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Author

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R Software Module

rwasp_arimaforecasting.wasp

Title produced by software

ARIMA Forecasting

Date of computation

Tue, 06 Dec 2011 18:42:39 -0500

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/06/t1323214979cggxifptoef18e0.htm/, Retrieved Mon, 29 Apr 2024 03:16:38 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=152045, Retrieved Mon, 29 Apr 2024 03:16:38 +0000

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IsPrivate?

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User-defined keywords

Estimated Impact

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]
-   PD      [ARIMA Forecasting] [WS9 - ARIMA Forec...] [2010-12-04 16:25:58] [8ef49741e164ec6343c90c7935194465]
-   P         [ARIMA Forecasting] [WS9 - ARIMA Forec...] [2010-12-04 16:58:46] [8ef49741e164ec6343c90c7935194465]
- R PD          [ARIMA Forecasting] [WS9 - ARIMA Forec...] [2010-12-07 11:26:44] [1f5baf2b24e732d76900bb8178fc04e7]
-                 [ARIMA Forecasting] [WS9 - ARIMA Forec...] [2010-12-07 11:48:16] [1f5baf2b24e732d76900bb8178fc04e7]
- R P                 [ARIMA Forecasting] [Arima Forecasting 24] [2011-12-06 23:42:39] [0f9b7c3b8d01420b2751adc6f98a35df] [Current]

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Dataseries X:

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Histogram

Boxplots

2.4
2.4
2.5
2.6
2.4
2.6
2.4
2.3
2.4
2.4
2.4
2.4
2.4
2.4
2.4
2.4
2.5
2.1
2.1
2
2
2
1.9
1.9
2
1.8
1.6
1.3
1.4
1.4
1.5
1.7
1.6
1.5
1.6
1.5
1.1
1.1
1.1
1.4
1.3
1.4
1.3
1.1
1
0.9
0.8
0.8
0.8
0.8
1
1.1
1
0.9
1.1
1.2
1.2
1.4
1.5
1.7
1.9
1.9
1.9
1.7
1.7
2.1
2
2
2.5
2.4
2.5
2.5
2
1.9
2.2
2.7
3.1
2.8
2.6
2.3
2.2
2.2
2
2
2.6
2.5
2.5
2.3
2
1.9
2
2.1
2.1
2.3
2.3
2.3
2.1
2.4
2.5
2.1
1.8
1.9
1.9
2.1
2.2
2
2.2
2
1.9
1.6
1.7
2
2.5
2.4
2.3
2.3
2.1
2.4
2.2
2.4
1.9
2.1
2.1
2.1
2
2.1
2.2
2.2
2.6
2.5
2.3
2.2
2.4
2.3
2.2
2.5
2.5
2.5
2.4
2.3
1.7
1.6
1.9
1.9
1.8
1.8
1.9
1.9
1.9
1.9
1.8
1.7
2.1
2.6
3.1
3.1
3.2
3.3
3.6
3.3
3.7
4
4
3.8
3.6
3.2
2.1
1.6
1.1
1.2
0.6
0.6
0
-0.1
-0.6
-0.2
-0.3
-0.1
0.5
0.9

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

\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' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152045&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' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152045&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152045&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	2 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

