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R Software Module

rwasp_arimaforecasting.wasp

Title produced by software

ARIMA Forecasting

Date of computation

Sat, 04 Dec 2010 22:07:21 +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/04/t1291500479zn8d9efrgnv64ah.htm/, Retrieved Sun, 05 May 2024 08:24:34 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=105273, Retrieved Sun, 05 May 2024 08:24:34 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

158

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-12 13:32:37] [76963dc1903f0f612b6153510a3818cf]
- R  D  [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-17 12:14:40] [76963dc1903f0f612b6153510a3818cf]
-         [Univariate Explorative Data Analysis] [Run Sequence Plot...] [2008-12-22 18:19:51] [1ce0d16c8f4225c977b42c8fa93bc163]
- RMP       [Univariate Data Series] [Identifying Integ...] [2009-11-22 12:08:06] [b98453cac15ba1066b407e146608df68]
- RMP         [Standard Deviation-Mean Plot] [Births] [2010-11-29 10:52:49] [b98453cac15ba1066b407e146608df68]
- RMP           [ARIMA Forecasting] [Births] [2010-11-29 20:53:49] [b98453cac15ba1066b407e146608df68]
-   PD            [ARIMA Forecasting] [WS9 Arima forecast] [2010-12-04 17:55:48] [65eb19f81eab2b6e672eafaed2a27190]
-   P                 [ARIMA Forecasting] [WS9 Arima forecast] [2010-12-04 22:07:21] [8b27277f7b82c0354d659d066108e38e] [Current]

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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	'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 & 2 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105273&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105273&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105273&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	'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[60])
54	53	-	-	-	-	-	-	-
55	37	-	-	-	-	-	-	-
56	22	-	-	-	-	-	-	-
57	55	-	-	-	-	-	-	-
58	70	-	-	-	-	-	-	-
59	62	-	-	-	-	-	-	-
60	58	-	-	-	-	-	-	-
61	39	45.9	24.7039	67.0961	0.2617	0.1316	0.7947	0.1316
62	49	40.4	19.2039	61.5961	0.2132	0.5515	0.9556	0.0518
63	58	60.3	39.1039	81.4961	0.4158	0.852	0.688	0.5842
64	47	56.7	35.5039	77.8961	0.1849	0.4522	0.1094	0.4522
65	42	49.8	28.6039	70.9961	0.2354	0.6021	0.1296	0.2242
66	62	57.3	36.1039	78.4961	0.3319	0.9214	0.4742	0.4742
67	39	45.9	24.7039	67.0961	0.2617	0.0683	0.7383	0.1316
68	40	40.4	19.2039	61.5961	0.4852	0.5515	0.2132	0.0518
69	72	60.3	39.1039	81.4961	0.1396	0.9698	0.5842	0.5842
70	70	56.7	35.5039	77.8961	0.1094	0.0786	0.8151	0.4522
71	54	49.8	28.6039	70.9961	0.3489	0.0309	0.7646	0.2242
72	65	57.3	36.1039	78.4961	0.2382	0.6199	0.3319	0.4742

