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R Software Module

rwasp_arimaforecasting.wasp

Title produced by software

ARIMA Forecasting

Date of computation

Wed, 15 Dec 2010 17:27:57 +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/15/t1292433961s02yzbq2rjpfuuc.htm/, Retrieved Sun, 28 Apr 2024 12:17:04 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=110605, Retrieved Sun, 28 Apr 2024 12:17:04 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

171

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

-     [Univariate Explorative Data Analysis] [Monthly US soldie...] [2010-11-02 12:07:39] [b98453cac15ba1066b407e146608df68]
- RMP   [Variance Reduction Matrix] [Soldiers] [2010-11-29 09:51:25] [b98453cac15ba1066b407e146608df68]
- RM      [Standard Deviation-Mean Plot] [Soldiers] [2010-11-29 11:02:42] [b98453cac15ba1066b407e146608df68]
- RMP       [ARIMA Forecasting] [Soldiers] [2010-11-29 21:04:02] [b98453cac15ba1066b407e146608df68]
-   PD          [ARIMA Forecasting] [] [2010-12-15 17:27:57] [64cdeb58b12a5b72c79150d12c763b6f] [Current]
F   PD            [ARIMA Forecasting] [] [2010-12-17 08:37:05] [4dfa50539945b119a90a7606969443b9] 
- R PD              [ARIMA Forecasting] [] [2010-12-17 12:24:19] [c6813a60da787bb62b5d86150b8926dd] 
-   PD              [ARIMA Forecasting] [echte voorspelling] [2010-12-17 16:00:24] [75b8170d590d2aca2c97c1862bb2167f] 
-                     [ARIMA Forecasting] [Arima forecasting] [2010-12-17 16:24:27] [c895532cb7349383dee5125244983cc8] 
-                     [ARIMA Forecasting] [] [2010-12-17 19:43:59] [916599f00c9c716123aa8433d9efa14f] 
- R P               [ARIMA Forecasting] [forecasting ] [2012-12-02 20:11:28] [885d0a915dae889a27a534b235a2244f] 
-   P                 [ARIMA Forecasting] [ARIMA Forecast] [2012-12-18 21:14:33] [4187c7efada0bc11e94a41d61d855f64] 

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Dataseries X:

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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=110605&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=110605&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110605&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])
48	69	-	-	-	-	-	-	-
49	60	-	-	-	-	-	-	-
50	56	-	-	-	-	-	-	-
51	58	-	-	-	-	-	-	-
52	50	-	-	-	-	-	-	-
53	51	-	-	-	-	-	-	-
54	53	-	-	-	-	-	-	-
55	37	-	-	-	-	-	-	-
56	22	-	-	-	-	-	-	-
57	55	-	-	-	-	-	-	-
58	70	-	-	-	-	-	-	-
59	62	-	-	-	-	-	-	-
60	58	-	-	-	-	-	-	-
61	39	53.0525	35.8256	70.2794	0.0549	0.2867	0.2146	0.2867
62	49	52.4009	35.1717	69.63	0.3494	0.9363	0.3411	0.2621
63	58	65.7997	48.5705	83.0288	0.1875	0.972	0.8125	0.8125
64	47	50.8	33.5708	68.0291	0.3328	0.2064	0.5363	0.2064
65	42	51.1997	33.9706	68.4289	0.1476	0.6836	0.5091	0.2196
66	62	59.1999	41.9707	76.429	0.375	0.9748	0.7597	0.5543
67	39	38.7997	21.5705	56.0288	0.4909	0.0042	0.5811	0.0145
68	40	28.3998	11.1706	45.6289	0.0935	0.1139	0.7667	4e-04
69	72	54.7998	37.5706	72.0289	0.0252	0.9539	0.4909	0.3579
70	70	62.6001	45.371	79.8293	0.1999	0.1425	0.1999	0.6996
71	54	48.3999	31.1708	65.6291	0.262	0.007	0.0609	0.1374
72	65	55.377	38.1479	72.6061	0.1368	0.5622	0.3827	0.3827