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[156])
144	1.9	-	-	-	-	-	-	-
145	1.8	-	-	-	-	-	-	-
146	1.8	-	-	-	-	-	-	-
147	1.9	-	-	-	-	-	-	-
148	1.9	-	-	-	-	-	-	-
149	1.9	-	-	-	-	-	-	-
150	1.9	-	-	-	-	-	-	-
151	1.8	-	-	-	-	-	-	-
152	1.7	-	-	-	-	-	-	-
153	2.1	-	-	-	-	-	-	-
154	2.6	-	-	-	-	-	-	-
155	3.1	-	-	-	-	-	-	-
156	3.1	-	-	-	-	-	-	-
157	3.2	2.9929	2.6569	3.329	0.1136	0.2661	1	0.2661
158	3.3	2.9521	2.4429	3.4612	0.0902	0.1699	1	0.2845
159	3.6	2.8972	2.2802	3.5141	0.0128	0.1003	0.9992	0.2597
160	3.3	2.8088	2.1158	3.5018	0.0824	0.0126	0.9949	0.2051
161	3.7	2.8108	2.0557	3.566	0.0105	0.1021	0.991	0.2265
162	4	2.8131	2.0023	3.624	0.0021	0.016	0.9864	0.244
163	4	2.8989	2.0359	3.7619	0.0062	0.0062	0.9937	0.3239
164	3.8	2.9838	2.0714	3.8963	0.0398	0.0145	0.9971	0.4015
165	3.6	2.9251	1.9656	3.8847	0.084	0.037	0.9541	0.3605
166	3.2	2.67	1.6655	3.6745	0.1505	0.0348	0.5543	0.2007
167	2.1	2.303	1.2554	3.3505	0.3521	0.0466	0.0679	0.0679
168	1.6	2.303	1.2141	3.3919	0.1029	0.6426	0.0757	0.0757
169	1.1	2.3915	1.2974	3.4857	0.0103	0.9219	0.0738	0.1022
170	1.2	2.4147	1.3175	3.5118	0.015	0.9906	0.0569	0.1104
171	0.6	2.4178	1.3161	3.5194	6e-04	0.9849	0.0177	0.1124
172	0.6	2.4678	1.3598	3.5758	5e-04	0.9995	0.0705	0.1317
173	0	2.4667	1.3514	3.582	0	0.9995	0.0151	0.1329
174	-0.1	2.4654	1.3425	3.5882	0	1	0.0037	0.134
175	-0.6	2.4448	1.3144	3.5751	0	1	0.0035	0.1279
176	-0.2	2.4246	1.2869	3.5624	0	1	0.0089	0.1223
177	-0.3	2.346	1.201	3.4911	0	1	0.0159	0.0984
178	-0.1	2.3507	1.1985	3.503	0	1	0.0743	0.1012
179	0.5	2.4187	1.2593	3.5782	6e-04	1	0.705	0.1247
180	0.9	2.4187	1.2521	3.5853	0.0054	0.9994	0.9155	0.1262