\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[60]) \tabularnewline
54 & 53 & - & - & - & - & - & - & - \tabularnewline
55 & 37 & - & - & - & - & - & - & - \tabularnewline
56 & 22 & - & - & - & - & - & - & - \tabularnewline
57 & 55 & - & - & - & - & - & - & - \tabularnewline
58 & 70 & - & - & - & - & - & - & - \tabularnewline
59 & 62 & - & - & - & - & - & - & - \tabularnewline
60 & 58 & - & - & - & - & - & - & - \tabularnewline
61 & 39 & 45.9 & 24.7039 & 67.0961 & 0.2617 & 0.1316 & 0.7947 & 0.1316 \tabularnewline
62 & 49 & 40.4 & 19.2039 & 61.5961 & 0.2132 & 0.5515 & 0.9556 & 0.0518 \tabularnewline
63 & 58 & 60.3 & 39.1039 & 81.4961 & 0.4158 & 0.852 & 0.688 & 0.5842 \tabularnewline
64 & 47 & 56.7 & 35.5039 & 77.8961 & 0.1849 & 0.4522 & 0.1094 & 0.4522 \tabularnewline
65 & 42 & 49.8 & 28.6039 & 70.9961 & 0.2354 & 0.6021 & 0.1296 & 0.2242 \tabularnewline
66 & 62 & 57.3 & 36.1039 & 78.4961 & 0.3319 & 0.9214 & 0.4742 & 0.4742 \tabularnewline
67 & 39 & 45.9 & 24.7039 & 67.0961 & 0.2617 & 0.0683 & 0.7383 & 0.1316 \tabularnewline
68 & 40 & 40.4 & 19.2039 & 61.5961 & 0.4852 & 0.5515 & 0.2132 & 0.0518 \tabularnewline
69 & 72 & 60.3 & 39.1039 & 81.4961 & 0.1396 & 0.9698 & 0.5842 & 0.5842 \tabularnewline
70 & 70 & 56.7 & 35.5039 & 77.8961 & 0.1094 & 0.0786 & 0.8151 & 0.4522 \tabularnewline
71 & 54 & 49.8 & 28.6039 & 70.9961 & 0.3489 & 0.0309 & 0.7646 & 0.2242 \tabularnewline
72 & 65 & 57.3 & 36.1039 & 78.4961 & 0.2382 & 0.6199 & 0.3319 & 0.4742 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105273&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[60])[/C][/ROW]
[ROW][C]54[/C][C]53[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]55[/C][C]37[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]56[/C][C]22[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]57[/C][C]55[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]58[/C][C]70[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]59[/C][C]62[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]60[/C][C]58[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]61[/C][C]39[/C][C]45.9[/C][C]24.7039[/C][C]67.0961[/C][C]0.2617[/C][C]0.1316[/C][C]0.7947[/C][C]0.1316[/C][/ROW]