\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
48 & 69 & - & - & - & - & - & - & - \tabularnewline
49 & 60 & - & - & - & - & - & - & - \tabularnewline
50 & 56 & - & - & - & - & - & - & - \tabularnewline
51 & 58 & - & - & - & - & - & - & - \tabularnewline
52 & 50 & - & - & - & - & - & - & - \tabularnewline
53 & 51 & - & - & - & - & - & - & - \tabularnewline
54 & 53 & - & - & - & - & - & - & - \tabularnewline
55 & 37 & - & - & - & - & - & - & - \tabularnewline
56 & 22 & - & - & - & - & - & - & - \tabularnewline
57 & 55 & - & - & - & - & - & - & - \tabularnewline
58 & 70 & - & - & - & - & - & - & - \tabularnewline
59 & 62 & - & - & - & - & - & - & - \tabularnewline
60 & 58 & - & - & - & - & - & - & - \tabularnewline
61 & 39 & 53.0525 & 35.8256 & 70.2794 & 0.0549 & 0.2867 & 0.2146 & 0.2867 \tabularnewline
62 & 49 & 52.4009 & 35.1717 & 69.63 & 0.3494 & 0.9363 & 0.3411 & 0.2621 \tabularnewline
63 & 58 & 65.7997 & 48.5705 & 83.0288 & 0.1875 & 0.972 & 0.8125 & 0.8125 \tabularnewline
64 & 47 & 50.8 & 33.5708 & 68.0291 & 0.3328 & 0.2064 & 0.5363 & 0.2064 \tabularnewline
65 & 42 & 51.1997 & 33.9706 & 68.4289 & 0.1476 & 0.6836 & 0.5091 & 0.2196 \tabularnewline
66 & 62 & 59.1999 & 41.9707 & 76.429 & 0.375 & 0.9748 & 0.7597 & 0.5543 \tabularnewline
67 & 39 & 38.7997 & 21.5705 & 56.0288 & 0.4909 & 0.0042 & 0.5811 & 0.0145 \tabularnewline
68 & 40 & 28.3998 & 11.1706 & 45.6289 & 0.0935 & 0.1139 & 0.7667 & 4e-04 \tabularnewline
69 & 72 & 54.7998 & 37.5706 & 72.0289 & 0.0252 & 0.9539 & 0.4909 & 0.3579 \tabularnewline
70 & 70 & 62.6001 & 45.371 & 79.8293 & 0.1999 & 0.1425 & 0.1999 & 0.6996 \tabularnewline
71 & 54 & 48.3999 & 31.1708 & 65.6291 & 0.262 & 0.007 & 0.0609 & 0.1374 \tabularnewline
72 & 65 & 55.377 & 38.1479 & 72.6061 & 0.1368 & 0.5622 & 0.3827 & 0.3827 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110605&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]48[/C][C]69[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]49[/C][C]60[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]50[/C][C]56[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]51[/C][C]58[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]52[/C][C]50[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]53[/C][C]51[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/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]53.0525[/C][C]35.8256[/C][C]70.2794[/C][C]0.0549[/C][C]0.2867[/C][C]0.2146[/C][C]0.2867[/C][/ROW]