\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[156]) \tabularnewline
144 & 1.9 & - & - & - & - & - & - & - \tabularnewline
145 & 1.8 & - & - & - & - & - & - & - \tabularnewline
146 & 1.8 & - & - & - & - & - & - & - \tabularnewline
147 & 1.9 & - & - & - & - & - & - & - \tabularnewline
148 & 1.9 & - & - & - & - & - & - & - \tabularnewline
149 & 1.9 & - & - & - & - & - & - & - \tabularnewline
150 & 1.9 & - & - & - & - & - & - & - \tabularnewline
151 & 1.8 & - & - & - & - & - & - & - \tabularnewline
152 & 1.7 & - & - & - & - & - & - & - \tabularnewline
153 & 2.1 & - & - & - & - & - & - & - \tabularnewline
154 & 2.6 & - & - & - & - & - & - & - \tabularnewline
155 & 3.1 & - & - & - & - & - & - & - \tabularnewline
156 & 3.1 & - & - & - & - & - & - & - \tabularnewline
157 & 3.2 & 2.9929 & 2.6569 & 3.329 & 0.1136 & 0.2661 & 1 & 0.2661 \tabularnewline
158 & 3.3 & 2.9521 & 2.4429 & 3.4612 & 0.0902 & 0.1699 & 1 & 0.2845 \tabularnewline
159 & 3.6 & 2.8972 & 2.2802 & 3.5141 & 0.0128 & 0.1003 & 0.9992 & 0.2597 \tabularnewline
160 & 3.3 & 2.8088 & 2.1158 & 3.5018 & 0.0824 & 0.0126 & 0.9949 & 0.2051 \tabularnewline
161 & 3.7 & 2.8108 & 2.0557 & 3.566 & 0.0105 & 0.1021 & 0.991 & 0.2265 \tabularnewline
162 & 4 & 2.8131 & 2.0023 & 3.624 & 0.0021 & 0.016 & 0.9864 & 0.244 \tabularnewline
163 & 4 & 2.8989 & 2.0359 & 3.7619 & 0.0062 & 0.0062 & 0.9937 & 0.3239 \tabularnewline
164 & 3.8 & 2.9838 & 2.0714 & 3.8963 & 0.0398 & 0.0145 & 0.9971 & 0.4015 \tabularnewline
165 & 3.6 & 2.9251 & 1.9656 & 3.8847 & 0.084 & 0.037 & 0.9541 & 0.3605 \tabularnewline
166 & 3.2 & 2.67 & 1.6655 & 3.6745 & 0.1505 & 0.0348 & 0.5543 & 0.2007 \tabularnewline
167 & 2.1 & 2.303 & 1.2554 & 3.3505 & 0.3521 & 0.0466 & 0.0679 & 0.0679 \tabularnewline
168 & 1.6 & 2.303 & 1.2141 & 3.3919 & 0.1029 & 0.6426 & 0.0757 & 0.0757 \tabularnewline
169 & 1.1 & 2.3915 & 1.2974 & 3.4857 & 0.0103 & 0.9219 & 0.0738 & 0.1022 \tabularnewline
170 & 1.2 & 2.4147 & 1.3175 & 3.5118 & 0.015 & 0.9906 & 0.0569 & 0.1104 \tabularnewline
171 & 0.6 & 2.4178 & 1.3161 & 3.5194 & 6e-04 & 0.9849 & 0.0177 & 0.1124 \tabularnewline
172 & 0.6 & 2.4678 & 1.3598 & 3.5758 & 5e-04 & 0.9995 & 0.0705 & 0.1317 \tabularnewline
173 & 0 & 2.4667 & 1.3514 & 3.582 & 0 & 0.9995 & 0.0151 & 0.1329 \tabularnewline
174 & -0.1 & 2.4654 & 1.3425 & 3.5882 & 0 & 1 & 0.0037 & 0.134 \tabularnewline
175 & -0.6 & 2.4448 & 1.3144 & 3.5751 & 0 & 1 & 0.0035 & 0.1279 \tabularnewline
176 & -0.2 & 2.4246 & 1.2869 & 3.5624 & 0 & 1 & 0.0089 & 0.1223 \tabularnewline
177 & -0.3 & 2.346 & 1.201 & 3.4911 & 0 & 1 & 0.0159 & 0.0984 \tabularnewline
178 & -0.1 & 2.3507 & 1.1985 & 3.503 & 0 & 1 & 0.0743 & 0.1012 \tabularnewline
179 & 0.5 & 2.4187 & 1.2593 & 3.5782 & 6e-04 & 1 & 0.705 & 0.1247 \tabularnewline
