Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_arimaforecasting.wasp
Title produced by softwareARIMA Forecasting
Date of computationWed, 04 Dec 2013 07:20:08 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Dec/04/t1386160280hy3lij3mbtvzff9.htm/, Retrieved Thu, 28 Mar 2024 21:04:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=230571, Retrieved Thu, 28 Mar 2024 21:04:56 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact75
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [ARIMA Forecasting] [ws9] [2013-12-04 12:20:08] [a17fd0651b2d0a0974784add16174701] [Current]
Feedback Forum

Post a new message
Dataseries X:
46
62
66
59
58
61
41
27
58
70
49
59
44
36
72
45
56
54
53
35
61
52
47
51
52
63
74
45
51
64
36
30
55
64
39
40
63
45
59
55
40
64
27
28
45
57
45
69
60
56
58
50
51
53
37
22
55
70
62
58
39
49
58
47
42
62
39
40
72
70
54
65




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 4 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=230571&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=230571&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=230571&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Univariate ARIMA Extrapolation Forecast
timeY[t]F[t]95% LB95% UBp-value(H0: Y[t] = F[t])P(F[t]>Y[t-1])P(F[t]>Y[t-s])P(F[t]>Y[60])
4869-------
4960-------
5056-------
5158-------
5250-------
5351-------
5453-------
5537-------
5622-------
5755-------
5870-------
5962-------
6058-------
61390-104.106104.1060.23140.13740.12930.1374
62490-104.106104.1060.17810.23140.14590.1374
63580-104.106104.1060.13740.17810.13740.1374
64470-104.106104.1060.18810.13740.17330.1374
65420-104.106104.1060.21450.18810.16850.1374
66620-104.106104.1060.12160.21450.15920.1374
67390-104.106104.1060.23140.12160.2430.1374
68400-104.106104.1060.22570.23140.33940.1374
69720-104.106104.1060.08760.22570.15020.1374
70700-104.106104.1060.09380.08760.09380.1374
71540-104.106104.1060.15470.09380.12160.1374
72650-104.106104.1060.11050.15470.13740.1374

\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 & 0 & -104.106 & 104.106 & 0.2314 & 0.1374 & 0.1293 & 0.1374 \tabularnewline
62 & 49 & 0 & -104.106 & 104.106 & 0.1781 & 0.2314 & 0.1459 & 0.1374 \tabularnewline
63 & 58 & 0 & -104.106 & 104.106 & 0.1374 & 0.1781 & 0.1374 & 0.1374 \tabularnewline
64 & 47 & 0 & -104.106 & 104.106 & 0.1881 & 0.1374 & 0.1733 & 0.1374 \tabularnewline
65 & 42 & 0 & -104.106 & 104.106 & 0.2145 & 0.1881 & 0.1685 & 0.1374 \tabularnewline
66 & 62 & 0 & -104.106 & 104.106 & 0.1216 & 0.2145 & 0.1592 & 0.1374 \tabularnewline
67 & 39 & 0 & -104.106 & 104.106 & 0.2314 & 0.1216 & 0.243 & 0.1374 \tabularnewline
68 & 40 & 0 & -104.106 & 104.106 & 0.2257 & 0.2314 & 0.3394 & 0.1374 \tabularnewline
69 & 72 & 0 & -104.106 & 104.106 & 0.0876 & 0.2257 & 0.1502 & 0.1374 \tabularnewline
