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R Software Module

rwasp_arimaforecasting.wasp

Title produced by software

ARIMA Forecasting

Date of computation

Tue, 07 Dec 2010 11:50:46 +0000

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/2010/Dec/07/t1291722574b1pzo7e06bhgutf.htm/, Retrieved Sat, 04 May 2024 01:52:47 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=106200, Retrieved Sat, 04 May 2024 01:52:47 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

119

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 11:50:46] [ee4a783fb13f41eb2e9bc8a0c4f26279] [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	12 seconds
R Server	'George Udny Yule' @ 72.249.76.132

\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 & 12 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=106200&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]12 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=106200&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=106200&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	12 seconds
R Server	'George Udny Yule' @ 72.249.76.132

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[167])
155	3.1	-	-	-	-	-	-	-
156	3.1	-	-	-	-	-	-	-
157	3.2	-	-	-	-	-	-	-
158	3.3	-	-	-	-	-	-	-
159	3.6	-	-	-	-	-	-	-
160	3.3	-	-	-	-	-	-	-
161	3.7	-	-	-	-	-	-	-
162	4	-	-	-	-	-	-	-
163	4	-	-	-	-	-	-	-
164	3.8	-	-	-	-	-	-	-
165	3.6	-	-	-	-	-	-	-
166	3.2	-	-	-	-	-	-	-
167	2.1	-	-	-	-	-	-	-
168	1.6	2.0108	1.652	2.3696	0.0124	0.3131	0	0.3131
169	1.1	2.0026	1.4557	2.5495	6e-04	0.9255	0	0.3635
170	1.2	1.962	1.2815	2.6426	0.0141	0.9935	1e-04	0.3455
171	0.6	1.7436	0.9575	2.5297	0.0022	0.9124	0	0.1871
172	0.6	1.9463	1.0703	2.8223	0.0013	0.9987	0.0012	0.3655
173	0	1.6867	0.7304	2.643	3e-04	0.987	0	0.1985
174	-0.1	1.4911	0.4612	2.521	0.0012	0.9977	0	0.1233
175	-0.6	1.5272	0.4288	2.6256	1e-04	0.9982	0	0.1534
176	-0.2	1.6941	0.5313	2.857	7e-04	0.9999	2e-04	0.247
177	-0.3	1.6817	0.4578	2.9057	8e-04	0.9987	0.0011	0.2515
178	-0.1	1.7645	0.4824	3.0466	0.0022	0.9992	0.0141	0.304
179	0.5	2.3058	0.9681	3.6435	0.0041	0.9998	0.6185	0.6185
180	0.9	2.3642	1.0185	3.7099	0.0165	0.9967	0.8671	0.6498