180 & 0.9 & 2.4187 & 1.2521 & 3.5853 & 0.0054 & 0.9994 & 0.9155 & 0.1262 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152045&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[156])[/C][/ROW]
[ROW][C]144[/C][C]1.9[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]145[/C][C]1.8[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]146[/C][C]1.8[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]147[/C][C]1.9[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]148[/C][C]1.9[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]149[/C][C]1.9[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]150[/C][C]1.9[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]151[/C][C]1.8[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]152[/C][C]1.7[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]153[/C][C]2.1[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]154[/C][C]2.6[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]155[/C][C]3.1[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]156[/C][C]3.1[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]157[/C][C]3.2[/C][C]2.9929[/C][C]2.6569[/C][C]3.329[/C][C]0.1136[/C][C]0.2661[/C][C]1[/C][C]0.2661[/C][/ROW]
[ROW][C]158[/C][C]3.3[/C][C]2.9521[/C][C]2.4429[/C][C]3.4612[/C][C]0.0902[/C][C]0.1699[/C][C]1[/C][C]0.2845[/C][/ROW]
[ROW][C]159[/C][C]3.6[/C][C]2.8972[/C][C]2.2802[/C][C]3.5141[/C][C]0.0128[/C][C]0.1003[/C][C]0.9992[/C][C]0.2597[/C][/ROW]
[ROW][C]160[/C][C]3.3[/C][C]2.8088[/C][C]2.1158[/C][C]3.5018[/C][C]0.0824[/C][C]0.0126[/C][C]0.9949[/C][C]0.2051[/C][/ROW]
[ROW][C]161[/C][C]3.7[/C][C]2.8108[/C][C]2.0557[/C][C]3.566[/C][C]0.0105[/C][C]0.1021[/C][C]0.991[/C][C]0.2265[/C][/ROW]
[ROW][C]162[/C][C]4[/C][C]2.8131[/C][C]2.0023[/C][C]3.624[/C][C]0.0021[/C][C]0.016[/C][C]0.9864[/C][C]0.244[/C][/ROW]
[ROW][C]163[/C][C]4[/C][C]2.8989[/C][C]2.0359[/C][C]3.7619[/C][C]0.0062[/C][C]0.0062[/C][C]0.9937[/C][C]0.3239[/C][/ROW]
[ROW][C]164[/C][C]3.8[/C][C]2.9838[/C][C]2.0714[/C][C]3.8963[/C][C]0.0398[/C][C]0.0145[/C][C]0.9971[/C][C]0.4015[/C][/ROW]
[ROW][C]165[/C][C]3.6[/C][C]2.9251[/C][C]1.9656[/C][C]3.8847[/C][C]0.084[/C][C]0.037[/C][C]0.9541[/C][C]0.3605[/C][/ROW]
[ROW][C]166[/C][C]3.2[/C][C]2.67[/C][C]1.6655[/C][C]3.6745[/C][C]0.1505[/C][C]0.0348[/C][C]0.5543[/C][C]0.2007[/C][/ROW]
[ROW][C]167[/C][C]2.1[/C][C]2.303[/C][C]1.2554[/C][C]3.3505[/C][C]0.3521[/C][C]0.0466[/C][C]0.0679[/C][C]0.0679[/C][/ROW]
[ROW][C]168[/C][C]1.6[/C][C]2.303[/C][C]1.2141[/C][C]3.3919[/C][C]0.1029[/C][C]0.6426[/C][C]0.0757[/C][C]0.0757[/C][/ROW]
[ROW][C]169[/C][C]1.1[/C][C]2.3915[/C][C]1.2974[/C][C]3.4857[/C][C]0.0103[/C][C]0.9219[/C][C]0.0738[/C][C]0.1022[/C][/ROW]
[ROW][C]170[/C][C]1.2[/C][C]2.4147[/C][C]1.3175[/C][C]3.5118[/C][C]0.015[/C][C]0.9906[/C][C]0.0569[/C][C]0.1104[/C][/ROW]
[ROW][C]171[/C][C]0.6[/C][C]2.4178[/C][C]1.3161[/C][C]3.5194[/C][C]6e-04[/C][C]0.9849[/C][C]0.0177[/C][C]0.1124[/C][/ROW]