[ROW][C]62[/C][C]49[/C][C]52.4009[/C][C]35.1717[/C][C]69.63[/C][C]0.3494[/C][C]0.9363[/C][C]0.3411[/C][C]0.2621[/C][/ROW]
[ROW][C]63[/C][C]58[/C][C]65.7997[/C][C]48.5705[/C][C]83.0288[/C][C]0.1875[/C][C]0.972[/C][C]0.8125[/C][C]0.8125[/C][/ROW]
[ROW][C]64[/C][C]47[/C][C]50.8[/C][C]33.5708[/C][C]68.0291[/C][C]0.3328[/C][C]0.2064[/C][C]0.5363[/C][C]0.2064[/C][/ROW]
[ROW][C]65[/C][C]42[/C][C]51.1997[/C][C]33.9706[/C][C]68.4289[/C][C]0.1476[/C][C]0.6836[/C][C]0.5091[/C][C]0.2196[/C][/ROW]
[ROW][C]66[/C][C]62[/C][C]59.1999[/C][C]41.9707[/C][C]76.429[/C][C]0.375[/C][C]0.9748[/C][C]0.7597[/C][C]0.5543[/C][/ROW]
[ROW][C]67[/C][C]39[/C][C]38.7997[/C][C]21.5705[/C][C]56.0288[/C][C]0.4909[/C][C]0.0042[/C][C]0.5811[/C][C]0.0145[/C][/ROW]
[ROW][C]68[/C][C]40[/C][C]28.3998[/C][C]11.1706[/C][C]45.6289[/C][C]0.0935[/C][C]0.1139[/C][C]0.7667[/C][C]4e-04[/C][/ROW]
[ROW][C]69[/C][C]72[/C][C]54.7998[/C][C]37.5706[/C][C]72.0289[/C][C]0.0252[/C][C]0.9539[/C][C]0.4909[/C][C]0.3579[/C][/ROW]
[ROW][C]70[/C][C]70[/C][C]62.6001[/C][C]45.371[/C][C]79.8293[/C][C]0.1999[/C][C]0.1425[/C][C]0.1999[/C][C]0.6996[/C][/ROW]
[ROW][C]71[/C][C]54[/C][C]48.3999[/C][C]31.1708[/C][C]65.6291[/C][C]0.262[/C][C]0.007[/C][C]0.0609[/C][C]0.1374[/C][/ROW]
[ROW][C]72[/C][C]65[/C][C]55.377[/C][C]38.1479[/C][C]72.6061[/C][C]0.1368[/C][C]0.5622[/C][C]0.3827[/C][C]0.3827[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110605&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110605&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])
48	69	-	-	-	-	-	-	-
49	60	-	-	-	-	-	-	-
50	56	-	-	-	-	-	-	-
51	58	-	-	-	-	-	-	-
52	50	-	-	-	-	-	-	-
53	51	-	-	-	-	-	-	-
54	53	-	-	-	-	-	-	-
55	37	-	-	-	-	-	-	-
56	22	-	-	-	-	-	-	-
57	55	-	-	-	-	-	-	-
58	70	-	-	-	-	-	-	-
59	62	-	-	-	-	-	-	-
60	58	-	-	-	-	-	-	-
61	39	53.0525	35.8256	70.2794	0.0549	0.2867	0.2146	0.2867
62	49	52.4009	35.1717	69.63	0.3494	0.9363	0.3411	0.2621
63	58	65.7997	48.5705	83.0288	0.1875	0.972	0.8125	0.8125
64	47	50.8	33.5708	68.0291	0.3328	0.2064	0.5363	0.2064
65	42	51.1997	33.9706	68.4289	0.1476	0.6836	0.5091	0.2196
66	62	59.1999	41.9707	76.429	0.375	0.9748	0.7597	0.5543
67	39	38.7997	21.5705	56.0288	0.4909	0.0042	0.5811	0.0145
68	40	28.3998	11.1706	45.6289	0.0935	0.1139	0.7667	4e-04
69	72	54.7998	37.5706	72.0289	0.0252	0.9539	0.4909	0.3579
70	70	62.6001	45.371	79.8293	0.1999	0.1425	0.1999	0.6996
71	54	48.3999	31.1708	65.6291	0.262	0.007	0.0609	0.1374
72	65	55.377	38.1479	72.6061	0.1368	0.5622	0.3827	0.3827

