Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_arimaforecasting.wasp

Title produced by software

ARIMA Forecasting

Date of computation

Tue, 06 Dec 2011 18:30:39 -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/t1323214258b5rx2pje6rl9xro.htm/, Retrieved Mon, 29 Apr 2024 03:59:24 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=152041, Retrieved Mon, 29 Apr 2024 03:59:24 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

106

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 13] [2011-12-06 23:30:39] [0f9b7c3b8d01420b2751adc6f98a35df] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

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	3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152041&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]3 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=152041&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152041&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	3 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[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	2.087	1.7275	2.4464	0.004	0.4717	0	0.4717
169	1.1	2.064	1.5459	2.5822	1e-04	0.9604	0	0.4459
170	1.2	1.9879	1.3572	2.6186	0.0072	0.9971	0	0.3638
171	0.6	1.7645	1.0323	2.4968	9e-04	0.9346	0	0.1846
172	0.6	1.9448	1.1291	2.7605	6e-04	0.9994	6e-04	0.3546
173	0	1.6927	0.7967	2.5888	1e-04	0.9916	0	0.1865
174	-0.1	1.4902	0.5247	2.4557	6e-04	0.9988	0	0.1079
175	-0.6	1.5291	0.4952	2.563	0	0.999	0	0.1396
176	-0.2	1.6826	0.5881	2.7772	4e-04	1	1e-04	0.2274
177	-0.3	1.6691	0.5141	2.8242	4e-04	0.9992	5e-04	0.2323
178	-0.1	1.7356	0.526	2.9451	0.0015	0.9995	0.0088	0.2774
179	0.5	2.2608	0.9966	3.5251	0.0032	0.9999	0.5984	0.5984
180	0.9	2.26	0.9892	3.5309	0.018	0.9967	0.8456	0.5975