[ROW][C]172[/C][C]0.6[/C][C]2.4678[/C][C]1.3598[/C][C]3.5758[/C][C]5e-04[/C][C]0.9995[/C][C]0.0705[/C][C]0.1317[/C][/ROW]
[ROW][C]173[/C][C]0[/C][C]2.4667[/C][C]1.3514[/C][C]3.582[/C][C]0[/C][C]0.9995[/C][C]0.0151[/C][C]0.1329[/C][/ROW]
[ROW][C]174[/C][C]-0.1[/C][C]2.4654[/C][C]1.3425[/C][C]3.5882[/C][C]0[/C][C]1[/C][C]0.0037[/C][C]0.134[/C][/ROW]
[ROW][C]175[/C][C]-0.6[/C][C]2.4448[/C][C]1.3144[/C][C]3.5751[/C][C]0[/C][C]1[/C][C]0.0035[/C][C]0.1279[/C][/ROW]
[ROW][C]176[/C][C]-0.2[/C][C]2.4246[/C][C]1.2869[/C][C]3.5624[/C][C]0[/C][C]1[/C][C]0.0089[/C][C]0.1223[/C][/ROW]
[ROW][C]177[/C][C]-0.3[/C][C]2.346[/C][C]1.201[/C][C]3.4911[/C][C]0[/C][C]1[/C][C]0.0159[/C][C]0.0984[/C][/ROW]
[ROW][C]178[/C][C]-0.1[/C][C]2.3507[/C][C]1.1985[/C][C]3.503[/C][C]0[/C][C]1[/C][C]0.0743[/C][C]0.1012[/C][/ROW]
[ROW][C]179[/C][C]0.5[/C][C]2.4187[/C][C]1.2593[/C][C]3.5782[/C][C]6e-04[/C][C]1[/C][C]0.705[/C][C]0.1247[/C][/ROW]
[ROW][C]180[/C][C]0.9[/C][C]2.4187[/C][C]1.2521[/C][C]3.5853[/C][C]0.0054[/C][C]0.9994[/C][C]0.9155[/C][C]0.1262[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152045&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152045&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[156])
144	1.9	-	-	-	-	-	-	-
145	1.8	-	-	-	-	-	-	-
146	1.8	-	-	-	-	-	-	-
147	1.9	-	-	-	-	-	-	-
148	1.9	-	-	-	-	-	-	-
149	1.9	-	-	-	-	-	-	-
150	1.9	-	-	-	-	-	-	-
151	1.8	-	-	-	-	-	-	-
152	1.7	-	-	-	-	-	-	-
153	2.1	-	-	-	-	-	-	-
154	2.6	-	-	-	-	-	-	-
155	3.1	-	-	-	-	-	-	-
156	3.1	-	-	-	-	-	-	-
157	3.2	2.9929	2.6569	3.329	0.1136	0.2661	1	0.2661
158	3.3	2.9521	2.4429	3.4612	0.0902	0.1699	1	0.2845
159	3.6	2.8972	2.2802	3.5141	0.0128	0.1003	0.9992	0.2597
160	3.3	2.8088	2.1158	3.5018	0.0824	0.0126	0.9949	0.2051
161	3.7	2.8108	2.0557	3.566	0.0105	0.1021	0.991	0.2265
162	4	2.8131	2.0023	3.624	0.0021	0.016	0.9864	0.244
163	4	2.8989	2.0359	3.7619	0.0062	0.0062	0.9937	0.3239
164	3.8	2.9838	2.0714	3.8963	0.0398	0.0145	0.9971	0.4015
165	3.6	2.9251	1.9656	3.8847	0.084	0.037	0.9541	0.3605
166	3.2	2.67	1.6655	3.6745	0.1505	0.0348	0.5543	0.2007
167	2.1	2.303	1.2554	3.3505	0.3521	0.0466	0.0679	0.0679
168	1.6	2.303	1.2141	3.3919	0.1029	0.6426	0.0757	0.0757
169	1.1	2.3915	1.2974	3.4857	0.0103	0.9219	0.0738	0.1022
170	1.2	2.4147	1.3175	3.5118	0.015	0.9906	0.0569	0.1104
171	0.6	2.4178	1.3161	3.5194	6e-04	0.9849	0.0177	0.1124
172	0.6	2.4678	1.3598	3.5758	5e-04	0.9995	0.0705	0.1317
173	0	2.4667	1.3514	3.582	0	0.9995	0.0151	0.1329
174	-0.1	2.4654	1.3425	3.5882	0	1	0.0037	0.134
175	-0.6	2.4448	1.3144	3.5751	0	1	0.0035	0.1279
176	-0.2	2.4246	1.2869	3.5624	0	1	0.0089	0.1223
177	-0.3	2.346	1.201	3.4911	0	1	0.0159	0.0984
178	-0.1	2.3507	1.1985	3.503	0	1	0.0743	0.1012
179	0.5	2.4187	1.2593	3.5782	6e-04	1	0.705	0.1247
180	0.9	2.4187	1.2521	3.5853	0.0054	0.9994	0.9155	0.1262