Univariate ARIMA Extrapolation Forecast Performance
time	% S.E.	PE	MAPE	Sq.E	MSE	RMSE
61	0.1657	-0.2649	0	197.4728	0	0
62	0.1678	-0.0649	0.1649	11.566	104.5194	10.2235
63	0.1336	-0.1185	0.1494	60.8347	89.9578	9.4846
64	0.173	-0.0748	0.1308	14.4397	71.0783	8.4308
65	0.1717	-0.1797	0.1406	84.6352	73.7897	8.5901
66	0.1485	0.0473	0.125	7.8406	62.7982	7.9245
67	0.2266	0.0052	0.1079	0.0401	53.8327	7.3371
68	0.3095	0.4085	0.1455	134.5651	63.9243	7.9953
69	0.1604	0.3139	0.1642	295.8471	89.6935	9.4707
70	0.1404	0.1182	0.1596	54.7583	86.2	9.2844
71	0.1816	0.1157	0.1556	31.3607	81.2146	9.0119
72	0.1587	0.1738	0.1571	92.6015	82.1635	9.0644

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
61 & 0.1657 & -0.2649 & 0 & 197.4728 & 0 & 0 \tabularnewline
62 & 0.1678 & -0.0649 & 0.1649 & 11.566 & 104.5194 & 10.2235 \tabularnewline
63 & 0.1336 & -0.1185 & 0.1494 & 60.8347 & 89.9578 & 9.4846 \tabularnewline
64 & 0.173 & -0.0748 & 0.1308 & 14.4397 & 71.0783 & 8.4308 \tabularnewline
65 & 0.1717 & -0.1797 & 0.1406 & 84.6352 & 73.7897 & 8.5901 \tabularnewline
66 & 0.1485 & 0.0473 & 0.125 & 7.8406 & 62.7982 & 7.9245 \tabularnewline
67 & 0.2266 & 0.0052 & 0.1079 & 0.0401 & 53.8327 & 7.3371 \tabularnewline
68 & 0.3095 & 0.4085 & 0.1455 & 134.5651 & 63.9243 & 7.9953 \tabularnewline
69 & 0.1604 & 0.3139 & 0.1642 & 295.8471 & 89.6935 & 9.4707 \tabularnewline
70 & 0.1404 & 0.1182 & 0.1596 & 54.7583 & 86.2 & 9.2844 \tabularnewline
71 & 0.1816 & 0.1157 & 0.1556 & 31.3607 & 81.2146 & 9.0119 \tabularnewline
72 & 0.1587 & 0.1738 & 0.1571 & 92.6015 & 82.1635 & 9.0644 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110605&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]0.1657[/C][C]-0.2649[/C][C]0[/C][C]197.4728[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]62[/C][C]0.1678[/C][C]-0.0649[/C][C]0.1649[/C][C]11.566[/C][C]104.5194[/C][C]10.2235[/C][/ROW]
[ROW][C]63[/C][C]0.1336[/C][C]-0.1185[/C][C]0.1494[/C][C]60.8347[/C][C]89.9578[/C][C]9.4846[/C][/ROW]
[ROW][C]64[/C][C]0.173[/C][C]-0.0748[/C][C]0.1308[/C][C]14.4397[/C][C]71.0783[/C][C]8.4308[/C][/ROW]
[ROW][C]65[/C][C]0.1717[/C][C]-0.1797[/C][C]0.1406[/C][C]84.6352[/C][C]73.7897[/C][C]8.5901[/C][/ROW]
[ROW][C]66[/C][C]0.1485[/C][C]0.0473[/C][C]0.125[/C][C]7.8406[/C][C]62.7982[/C][C]7.9245[/C][/ROW]
[ROW][C]67[/C][C]0.2266[/C][C]0.0052[/C][C]0.1079[/C][C]0.0401[/C][C]53.8327[/C][C]7.3371[/C][/ROW]
[ROW][C]68[/C][C]0.3095[/C][C]0.4085[/C][C]0.1455[/C][C]134.5651[/C][C]63.9243[/C][C]7.9953[/C][/ROW]
[ROW][C]69[/C][C]0.1604[/C][C]0.3139[/C][C]0.1642[/C][C]295.8471[/C][C]89.6935[/C][C]9.4707[/C][/ROW]
[ROW][C]70[/C][C]0.1404[/C][C]0.1182[/C][C]0.1596[/C][C]54.7583[/C][C]86.2[/C][C]9.2844[/C][/ROW]
[ROW][C]71[/C][C]0.1816[/C][C]0.1157[/C][C]0.1556[/C][C]31.3607[/C][C]81.2146[/C][C]9.0119[/C][/ROW]
[ROW][C]72[/C][C]0.1587[/C][C]0.1738[/C][C]0.1571[/C][C]92.6015[/C][C]82.1635[/C][C]9.0644[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110605&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110605&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	0.1657	-0.2649	0	197.4728	0	0
62	0.1678	-0.0649	0.1649	11.566	104.5194	10.2235
63	0.1336	-0.1185	0.1494	60.8347	89.9578	9.4846
64	0.173	-0.0748	0.1308	14.4397	71.0783	8.4308
65	0.1717	-0.1797	0.1406	84.6352	73.7897	8.5901
66	0.1485	0.0473	0.125	7.8406	62.7982	7.9245
67	0.2266	0.0052	0.1079	0.0401	53.8327	7.3371
68	0.3095	0.4085	0.1455	134.5651	63.9243	7.9953
69	0.1604	0.3139	0.1642	295.8471	89.6935	9.4707
70	0.1404	0.1182	0.1596	54.7583	86.2	9.2844
71	0.1816	0.1157	0.1556	31.3607	81.2146	9.0119
72	0.1587	0.1738	0.1571	92.6015	82.1635	9.0644

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 = 12 ; par6 = 1 ; par7 = 0 ; par8 = 0 ; par9 = 1 ; par10 = FALSE ;

Parameters (R input):

par1 = 12 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 1 ; 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