\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 & 2.087 & 1.7275 & 2.4464 & 0.004 & 0.4717 & 0 & 0.4717 \tabularnewline
169 & 1.1 & 2.064 & 1.5459 & 2.5822 & 1e-04 & 0.9604 & 0 & 0.4459 \tabularnewline
170 & 1.2 & 1.9879 & 1.3572 & 2.6186 & 0.0072 & 0.9971 & 0 & 0.3638 \tabularnewline
171 & 0.6 & 1.7645 & 1.0323 & 2.4968 & 9e-04 & 0.9346 & 0 & 0.1846 \tabularnewline
172 & 0.6 & 1.9448 & 1.1291 & 2.7605 & 6e-04 & 0.9994 & 6e-04 & 0.3546 \tabularnewline
173 & 0 & 1.6927 & 0.7967 & 2.5888 & 1e-04 & 0.9916 & 0 & 0.1865 \tabularnewline
174 & -0.1 & 1.4902 & 0.5247 & 2.4557 & 6e-04 & 0.9988 & 0 & 0.1079 \tabularnewline
175 & -0.6 & 1.5291 & 0.4952 & 2.563 & 0 & 0.999 & 0 & 0.1396 \tabularnewline
176 & -0.2 & 1.6826 & 0.5881 & 2.7772 & 4e-04 & 1 & 1e-04 & 0.2274 \tabularnewline
177 & -0.3 & 1.6691 & 0.5141 & 2.8242 & 4e-04 & 0.9992 & 5e-04 & 0.2323 \tabularnewline
178 & -0.1 & 1.7356 & 0.526 & 2.9451 & 0.0015 & 0.9995 & 0.0088 & 0.2774 \tabularnewline
179 & 0.5 & 2.2608 & 0.9966 & 3.5251 & 0.0032 & 0.9999 & 0.5984 & 0.5984 \tabularnewline
180 & 0.9 & 2.26 & 0.9892 & 3.5309 & 0.018 & 0.9967 & 0.8456 & 0.5975 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152041&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]2.087[/C][C]1.7275[/C][C]2.4464[/C][C]0.004[/C][C]0.4717[/C][C]0[/C][C]0.4717[/C][/ROW]
[ROW][C]169[/C][C]1.1[/C][C]2.064[/C][C]1.5459[/C][C]2.5822[/C][C]1e-04[/C][C]0.9604[/C][C]0[/C][C]0.4459[/C][/ROW]
[ROW][C]170[/C][C]1.2[/C][C]1.9879[/C][C]1.3572[/C][C]2.6186[/C][C]0.0072[/C][C]0.9971[/C][C]0[/C][C]0.3638[/C][/ROW]
[ROW][C]171[/C][C]0.6[/C][C]1.7645[/C][C]1.0323[/C][C]2.4968[/C][C]9e-04[/C][C]0.9346[/C][C]0[/C][C]0.1846[/C][/ROW]
[ROW][C]172[/C][C]0.6[/C][C]1.9448[/C][C]1.1291[/C][C]2.7605[/C][C]6e-04[/C][C]0.9994[/C][C]6e-04[/C][C]0.3546[/C][/ROW]
[ROW][C]173[/C][C]0[/C][C]1.6927[/C][C]0.7967[/C][C]2.5888[/C][C]1e-04[/C][C]0.9916[/C][C]0[/C][C]0.1865[/C][/ROW]
[ROW][C]174[/C][C]-0.1[/C][C]1.4902[/C][C]0.5247[/C][C]2.4557[/C][C]6e-04[/C][C]0.9988[/C][C]0[/C][C]0.1079[/C][/ROW]
[ROW][C]175[/C][C]-0.6[/C][C]1.5291[/C][C]0.4952[/C][C]2.563[/C][C]0[/C][C]0.999[/C][C]0[/C][C]0.1396[/C][/ROW]
[ROW][C]176[/C][C]-0.2[/C][C]1.6826[/C][C]0.5881[/C][C]2.7772[/C][C]4e-04[/C][C]1[/C][C]1e-04[/C][C]0.2274[/C][/ROW]
[ROW][C]177[/C][C]-0.3[/C][C]1.6691[/C][C]0.5141[/C][C]2.8242[/C][C]4e-04[/C][C]0.9992[/C][C]5e-04[/C][C]0.2323[/C][/ROW]
[ROW][C]178[/C][C]-0.1[/C][C]1.7356[/C][C]0.526[/C][C]2.9451[/C][C]0.0015[/C][C]0.9995[/C][C]0.0088[/C][C]0.2774[/C][/ROW]
[ROW][C]179[/C][C]0.5[/C][C]2.2608[/C][C]0.9966[/C][C]3.5251[/C][C]0.0032[/C][C]0.9999[/C][C]0.5984[/C][C]0.5984[/C][/ROW]
[ROW][C]180[/C][C]0.9[/C][C]2.26[/C][C]0.9892[/C][C]3.5309[/C][C]0.018[/C][C]0.9967[/C][C]0.8456[/C][C]0.5975[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152041&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152041&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	2.087	1.7275	2.4464	0.004	0.4717	0	0.4717
169	1.1	2.064	1.5459	2.5822	1e-04	0.9604	0	0.4459
170	1.2	1.9879	1.3572	2.6186	0.0072	0.9971	0	0.3638
171	0.6	1.7645	1.0323	2.4968	9e-04	0.9346	0	0.1846
172	0.6	1.9448	1.1291	2.7605	6e-04	0.9994	6e-04	0.3546
173	0	1.6927	0.7967	2.5888	1e-04	0.9916	0	0.1865
174	-0.1	1.4902	0.5247	2.4557	6e-04	0.9988	0	0.1079
175	-0.6	1.5291	0.4952	2.563	0	0.999	0	0.1396
176	-0.2	1.6826	0.5881	2.7772	4e-04	1	1e-04	0.2274
177	-0.3	1.6691	0.5141	2.8242	4e-04	0.9992	5e-04	0.2323
178	-0.1	1.7356	0.526	2.9451	0.0015	0.9995	0.0088	0.2774
179	0.5	2.2608	0.9966	3.5251	0.0032	0.9999	0.5984	0.5984
180	0.9	2.26	0.9892	3.5309	0.018	0.9967	0.8456	0.5975

Univariate ARIMA Extrapolation Forecast Performance
time	% S.E.	PE	MAPE	Sq.E	MSE	RMSE
168	0.0879	-0.2333	0	0.2371	0	0
169	0.1281	-0.4671	0.3502	0.9293	0.5832	0.7637
170	0.1619	-0.3963	0.3656	0.6208	0.5958	0.7719
171	0.2117	-0.66	0.4392	1.3561	0.7859	0.8865
172	0.214	-0.6915	0.4896	1.8084	0.9904	0.9952
173	0.2701	-1	0.5747	2.8654	1.3029	1.1414
174	0.3306	-1.0671	0.645	2.5288	1.478	1.2157
175	0.345	-1.3924	0.7385	4.533	1.8599	1.3638
176	0.3319	-1.1189	0.7807	3.5443	2.047	1.4307
177	0.3531	-1.1797	0.8206	3.8775	2.2301	1.4933
178	0.3556	-1.0576	0.8422	3.3693	2.3336	1.5276
179	0.2853	-0.7788	0.8369	3.1004	2.3975	1.5484
180	0.2869	-0.6018	0.8188	1.8497	2.3554	1.5347