Univariate ARIMA Extrapolation Forecast Performance
time	% S.E.	PE	MAPE	Sq.E	MSE	RMSE
157	0.0573	0.0692	0	0.0429	0	0
158	0.088	0.1179	0.0935	0.121	0.082	0.2863
159	0.1086	0.2426	0.1432	0.4939	0.2193	0.4683
160	0.1259	0.1749	0.1511	0.2413	0.2248	0.4741
161	0.1371	0.3163	0.1842	0.7906	0.3379	0.5813
162	0.1471	0.4219	0.2238	1.4086	0.5164	0.7186
163	0.1519	0.3798	0.2461	1.2124	0.6158	0.7847
164	0.156	0.2735	0.2495	0.6661	0.6221	0.7887
165	0.1674	0.2307	0.2474	0.4554	0.6036	0.7769
166	0.192	0.1985	0.2425	0.2809	0.5713	0.7559
167	0.2321	-0.0881	0.2285	0.0412	0.5231	0.7233
168	0.2412	-0.3052	0.2349	0.4942	0.5207	0.7216
169	0.2334	-0.54	0.2584	1.668	0.609	0.7804
170	0.2318	-0.503	0.2758	1.4754	0.6709	0.8191
171	0.2325	-0.7518	0.3076	3.3043	0.8464	0.92
172	0.2291	-0.7569	0.3357	3.4888	1.0116	1.0058
173	0.2307	-1	0.3747	6.0844	1.31	1.1445
174	0.2324	-1.0406	0.4117	6.5811	1.6028	1.266
175	0.2359	-1.2454	0.4556	9.2706	2.0064	1.4165
176	0.2394	-1.0825	0.4869	6.8887	2.2505	1.5002
177	0.249	-1.1279	0.5175	7.0016	2.4767	1.5738
178	0.2501	-1.0425	0.5413	6.0061	2.6372	1.6239
179	0.2446	-0.7933	0.5523	3.6815	2.6826	1.6379
180	0.2461	-0.6279	0.5554	2.3065	2.6669	1.6331