\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[167]) \tabularnewline
155 & 3.1 & - & - & - & - & - & - & - \tabularnewline
156 & 3.1 & - & - & - & - & - & - & - \tabularnewline
157 & 3.2 & - & - & - & - & - & - & - \tabularnewline
158 & 3.3 & - & - & - & - & - & - & - \tabularnewline
159 & 3.6 & - & - & - & - & - & - & - \tabularnewline
160 & 3.3 & - & - & - & - & - & - & - \tabularnewline
161 & 3.7 & - & - & - & - & - & - & - \tabularnewline
162 & 4 & - & - & - & - & - & - & - \tabularnewline
163 & 4 & - & - & - & - & - & - & - \tabularnewline
164 & 3.8 & - & - & - & - & - & - & - \tabularnewline
165 & 3.6 & - & - & - & - & - & - & - \tabularnewline
166 & 3.2 & - & - & - & - & - & - & - \tabularnewline
167 & 2.1 & - & - & - & - & - & - & - \tabularnewline
168 & 1.6 & 2.0108 & 1.652 & 2.3696 & 0.0124 & 0.3131 & 0 & 0.3131 \tabularnewline
169 & 1.1 & 2.0026 & 1.4557 & 2.5495 & 6e-04 & 0.9255 & 0 & 0.3635 \tabularnewline
170 & 1.2 & 1.962 & 1.2815 & 2.6426 & 0.0141 & 0.9935 & 1e-04 & 0.3455 \tabularnewline
171 & 0.6 & 1.7436 & 0.9575 & 2.5297 & 0.0022 & 0.9124 & 0 & 0.1871 \tabularnewline
172 & 0.6 & 1.9463 & 1.0703 & 2.8223 & 0.0013 & 0.9987 & 0.0012 & 0.3655 \tabularnewline
173 & 0 & 1.6867 & 0.7304 & 2.643 & 3e-04 & 0.987 & 0 & 0.1985 \tabularnewline
174 & -0.1 & 1.4911 & 0.4612 & 2.521 & 0.0012 & 0.9977 & 0 & 0.1233 \tabularnewline
175 & -0.6 & 1.5272 & 0.4288 & 2.6256 & 1e-04 & 0.9982 & 0 & 0.1534 \tabularnewline
176 & -0.2 & 1.6941 & 0.5313 & 2.857 & 7e-04 & 0.9999 & 2e-04 & 0.247 \tabularnewline
177 & -0.3 & 1.6817 & 0.4578 & 2.9057 & 8e-04 & 0.9987 & 0.0011 & 0.2515 \tabularnewline
178 & -0.1 & 1.7645 & 0.4824 & 3.0466 & 0.0022 & 0.9992 & 0.0141 & 0.304 \tabularnewline
179 & 0.5 & 2.3058 & 0.9681 & 3.6435 & 0.0041 & 0.9998 & 0.6185 & 0.6185 \tabularnewline
180 & 0.9 & 2.3642 & 1.0185 & 3.7099 & 0.0165 & 0.9967 & 0.8671 & 0.6498 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=106200&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[167])[/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]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]158[/C][C]3.3[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]159[/C][C]3.6[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]160[/C][C]3.3[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]161[/C][C]3.7[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]162[/C][C]4[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]163[/C][C]4[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]164[/C][C]3.8[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]165[/C][C]3.6[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]166[/C][C]3.2[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]167[/C][C]2.1[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]168[/C][C]1.6[/C][C]2.0108[/C][C]1.652[/C][C]2.3696[/C][C]0.0124[/C][C]0.3131[/C][C]0[/C][C]0.3131[/C][/ROW]