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
168 & 0.0879 & -0.2333 & 0 & 0.2371 & 0 & 0 \tabularnewline
169 & 0.1281 & -0.4671 & 0.3502 & 0.9293 & 0.5832 & 0.7637 \tabularnewline
170 & 0.1619 & -0.3963 & 0.3656 & 0.6208 & 0.5958 & 0.7719 \tabularnewline
171 & 0.2117 & -0.66 & 0.4392 & 1.3561 & 0.7859 & 0.8865 \tabularnewline
172 & 0.214 & -0.6915 & 0.4896 & 1.8084 & 0.9904 & 0.9952 \tabularnewline
173 & 0.2701 & -1 & 0.5747 & 2.8654 & 1.3029 & 1.1414 \tabularnewline
174 & 0.3306 & -1.0671 & 0.645 & 2.5288 & 1.478 & 1.2157 \tabularnewline
175 & 0.345 & -1.3924 & 0.7385 & 4.533 & 1.8599 & 1.3638 \tabularnewline
176 & 0.3319 & -1.1189 & 0.7807 & 3.5443 & 2.047 & 1.4307 \tabularnewline
177 & 0.3531 & -1.1797 & 0.8206 & 3.8775 & 2.2301 & 1.4933 \tabularnewline
178 & 0.3556 & -1.0576 & 0.8422 & 3.3693 & 2.3336 & 1.5276 \tabularnewline
179 & 0.2853 & -0.7788 & 0.8369 & 3.1004 & 2.3975 & 1.5484 \tabularnewline
180 & 0.2869 & -0.6018 & 0.8188 & 1.8497 & 2.3554 & 1.5347 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=152041&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]0.0879[/C][C]-0.2333[/C][C]0[/C][C]0.2371[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]169[/C][C]0.1281[/C][C]-0.4671[/C][C]0.3502[/C][C]0.9293[/C][C]0.5832[/C][C]0.7637[/C][/ROW]
[ROW][C]170[/C][C]0.1619[/C][C]-0.3963[/C][C]0.3656[/C][C]0.6208[/C][C]0.5958[/C][C]0.7719[/C][/ROW]
[ROW][C]171[/C][C]0.2117[/C][C]-0.66[/C][C]0.4392[/C][C]1.3561[/C][C]0.7859[/C][C]0.8865[/C][/ROW]
[ROW][C]172[/C][C]0.214[/C][C]-0.6915[/C][C]0.4896[/C][C]1.8084[/C][C]0.9904[/C][C]0.9952[/C][/ROW]
[ROW][C]173[/C][C]0.2701[/C][C]-1[/C][C]0.5747[/C][C]2.8654[/C][C]1.3029[/C][C]1.1414[/C][/ROW]
[ROW][C]174[/C][C]0.3306[/C][C]-1.0671[/C][C]0.645[/C][C]2.5288[/C][C]1.478[/C][C]1.2157[/C][/ROW]
[ROW][C]175[/C][C]0.345[/C][C]-1.3924[/C][C]0.7385[/C][C]4.533[/C][C]1.8599[/C][C]1.3638[/C][/ROW]
[ROW][C]176[/C][C]0.3319[/C][C]-1.1189[/C][C]0.7807[/C][C]3.5443[/C][C]2.047[/C][C]1.4307[/C][/ROW]
[ROW][C]177[/C][C]0.3531[/C][C]-1.1797[/C][C]0.8206[/C][C]3.8775[/C][C]2.2301[/C][C]1.4933[/C][/ROW]
[ROW][C]178[/C][C]0.3556[/C][C]-1.0576[/C][C]0.8422[/C][C]3.3693[/C][C]2.3336[/C][C]1.5276[/C][/ROW]
[ROW][C]179[/C][C]0.2853[/C][C]-0.7788[/C][C]0.8369[/C][C]3.1004[/C][C]2.3975[/C][C]1.5484[/C][/ROW]
[ROW][C]180[/C][C]0.2869[/C][C]-0.6018[/C][C]0.8188[/C][C]1.8497[/C][C]2.3554[/C][C]1.5347[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=152041&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=152041&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	0.0879	-0.2333	0	0.2371	0	0
169	0.1281	-0.4671	0.3502	0.9293	0.5832	0.7637
170	0.1619	-0.3963	0.3656	0.6208	0.5958	0.7719
171	0.2117	-0.66	0.4392	1.3561	0.7859	0.8865
172	0.214	-0.6915	0.4896	1.8084	0.9904	0.9952
173	0.2701	-1	0.5747	2.8654	1.3029	1.1414
174	0.3306	-1.0671	0.645	2.5288	1.478	1.2157
175	0.345	-1.3924	0.7385	4.533	1.8599	1.3638
176	0.3319	-1.1189	0.7807	3.5443	2.047	1.4307
177	0.3531	-1.1797	0.8206	3.8775	2.2301	1.4933
178	0.3556	-1.0576	0.8422	3.3693	2.3336	1.5276
179	0.2853	-0.7788	0.8369	3.1004	2.3975	1.5484
180	0.2869	-0.6018	0.8188	1.8497	2.3554	1.5347

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 = 13 ; par2 = 1 ; par3 = 0 ; 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