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
157 & 0.0573 & 0.0692 & 0 & 0.0429 & 0 & 0 \tabularnewline
158 & 0.088 & 0.1179 & 0.0935 & 0.121 & 0.082 & 0.2863 \tabularnewline
159 & 0.1086 & 0.2426 & 0.1432 & 0.4939 & 0.2193 & 0.4683 \tabularnewline
160 & 0.1259 & 0.1749 & 0.1511 & 0.2413 & 0.2248 & 0.4741 \tabularnewline
161 & 0.1371 & 0.3163 & 0.1842 & 0.7906 & 0.3379 & 0.5813 \tabularnewline
162 & 0.1471 & 0.4219 & 0.2238 & 1.4086 & 0.5164 & 0.7186 \tabularnewline
163 & 0.1519 & 0.3798 & 0.2461 & 1.2124 & 0.6158 & 0.7847 \tabularnewline
164 & 0.156 & 0.2735 & 0.2495 & 0.6661 & 0.6221 & 0.7887 \tabularnewline
165 & 0.1674 & 0.2307 & 0.2474 & 0.4554 & 0.6036 & 0.7769 \tabularnewline
166 & 0.192 & 0.1985 & 0.2425 & 0.2809 & 0.5713 & 0.7559 \tabularnewline
167 & 0.2321 & -0.0881 & 0.2285 & 0.0412 & 0.5231 & 0.7233 \tabularnewline
168 & 0.2412 & -0.3052 & 0.2349 & 0.4942 & 0.5207 & 0.7216 \tabularnewline
169 & 0.2334 & -0.54 & 0.2584 & 1.668 & 0.609 & 0.7804 \tabularnewline
170 & 0.2318 & -0.503 & 0.2758 & 1.4754 & 0.6709 & 0.8191 \tabularnewline
171 & 0.2325 & -0.7518 & 0.3076 & 3.3043 & 0.8464 & 0.92 \tabularnewline
172 & 0.2291 & -0.7569 & 0.3357 & 3.4888 & 1.0116 & 1.0058 \tabularnewline
173 & 0.2307 & -1 & 0.3747 & 6.0844 & 1.31 & 1.1445 \tabularnewline
174 & 0.2324 & -1.0406 & 0.4117 & 6.5811 & 1.6028 & 1.266 \tabularnewline
175 & 0.2359 & -1.2454 & 0.4556 & 9.2706 & 2.0064 & 1.4165 \tabularnewline
176 & 0.2394 & -1.0825 & 0.4869 & 6.8887 & 2.2505 & 1.5002 \tabularnewline
177 & 0.249 & -1.1279 & 0.5175 & 7.0016 & 2.4767 & 1.5738 \tabularnewline
178 & 0.2501 & -1.0425 & 0.5413 & 6.0061 & 2.6372 & 1.6239 \tabularnewline
179 & 0.2446 & -0.7933 & 0.5523 & 3.6815 & 2.6826 & 1.6379 \tabularnewline
180 & 0.2461 & -0.6279 & 0.5554 & 2.3065 & 2.6669 & 1.6331 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152045&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]157[/C][C]0.0573[/C][C]0.0692[/C][C]0[/C][C]0.0429[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]158[/C][C]0.088[/C][C]0.1179[/C][C]0.0935[/C][C]0.121[/C][C]0.082[/C][C]0.2863[/C][/ROW]
[ROW][C]159[/C][C]0.1086[/C][C]0.2426[/C][C]0.1432[/C][C]0.4939[/C][C]0.2193[/C][C]0.4683[/C][/ROW]
[ROW][C]160[/C][C]0.1259[/C][C]0.1749[/C][C]0.1511[/C][C]0.2413[/C][C]0.2248[/C][C]0.4741[/C][/ROW]
[ROW][C]161[/C][C]0.1371[/C][C]0.3163[/C][C]0.1842[/C][C]0.7906[/C][C]0.3379[/C][C]0.5813[/C][/ROW]
[ROW][C]162[/C][C]0.1471[/C][C]0.4219[/C][C]0.2238[/C][C]1.4086[/C][C]0.5164[/C][C]0.7186[/C][/ROW]
[ROW][C]163[/C][C]0.1519[/C][C]0.3798[/C][C]0.2461[/C][C]1.2124[/C][C]0.6158[/C][C]0.7847[/C][/ROW]
[ROW][C]164[/C][C]0.156[/C][C]0.2735[/C][C]0.2495[/C][C]0.6661[/C][C]0.6221[/C][C]0.7887[/C][/ROW]
[ROW][C]165[/C][C]0.1674[/C][C]0.2307[/C][C]0.2474[/C][C]0.4554[/C][C]0.6036[/C][C]0.7769[/C][/ROW]
[ROW][C]166[/C][C]0.192[/C][C]0.1985[/C][C]0.2425[/C][C]0.2809[/C][C]0.5713[/C][C]0.7559[/C][/ROW]
