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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 Outputview raw output of R engine
Computing time1 seconds
R ServerBig 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 Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center







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[16])
40-------
52-------
60-------
72-------
813-------
98-------
107-------
110-------
1235-------
1327-------
1421-------
1517-------
1638-------
17NA4015.389264.6108NA0.56330.99880.5633
18NA383.195172.8049NANA0.98380.5
19NA40-2.627282.6272NANA0.95970.5366
20NA511.7784100.2216NANA0.93490.6977
21NA46-9.0315101.0315NANA0.9120.6122
22NA45-15.2839105.2839NANA0.89170.59
23NA38-27.1141103.1141NANA0.87370.5
24NA733.3901142.6099NANA0.85770.8378
25NA65-8.8324138.8324NANA0.84350.7632
26NA59-18.8262136.8262NANA0.83070.7016
27NA55-26.6248136.6248NANA0.81920.6584
28NA76-9.2544161.2544NANA0.80880.8088
29NA78-20.4433176.4433NANANA0.7871
30NA76-34.0629186.0629NANANA0.7507
31NA78-42.5679198.5679NANANA0.7422
32NA89-41.2282219.2282NANANA0.7786
33NA84-55.2198223.2198NANANA0.7414
34NA83-64.6649230.6649NANANA0.7248
35NA76-79.6525231.6525NANANA0.6839
36NA111-52.2497274.2497NANANA0.8096
37NA103-67.5087273.5087NANANA0.7725
38NA97-80.4711274.4711NANANA0.7427
39NA93-91.1705277.1705NANANA0.7208
40NA114-76.6346304.6346NANANA0.7827

\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
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[16])
40-------
52-------
60-------
72-------
813-------
98-------
107-------
110-------
1235-------
1327-------
1421-------
1517-------
1638-------
17NA4015.389264.6108NA0.56330.99880.5633
18NA383.195172.8049NANA0.98380.5
19NA40-2.627282.6272NANA0.95970.5366
20NA511.7784100.2216NANA0.93490.6977
21NA46-9.0315101.0315NANA0.9120.6122
22NA45-15.2839105.2839NANA0.89170.59
23NA38-27.1141103.1141NANA0.87370.5
24NA733.3901142.6099NANA0.85770.8378
25NA65-8.8324138.8324NANA0.84350.7632
26NA59-18.8262136.8262NANA0.83070.7016
27NA55-26.6248136.6248NANA0.81920.6584
28NA76-9.2544161.2544NANA0.80880.8088
29NA78-20.4433176.4433NANANA0.7871
30NA76-34.0629186.0629NANANA0.7507
31NA78-42.5679198.5679NANANA0.7422
32NA89-41.2282219.2282NANANA0.7786
33NA84-55.2198223.2198NANANA0.7414
34NA83-64.6649230.6649NANANA0.7248
35NA76-79.6525231.6525NANANA0.6839
36NA111-52.2497274.2497NANANA0.8096
37NA103-67.5087273.5087NANANA0.7725
38NA97-80.4711274.4711NANANA0.7427
39NA93-91.1705277.1705NANANA0.7208
40NA114-76.6346304.6346NANANA0.7827







Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPEsMAPESq.EMSERMSEScaledEMASE
170.3139NANANANA00NANA
180.4673NANANANANANANANA
190.5437NANANANANANANANA
200.4924NANANANANANANANA
210.6104NANANANANANANANA
220.6835NANANANANANANANA
230.8742NANANANANANANANA
240.4865NANANANANANANANA
250.5795NANANANANANANANA
260.673NANANANANANANANA
270.7572NANANANANANANANA
280.5723NANANANANANANANA
290.6439NANANANANANANANA
300.7389NANANANANANANANA
310.7886NANANANANANANANA
320.7466NANANANANANANANA
330.8456NANANANANANANANA
340.9077NANANANANANANANA
351.0449NANANANANANANANA
360.7504NANANANANANANANA
370.8446NANANANANANANANA
380.9335NANANANANANANANA
391.0104NANANANANANANANA
400.8532NANANANANANANANA

\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.PEMAPEsMAPESq.EMSERMSEScaledEMASE
170.3139NANANANA00NANA
180.4673NANANANANANANANA
190.5437NANANANANANANANA
200.4924NANANANANANANANA
210.6104NANANANANANANANA
220.6835NANANANANANANANA
230.8742NANANANANANANANA
240.4865NANANANANANANANA
250.5795NANANANANANANANA
260.673NANANANANANANANA
270.7572NANANANANANANANA
280.5723NANANANANANANANA
290.6439NANANANANANANANA
300.7389NANANANANANANANA
310.7886NANANANANANANANA
320.7466NANANANANANANANA
330.8456NANANANANANANANA
340.9077NANANANANANANANA
351.0449NANANANANANANANA
360.7504NANANANANANANANA
370.8446NANANANANANANANA
380.9335NANANANANANANANA
391.0104NANANANANANANANA
400.8532NANANANANANANANA



Parameters (Session):
par1 = 0 ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 0 ; par7 = 0 ; par8 = 0 ; par9 = 0 ; par10 = FALSE ;
Parameters (R input):
par1 = 0 ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 0 ; par7 = 0 ; par8 = 0 ; par9 = 0 ; par10 = FALSE ;
R code (references can be found in the software module):
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 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*2
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,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 <- 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.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 <- 0
for (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$pred[i]) / 1.96
perf.scalederr[i] = (x[nx+i] - forecast$pred[i]) / perf.scaleddenom
perf.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])^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.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] / i
perf.smape[i] = perf.smape[i-1] + abs(perf.spe[i])
perf.smape1[i] = perf.smape[i] / i
perf.mse[i] = perf.mse[i-1] + perf.se[i]
perf.mse1[i] = perf.mse[i] / i
perf.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$pred, pch=19, type='b',main='ARIMA Extrapolation Forecast', ylab='Forecast and 95% CI', xlab='time',ylim=c(min(lb),max(ub)))
dum <- forecast$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',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')