[ROW][C]62[/C][C]49[/C][C]40.4[/C][C]19.2039[/C][C]61.5961[/C][C]0.2132[/C][C]0.5515[/C][C]0.9556[/C][C]0.0518[/C][/ROW]
[ROW][C]63[/C][C]58[/C][C]60.3[/C][C]39.1039[/C][C]81.4961[/C][C]0.4158[/C][C]0.852[/C][C]0.688[/C][C]0.5842[/C][/ROW]
[ROW][C]64[/C][C]47[/C][C]56.7[/C][C]35.5039[/C][C]77.8961[/C][C]0.1849[/C][C]0.4522[/C][C]0.1094[/C][C]0.4522[/C][/ROW]
[ROW][C]65[/C][C]42[/C][C]49.8[/C][C]28.6039[/C][C]70.9961[/C][C]0.2354[/C][C]0.6021[/C][C]0.1296[/C][C]0.2242[/C][/ROW]
[ROW][C]66[/C][C]62[/C][C]57.3[/C][C]36.1039[/C][C]78.4961[/C][C]0.3319[/C][C]0.9214[/C][C]0.4742[/C][C]0.4742[/C][/ROW]
[ROW][C]67[/C][C]39[/C][C]45.9[/C][C]24.7039[/C][C]67.0961[/C][C]0.2617[/C][C]0.0683[/C][C]0.7383[/C][C]0.1316[/C][/ROW]
[ROW][C]68[/C][C]40[/C][C]40.4[/C][C]19.2039[/C][C]61.5961[/C][C]0.4852[/C][C]0.5515[/C][C]0.2132[/C][C]0.0518[/C][/ROW]
[ROW][C]69[/C][C]72[/C][C]60.3[/C][C]39.1039[/C][C]81.4961[/C][C]0.1396[/C][C]0.9698[/C][C]0.5842[/C][C]0.5842[/C][/ROW]
[ROW][C]70[/C][C]70[/C][C]56.7[/C][C]35.5039[/C][C]77.8961[/C][C]0.1094[/C][C]0.0786[/C][C]0.8151[/C][C]0.4522[/C][/ROW]
[ROW][C]71[/C][C]54[/C][C]49.8[/C][C]28.6039[/C][C]70.9961[/C][C]0.3489[/C][C]0.0309[/C][C]0.7646[/C][C]0.2242[/C][/ROW]
[ROW][C]72[/C][C]65[/C][C]57.3[/C][C]36.1039[/C][C]78.4961[/C][C]0.2382[/C][C]0.6199[/C][C]0.3319[/C][C]0.4742[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105273&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105273&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[60])
54	53	-	-	-	-	-	-	-
55	37	-	-	-	-	-	-	-
56	22	-	-	-	-	-	-	-
57	55	-	-	-	-	-	-	-
58	70	-	-	-	-	-	-	-
59	62	-	-	-	-	-	-	-
60	58	-	-	-	-	-	-	-
61	39	45.9	24.7039	67.0961	0.2617	0.1316	0.7947	0.1316
62	49	40.4	19.2039	61.5961	0.2132	0.5515	0.9556	0.0518
63	58	60.3	39.1039	81.4961	0.4158	0.852	0.688	0.5842
64	47	56.7	35.5039	77.8961	0.1849	0.4522	0.1094	0.4522
65	42	49.8	28.6039	70.9961	0.2354	0.6021	0.1296	0.2242
66	62	57.3	36.1039	78.4961	0.3319	0.9214	0.4742	0.4742
67	39	45.9	24.7039	67.0961	0.2617	0.0683	0.7383	0.1316
68	40	40.4	19.2039	61.5961	0.4852	0.5515	0.2132	0.0518
69	72	60.3	39.1039	81.4961	0.1396	0.9698	0.5842	0.5842
70	70	56.7	35.5039	77.8961	0.1094	0.0786	0.8151	0.4522
71	54	49.8	28.6039	70.9961	0.3489	0.0309	0.7646	0.2242
72	65	57.3	36.1039	78.4961	0.2382	0.6199	0.3319	0.4742