[ROW][C]167[/C][C]0.2321[/C][C]-0.0881[/C][C]0.2285[/C][C]0.0412[/C][C]0.5231[/C][C]0.7233[/C][/ROW]
[ROW][C]168[/C][C]0.2412[/C][C]-0.3052[/C][C]0.2349[/C][C]0.4942[/C][C]0.5207[/C][C]0.7216[/C][/ROW]
[ROW][C]169[/C][C]0.2334[/C][C]-0.54[/C][C]0.2584[/C][C]1.668[/C][C]0.609[/C][C]0.7804[/C][/ROW]
[ROW][C]170[/C][C]0.2318[/C][C]-0.503[/C][C]0.2758[/C][C]1.4754[/C][C]0.6709[/C][C]0.8191[/C][/ROW]
[ROW][C]171[/C][C]0.2325[/C][C]-0.7518[/C][C]0.3076[/C][C]3.3043[/C][C]0.8464[/C][C]0.92[/C][/ROW]
[ROW][C]172[/C][C]0.2291[/C][C]-0.7569[/C][C]0.3357[/C][C]3.4888[/C][C]1.0116[/C][C]1.0058[/C][/ROW]
[ROW][C]173[/C][C]0.2307[/C][C]-1[/C][C]0.3747[/C][C]6.0844[/C][C]1.31[/C][C]1.1445[/C][/ROW]
[ROW][C]174[/C][C]0.2324[/C][C]-1.0406[/C][C]0.4117[/C][C]6.5811[/C][C]1.6028[/C][C]1.266[/C][/ROW]
[ROW][C]175[/C][C]0.2359[/C][C]-1.2454[/C][C]0.4556[/C][C]9.2706[/C][C]2.0064[/C][C]1.4165[/C][/ROW]
[ROW][C]176[/C][C]0.2394[/C][C]-1.0825[/C][C]0.4869[/C][C]6.8887[/C][C]2.2505[/C][C]1.5002[/C][/ROW]
[ROW][C]177[/C][C]0.249[/C][C]-1.1279[/C][C]0.5175[/C][C]7.0016[/C][C]2.4767[/C][C]1.5738[/C][/ROW]
[ROW][C]178[/C][C]0.2501[/C][C]-1.0425[/C][C]0.5413[/C][C]6.0061[/C][C]2.6372[/C][C]1.6239[/C][/ROW]
[ROW][C]179[/C][C]0.2446[/C][C]-0.7933[/C][C]0.5523[/C][C]3.6815[/C][C]2.6826[/C][C]1.6379[/C][/ROW]
[ROW][C]180[/C][C]0.2461[/C][C]-0.6279[/C][C]0.5554[/C][C]2.3065[/C][C]2.6669[/C][C]1.6331[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152045&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152045&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	Sq.E	MSE	RMSE
157	0.0573	0.0692	0	0.0429	0	0
158	0.088	0.1179	0.0935	0.121	0.082	0.2863
159	0.1086	0.2426	0.1432	0.4939	0.2193	0.4683
160	0.1259	0.1749	0.1511	0.2413	0.2248	0.4741
161	0.1371	0.3163	0.1842	0.7906	0.3379	0.5813
162	0.1471	0.4219	0.2238	1.4086	0.5164	0.7186
163	0.1519	0.3798	0.2461	1.2124	0.6158	0.7847
164	0.156	0.2735	0.2495	0.6661	0.6221	0.7887
165	0.1674	0.2307	0.2474	0.4554	0.6036	0.7769
166	0.192	0.1985	0.2425	0.2809	0.5713	0.7559
167	0.2321	-0.0881	0.2285	0.0412	0.5231	0.7233
168	0.2412	-0.3052	0.2349	0.4942	0.5207	0.7216
169	0.2334	-0.54	0.2584	1.668	0.609	0.7804
170	0.2318	-0.503	0.2758	1.4754	0.6709	0.8191
171	0.2325	-0.7518	0.3076	3.3043	0.8464	0.92
172	0.2291	-0.7569	0.3357	3.4888	1.0116	1.0058
173	0.2307	-1	0.3747	6.0844	1.31	1.1445
174	0.2324	-1.0406	0.4117	6.5811	1.6028	1.266
175	0.2359	-1.2454	0.4556	9.2706	2.0064	1.4165
176	0.2394	-1.0825	0.4869	6.8887	2.2505	1.5002
177	0.249	-1.1279	0.5175	7.0016	2.4767	1.5738
178	0.2501	-1.0425	0.5413	6.0061	2.6372	1.6239
179	0.2446	-0.7933	0.5523	3.6815	2.6826	1.6379
180	0.2461	-0.6279	0.5554	2.3065	2.6669	1.6331

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;

Parameters (R input):

par1 = 24 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 2 ; par7 = 1 ; par8 = 2 ; 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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code