[ROW][C]169[/C][C]1.1[/C][C]2.0026[/C][C]1.4557[/C][C]2.5495[/C][C]6e-04[/C][C]0.9255[/C][C]0[/C][C]0.3635[/C][/ROW]
[ROW][C]170[/C][C]1.2[/C][C]1.962[/C][C]1.2815[/C][C]2.6426[/C][C]0.0141[/C][C]0.9935[/C][C]1e-04[/C][C]0.3455[/C][/ROW]
[ROW][C]171[/C][C]0.6[/C][C]1.7436[/C][C]0.9575[/C][C]2.5297[/C][C]0.0022[/C][C]0.9124[/C][C]0[/C][C]0.1871[/C][/ROW]
[ROW][C]172[/C][C]0.6[/C][C]1.9463[/C][C]1.0703[/C][C]2.8223[/C][C]0.0013[/C][C]0.9987[/C][C]0.0012[/C][C]0.3655[/C][/ROW]
[ROW][C]173[/C][C]0[/C][C]1.6867[/C][C]0.7304[/C][C]2.643[/C][C]3e-04[/C][C]0.987[/C][C]0[/C][C]0.1985[/C][/ROW]
[ROW][C]174[/C][C]-0.1[/C][C]1.4911[/C][C]0.4612[/C][C]2.521[/C][C]0.0012[/C][C]0.9977[/C][C]0[/C][C]0.1233[/C][/ROW]
[ROW][C]175[/C][C]-0.6[/C][C]1.5272[/C][C]0.4288[/C][C]2.6256[/C][C]1e-04[/C][C]0.9982[/C][C]0[/C][C]0.1534[/C][/ROW]
[ROW][C]176[/C][C]-0.2[/C][C]1.6941[/C][C]0.5313[/C][C]2.857[/C][C]7e-04[/C][C]0.9999[/C][C]2e-04[/C][C]0.247[/C][/ROW]
[ROW][C]177[/C][C]-0.3[/C][C]1.6817[/C][C]0.4578[/C][C]2.9057[/C][C]8e-04[/C][C]0.9987[/C][C]0.0011[/C][C]0.2515[/C][/ROW]
[ROW][C]178[/C][C]-0.1[/C][C]1.7645[/C][C]0.4824[/C][C]3.0466[/C][C]0.0022[/C][C]0.9992[/C][C]0.0141[/C][C]0.304[/C][/ROW]
[ROW][C]179[/C][C]0.5[/C][C]2.3058[/C][C]0.9681[/C][C]3.6435[/C][C]0.0041[/C][C]0.9998[/C][C]0.6185[/C][C]0.6185[/C][/ROW]
[ROW][C]180[/C][C]0.9[/C][C]2.3642[/C][C]1.0185[/C][C]3.7099[/C][C]0.0165[/C][C]0.9967[/C][C]0.8671[/C][C]0.6498[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=106200&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=106200&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[167])
155	3.1	-	-	-	-	-	-	-
156	3.1	-	-	-	-	-	-	-
157	3.2	-	-	-	-	-	-	-
158	3.3	-	-	-	-	-	-	-
159	3.6	-	-	-	-	-	-	-
160	3.3	-	-	-	-	-	-	-
161	3.7	-	-	-	-	-	-	-
162	4	-	-	-	-	-	-	-
163	4	-	-	-	-	-	-	-
164	3.8	-	-	-	-	-	-	-
165	3.6	-	-	-	-	-	-	-
166	3.2	-	-	-	-	-	-	-
167	2.1	-	-	-	-	-	-	-
168	1.6	2.0108	1.652	2.3696	0.0124	0.3131	0	0.3131
169	1.1	2.0026	1.4557	2.5495	6e-04	0.9255	0	0.3635
170	1.2	1.962	1.2815	2.6426	0.0141	0.9935	1e-04	0.3455
171	0.6	1.7436	0.9575	2.5297	0.0022	0.9124	0	0.1871
172	0.6	1.9463	1.0703	2.8223	0.0013	0.9987	0.0012	0.3655
173	0	1.6867	0.7304	2.643	3e-04	0.987	0	0.1985
174	-0.1	1.4911	0.4612	2.521	0.0012	0.9977	0	0.1233
175	-0.6	1.5272	0.4288	2.6256	1e-04	0.9982	0	0.1534
176	-0.2	1.6941	0.5313	2.857	7e-04	0.9999	2e-04	0.247
177	-0.3	1.6817	0.4578	2.9057	8e-04	0.9987	0.0011	0.2515
178	-0.1	1.7645	0.4824	3.0466	0.0022	0.9992	0.0141	0.304
179	0.5	2.3058	0.9681	3.6435	0.0041	0.9998	0.6185	0.6185
180	0.9	2.3642	1.0185	3.7099	0.0165	0.9967	0.8671	0.6498