Univariate ARIMA Extrapolation Forecast Performance
time	% S.E.	PE	MAPE	Sq.E	MSE	RMSE
61	0.2356	-0.1503	0	47.6099	0	0
62	0.2677	0.2129	0.1816	73.9601	60.785	7.7965
63	0.1793	-0.0381	0.1338	5.29	42.2867	6.5028
64	0.1907	-0.1711	0.1431	94.0899	55.2375	7.4322
65	0.2172	-0.1566	0.1458	60.8399	56.358	7.5072
66	0.1887	0.082	0.1352	22.0901	50.6466	7.1166
67	0.2356	-0.1503	0.1373	47.6099	50.2128	7.0861
68	0.2677	-0.0099	0.1214	0.16	43.9562	6.6299
69	0.1793	0.194	0.1295	136.8901	54.2822	7.3676
70	0.1907	0.2346	0.14	176.8902	66.543	8.1574
71	0.2172	0.0843	0.1349	17.64	62.0973	7.8802
72	0.1887	0.1344	0.1349	59.2901	61.8634	7.8653

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
61 & 0.2356 & -0.1503 & 0 & 47.6099 & 0 & 0 \tabularnewline
62 & 0.2677 & 0.2129 & 0.1816 & 73.9601 & 60.785 & 7.7965 \tabularnewline
63 & 0.1793 & -0.0381 & 0.1338 & 5.29 & 42.2867 & 6.5028 \tabularnewline
64 & 0.1907 & -0.1711 & 0.1431 & 94.0899 & 55.2375 & 7.4322 \tabularnewline
65 & 0.2172 & -0.1566 & 0.1458 & 60.8399 & 56.358 & 7.5072 \tabularnewline
66 & 0.1887 & 0.082 & 0.1352 & 22.0901 & 50.6466 & 7.1166 \tabularnewline
67 & 0.2356 & -0.1503 & 0.1373 & 47.6099 & 50.2128 & 7.0861 \tabularnewline
68 & 0.2677 & -0.0099 & 0.1214 & 0.16 & 43.9562 & 6.6299 \tabularnewline
69 & 0.1793 & 0.194 & 0.1295 & 136.8901 & 54.2822 & 7.3676 \tabularnewline
70 & 0.1907 & 0.2346 & 0.14 & 176.8902 & 66.543 & 8.1574 \tabularnewline
71 & 0.2172 & 0.0843 & 0.1349 & 17.64 & 62.0973 & 7.8802 \tabularnewline
72 & 0.1887 & 0.1344 & 0.1349 & 59.2901 & 61.8634 & 7.8653 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105273&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]61[/C][C]0.2356[/C][C]-0.1503[/C][C]0[/C][C]47.6099[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]62[/C][C]0.2677[/C][C]0.2129[/C][C]0.1816[/C][C]73.9601[/C][C]60.785[/C][C]7.7965[/C][/ROW]
[ROW][C]63[/C][C]0.1793[/C][C]-0.0381[/C][C]0.1338[/C][C]5.29[/C][C]42.2867[/C][C]6.5028[/C][/ROW]
[ROW][C]64[/C][C]0.1907[/C][C]-0.1711[/C][C]0.1431[/C][C]94.0899[/C][C]55.2375[/C][C]7.4322[/C][/ROW]
[ROW][C]65[/C][C]0.2172[/C][C]-0.1566[/C][C]0.1458[/C][C]60.8399[/C][C]56.358[/C][C]7.5072[/C][/ROW]
[ROW][C]66[/C][C]0.1887[/C][C]0.082[/C][C]0.1352[/C][C]22.0901[/C][C]50.6466[/C][C]7.1166[/C][/ROW]
[ROW][C]67[/C][C]0.2356[/C][C]-0.1503[/C][C]0.1373[/C][C]47.6099[/C][C]50.2128[/C][C]7.0861[/C][/ROW]
[ROW][C]68[/C][C]0.2677[/C][C]-0.0099[/C][C]0.1214[/C][C]0.16[/C][C]43.9562[/C][C]6.6299[/C][/ROW]
[ROW][C]69[/C][C]0.1793[/C][C]0.194[/C][C]0.1295[/C][C]136.8901[/C][C]54.2822[/C][C]7.3676[/C][/ROW]
[ROW][C]70[/C][C]0.1907[/C][C]0.2346[/C][C]0.14[/C][C]176.8902[/C][C]66.543[/C][C]8.1574[/C][/ROW]
[ROW][C]71[/C][C]0.2172[/C][C]0.0843[/C][C]0.1349[/C][C]17.64[/C][C]62.0973[/C][C]7.8802[/C][/ROW]
[ROW][C]72[/C][C]0.1887[/C][C]0.1344[/C][C]0.1349[/C][C]59.2901[/C][C]61.8634[/C][C]7.8653[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105273&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105273&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
61	0.2356	-0.1503	0	47.6099	0	0
62	0.2677	0.2129	0.1816	73.9601	60.785	7.7965
63	0.1793	-0.0381	0.1338	5.29	42.2867	6.5028
64	0.1907	-0.1711	0.1431	94.0899	55.2375	7.4322
65	0.2172	-0.1566	0.1458	60.8399	56.358	7.5072
66	0.1887	0.082	0.1352	22.0901	50.6466	7.1166
67	0.2356	-0.1503	0.1373	47.6099	50.2128	7.0861
68	0.2677	-0.0099	0.1214	0.16	43.9562	6.6299
69	0.1793	0.194	0.1295	136.8901	54.2822	7.3676
70	0.1907	0.2346	0.14	176.8902	66.543	8.1574
71	0.2172	0.0843	0.1349	17.64	62.0973	7.8802
72	0.1887	0.1344	0.1349	59.2901	61.8634	7.8653

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 12 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 6 ; par6 = 0 ; par7 = 0 ; par8 = 0 ; par9 = 1 ; par10 = FALSE ;

Parameters (R input):

par1 = 12 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 6 ; par6 = 0 ; par7 = 0 ; par8 = 0 ; par9 = 1 ; 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