = perf.mse[i-1] + perf.se[i]perf.mse1[i] = perf.mse[i] / iperf.mase[i] = perf.mase[i-1] + abs(perf.scalederr[i])perf.mase1[i] = perf.mase[i] / i}perf.rmse = sqrt(perf.mse1)bitmap(file='test2.png')plot(forecast$pred, pch=19, type='b',main='ARIMA Extrapolation Forecast', ylab='Forecast and 95% CI', xlab='time',ylim=c(min(lb),max(ub)))dum <- forecast$preddum[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',10,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,'sMAPE',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.element(a,'ScaledE',1,header=TRUE)a<-table.element(a,'MASE',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.smape1[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.element(a,round(perf.scalederr[i],4))a<-table.element(a,round(perf.mase1[i],4))a<-table.row.end(a)}a<-table.end(a)table.save(a,file='mytable1.tab')