Univariate ARIMA Extrapolation Forecast Performance
time	% S.E.	PE	MAPE	Sq.E	MSE	RMSE
168	0.091	-0.2043	0	0.1688	0	0
169	0.1393	-0.4507	0.3275	0.8146	0.4917	0.7012
170	0.177	-0.3884	0.3478	0.5807	0.5214	0.7221
171	0.23	-0.6559	0.4248	1.3078	0.718	0.8473
172	0.2296	-0.6917	0.4782	1.8126	0.9369	0.9679
173	0.2893	-1	0.5652	2.8451	1.2549	1.1202
174	0.3524	-1.0671	0.6369	2.5315	1.4373	1.1989
175	0.367	-1.3929	0.7314	4.525	1.8233	1.3503
176	0.3502	-1.1181	0.7743	3.5877	2.0193	1.421
177	0.3713	-1.1784	0.8147	3.9273	2.2101	1.4866
178	0.3707	-1.0567	0.8367	3.4764	2.3252	1.5249
179	0.296	-0.7832	0.8323	3.2608	2.4032	1.5502
180	0.2904	-0.6193	0.8159	2.1438	2.3832	1.5438

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
168 & 0.091 & -0.2043 & 0 & 0.1688 & 0 & 0 \tabularnewline
169 & 0.1393 & -0.4507 & 0.3275 & 0.8146 & 0.4917 & 0.7012 \tabularnewline
170 & 0.177 & -0.3884 & 0.3478 & 0.5807 & 0.5214 & 0.7221 \tabularnewline
171 & 0.23 & -0.6559 & 0.4248 & 1.3078 & 0.718 & 0.8473 \tabularnewline
172 & 0.2296 & -0.6917 & 0.4782 & 1.8126 & 0.9369 & 0.9679 \tabularnewline
173 & 0.2893 & -1 & 0.5652 & 2.8451 & 1.2549 & 1.1202 \tabularnewline
174 & 0.3524 & -1.0671 & 0.6369 & 2.5315 & 1.4373 & 1.1989 \tabularnewline
175 & 0.367 & -1.3929 & 0.7314 & 4.525 & 1.8233 & 1.3503 \tabularnewline
176 & 0.3502 & -1.1181 & 0.7743 & 3.5877 & 2.0193 & 1.421 \tabularnewline
177 & 0.3713 & -1.1784 & 0.8147 & 3.9273 & 2.2101 & 1.4866 \tabularnewline
178 & 0.3707 & -1.0567 & 0.8367 & 3.4764 & 2.3252 & 1.5249 \tabularnewline
179 & 0.296 & -0.7832 & 0.8323 & 3.2608 & 2.4032 & 1.5502 \tabularnewline
180 & 0.2904 & -0.6193 & 0.8159 & 2.1438 & 2.3832 & 1.5438 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=106200&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]168[/C][C]0.091[/C][C]-0.2043[/C][C]0[/C][C]0.1688[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]169[/C][C]0.1393[/C][C]-0.4507[/C][C]0.3275[/C][C]0.8146[/C][C]0.4917[/C][C]0.7012[/C][/ROW]
[ROW][C]170[/C][C]0.177[/C][C]-0.3884[/C][C]0.3478[/C][C]0.5807[/C][C]0.5214[/C][C]0.7221[/C][/ROW]
[ROW][C]171[/C][C]0.23[/C][C]-0.6559[/C][C]0.4248[/C][C]1.3078[/C][C]0.718[/C][C]0.8473[/C][/ROW]
[ROW][C]172[/C][C]0.2296[/C][C]-0.6917[/C][C]0.4782[/C][C]1.8126[/C][C]0.9369[/C][C]0.9679[/C][/ROW]
[ROW][C]173[/C][C]0.2893[/C][C]-1[/C][C]0.5652[/C][C]2.8451[/C][C]1.2549[/C][C]1.1202[/C][/ROW]
[ROW][C]174[/C][C]0.3524[/C][C]-1.0671[/C][C]0.6369[/C][C]2.5315[/C][C]1.4373[/C][C]1.1989[/C][/ROW]
[ROW][C]175[/C][C]0.367[/C][C]-1.3929[/C][C]0.7314[/C][C]4.525[/C][C]1.8233[/C][C]1.3503[/C][/ROW]
[ROW][C]176[/C][C]0.3502[/C][C]-1.1181[/C][C]0.7743[/C][C]3.5877[/C][C]2.0193[/C][C]1.421[/C][/ROW]
[ROW][C]177[/C][C]0.3713[/C][C]-1.1784[/C][C]0.8147[/C][C]3.9273[/C][C]2.2101[/C][C]1.4866[/C][/ROW]
[ROW][C]178[/C][C]0.3707[/C][C]-1.0567[/C][C]0.8367[/C][C]3.4764[/C][C]2.3252[/C][C]1.5249[/C][/ROW]
[ROW][C]179[/C][C]0.296[/C][C]-0.7832[/C][C]0.8323[/C][C]3.2608[/C][C]2.4032[/C][C]1.5502[/C][/ROW]
[ROW][C]180[/C][C]0.2904[/C][C]-0.6193[/C][C]0.8159[/C][C]2.1438[/C][C]2.3832[/C][C]1.5438[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=106200&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=106200&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
168	0.091	-0.2043	0	0.1688	0	0
169	0.1393	-0.4507	0.3275	0.8146	0.4917	0.7012
170	0.177	-0.3884	0.3478	0.5807	0.5214	0.7221
171	0.23	-0.6559	0.4248	1.3078	0.718	0.8473
172	0.2296	-0.6917	0.4782	1.8126	0.9369	0.9679
173	0.2893	-1	0.5652	2.8451	1.2549	1.1202
174	0.3524	-1.0671	0.6369	2.5315	1.4373	1.1989
175	0.367	-1.3929	0.7314	4.525	1.8233	1.3503
176	0.3502	-1.1181	0.7743	3.5877	2.0193	1.421
177	0.3713	-1.1784	0.8147	3.9273	2.2101	1.4866
178	0.3707	-1.0567	0.8367	3.4764	2.3252	1.5249
179	0.296	-0.7832	0.8323	3.2608	2.4032	1.5502
180	0.2904	-0.6193	0.8159	2.1438	2.3832	1.5438

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 24 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 2 ; par7 = 1 ; par8 = 2 ; par9 = 0 ; par10 = FALSE ;

Parameters (R input):

par1 = 13 ; 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