70 & 70 & 0 & -104.106 & 104.106 & 0.0938 & 0.0876 & 0.0938 & 0.1374 \tabularnewline
71 & 54 & 0 & -104.106 & 104.106 & 0.1547 & 0.0938 & 0.1216 & 0.1374 \tabularnewline
72 & 65 & 0 & -104.106 & 104.106 & 0.1105 & 0.1547 & 0.1374 & 0.1374 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=230571&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]0[/C][C]-104.106[/C][C]104.106[/C][C]0.2314[/C][C]0.1374[/C][C]0.1293[/C][C]0.1374[/C][/ROW]
[ROW][C]62[/C][C]49[/C][C]0[/C][C]-104.106[/C][C]104.106[/C][C]0.1781[/C][C]0.2314[/C][C]0.1459[/C][C]0.1374[/C][/ROW]
[ROW][C]63[/C][C]58[/C][C]0[/C][C]-104.106[/C][C]104.106[/C][C]0.1374[/C][C]0.1781[/C][C]0.1374[/C][C]0.1374[/C][/ROW]
[ROW][C]64[/C][C]47[/C][C]0[/C][C]-104.106[/C][C]104.106[/C][C]0.1881[/C][C]0.1374[/C][C]0.1733[/C][C]0.1374[/C][/ROW]
[ROW][C]65[/C][C]42[/C][C]0[/C][C]-104.106[/C][C]104.106[/C][C]0.2145[/C][C]0.1881[/C][C]0.1685[/C][C]0.1374[/C][/ROW]
[ROW][C]66[/C][C]62[/C][C]0[/C][C]-104.106[/C][C]104.106[/C][C]0.1216[/C][C]0.2145[/C][C]0.1592[/C][C]0.1374[/C][/ROW]
[ROW][C]67[/C][C]39[/C][C]0[/C][C]-104.106[/C][C]104.106[/C][C]0.2314[/C][C]0.1216[/C][C]0.243[/C][C]0.1374[/C][/ROW]
[ROW][C]68[/C][C]40[/C][C]0[/C][C]-104.106[/C][C]104.106[/C][C]0.2257[/C][C]0.2314[/C][C]0.3394[/C][C]0.1374[/C][/ROW]
[ROW][C]69[/C][C]72[/C][C]0[/C][C]-104.106[/C][C]104.106[/C][C]0.0876[/C][C]0.2257[/C][C]0.1502[/C][C]0.1374[/C][/ROW]
[ROW][C]70[/C][C]70[/C][C]0[/C][C]-104.106[/C][C]104.106[/C][C]0.0938[/C][C]0.0876[/C][C]0.0938[/C][C]0.1374[/C][/ROW]
[ROW][C]71[/C][C]54[/C][C]0[/C][C]-104.106[/C][C]104.106[/C][C]0.1547[/C][C]0.0938[/C][C]0.1216[/C][C]0.1374[/C][/ROW]
[ROW][C]72[/C][C]65[/C][C]0[/C][C]-104.106[/C][C]104.106[/C][C]0.1105[/C][C]0.1547[/C][C]0.1374[/C][C]0.1374[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=230571&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=230571&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
timeY[t]F[t]95% LB95% UBp-value(H0: Y[t] = F[t])P(F[t]>Y[t-1])P(F[t]>Y[t-s])P(F[t]>Y[60])
4869-------
4960-------
5056-------
5158-------
5250-------
5351-------
5453-------
5537-------
5622-------
5755-------
5870-------
5962-------
6058-------
61390-104.106104.1060.23140.13740.12930.1374
62490-104.106104.1060.17810.23140.14590.1374
63580-104.106104.1060.13740.17810.13740.1374
64470-104.106104.1060.18810.13740.17330.1374
65420-104.106104.1060.21450.18810.16850.1374
66620-104.106104.1060.12160.21450.15920.1374
67390-104.106104.1060.23140.12160.2430.1374
68400-104.106104.1060.22570.23140.33940.1374
69720-104.106104.1060.08760.22570.15020.1374
70700-104.106104.1060.09380.08760.09380.1374
71540-104.106104.1060.15470.09380.12160.1374
72650-104.106104.1060.11050.15470.13740.1374







Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPEsMAPESq.EMSERMSEScaledEMASE
61Inf1121521003.06433.0643
62Inf1122401196144.28323.853.4571
63Inf11233642428.666749.28154.55713.8238
64Inf11222092373.7548.72113.69293.7911
65Inf11217642251.847.45313.33.6929
66Inf11238442517.166750.17144.87143.8893
67Inf11215212374.857148.73253.06433.7714
68Inf1121600227847.72843.14293.6929
69Inf11251842600.888950.99895.65713.9111
70Inf11249002830.853.20535.54.07
71Inf11229162838.545553.2784.24294.0857
72Inf11242252954.083354.35155.10714.1708

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & sMAPE & Sq.E & MSE & RMSE & ScaledE & MASE \tabularnewline
61 & Inf & 1 & 1 & 2 & 1521 & 0 & 0 & 3.0643 & 3.0643 \tabularnewline
62 & Inf & 1 & 1 & 2 & 2401 & 1961 & 44.2832 & 3.85 & 3.4571 \tabularnewline
63 & Inf & 1 & 1 & 2 & 3364 & 2428.6667 & 49.2815 & 4.5571 & 3.8238 \tabularnewline
64 & Inf & 1 & 1 & 2 & 2209 & 2373.75 & 48.7211 & 3.6929 & 3.7911 \tabularnewline
65 & Inf & 1 & 1 & 2 & 1764 & 2251.8 & 47.4531 & 3.3 & 3.6929 \tabularnewline
66 & Inf & 1 & 1 & 2 & 3844 & 2517.1667 & 50.1714 & 4.8714 & 3.8893 \tabularnewline
67 & Inf & 1 & 1 & 2 & 1521 & 2374.8571 & 48.7325 & 3.0643 & 3.7714 \tabularnewline
68 & Inf & 1 & 1 & 2 & 1600 & 2278 & 47.7284 & 3.1429 & 3.6929 \tabularnewline
69 & Inf & 1 & 1 & 2 & 5184 & 2600.8889 & 50.9989 & 5.6571 & 3.9111 \tabularnewline
70 & Inf & 1 & 1 & 2 & 4900 & 2830.8 & 53.2053 & 5.5 & 4.07 \tabularnewline
71 & Inf & 1 & 1 & 2 & 2916 & 2838.5455 & 53.278 & 4.2429 & 4.0857 \tabularnewline
72 & Inf & 1 & 1 & 2 & 4225 & 2954.0833 & 54.3515 & 5.1071 & 4.1708 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=230571&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]sMAPE[/C][C]Sq.E[/C][C]MSE[/C][C]RMSE[/C][C]ScaledE[/C][C]MASE[/C][/ROW]
[ROW][C]61[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]1521[/C][C]0[/C][C]0[/C][C]3.0643[/C][C]3.0643[/C][/ROW]
[ROW][C]62[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]2401[/C][C]1961[/C][C]44.2832[/C][C]3.85[/C][C]3.4571[/C][/ROW]
[ROW][C]63[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]3364[/C][C]2428.6667[/C][C]49.2815[/C][C]4.5571[/C][C]3.8238[/C][/ROW]
[ROW][C]64[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]2209[/C][C]2373.75[/C][C]48.7211[/C][C]3.6929[/C][C]3.7911[/C][/ROW]
[ROW][C]65[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]1764[/C][C]2251.8[/C][C]47.4531[/C][C]3.3[/C][C]3.6929[/C][/ROW]
[ROW][C]66[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]3844[/C][C]2517.1667[/C][C]50.1714[/C][C]4.8714[/C][C]3.8893[/C][/ROW]
[ROW][C]67[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]1521[/C][C]2374.8571[/C][C]48.7325[/C][C]3.0643[/C][C]3.7714[/C][/ROW]
[ROW][C]68[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]1600[/C][C]2278[/C][C]47.7284[/C][C]3.1429[/C][C]3.6929[/C][/ROW]
[ROW][C]69[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]5184[/C][C]2600.8889[/C][C]50.9989[/C][C]5.6571[/C][C]3.9111[/C][/ROW]
[ROW][C]70[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]4900[/C][C]2830.8[/C][C]53.2053[/C][C]5.5[/C][C]4.07[/C][/ROW]
[ROW][C]71[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]2916[/C][C]2838.5455[/C][C]53.278[/C][C]4.2429[/C][C]4.0857[/C][/ROW]
[ROW][C]72[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]4225[/C][C]2954.0833[/C][C]54.3515[/C][C]5.1071[/C][C]4.1708[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=230571&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=230571&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.PEMAPEsMAPESq.EMSERMSEScaledEMASE
61Inf1121521003.06433.0643
62Inf1122401196144.28323.853.4571
63Inf11233642428.666749.28154.55713.8238
64Inf11222092373.7548.72113.69293.7911
65Inf11217642251.847.45313.33.6929
66Inf11238442517.166750.17144.87143.8893
67Inf11215212374.857148.73253.06433.7714
68Inf1121600227847.72843.14293.6929
69Inf11251842600.888950.99895.65713.9111
70Inf11249002830.853.20535.54.07
71Inf11229162838.545553.2784.24294.0857
72Inf11242252954.083354.35155.10714.1708



Parameters (Session):
par1 = 12 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = 0 ; par7 = 0 ; par8 = 0 ; par9 = 0 ; par10 = FALSE ;
Parameters (R input):
par1 = 12 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = 0 ; par7 = 0 ; par8 = 0 ; 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.spe <- array(0, dim=fx)
perf.scalederr <- array(0, dim=fx)
perf.mase <- array(0, dim=fx)
perf.mase1 <- array(0, dim=fx)
perf.mape <- array(0, dim=fx)
perf.smape <- array(0, dim=fx)
perf.mape1 <- array(0, dim=fx)
perf.smape1 <- 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)
perf.scaleddenom <- 0
for (i in 2:fx) {
perf.scaleddenom = perf.scaleddenom + abs(x[nx+i] - x[nx+i-1])
}
perf.scaleddenom = perf.scaleddenom / (fx-1)
for (i in 1:fx) {
locSD <- (ub[i] - forecast$pred[i]) / 1.96
perf.scalederr[i] = (x[nx+i] - forecast$pred[i]) / perf.scaleddenom
perf.pe[i] = (x[nx+i] - forecast$pred[i]) / x[nx+i]
perf.spe[i] = 2*(x[nx+i] - forecast$pred[i]) / (x[nx+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.smape[1] = abs(perf.spe[1])
perf.mape1[1] = perf.mape[1]
perf.smape1[1] = perf.smape[1]
perf.mse[1] = perf.se[1]
perf.mase[1] = abs(perf.scalederr[1])
perf.mase1[1] = perf.mase[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.smape[i] = perf.smape[i-1] + abs(perf.spe[i])
perf.smape1[i] = perf.smape[i] / i
perf.mse[i] = perf.mse[i-1] + perf.se[i]
perf.mse1[i] = perf.mse[i] / i
perf.mase[i] = perf.mase[i-1] + abs(perf.scalederr[i])
perf.mase1[i] = perf.mase[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',10,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,'sMAPE',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.element(a,'ScaledE',1,header=TRUE)
a<-table.element(a,'MASE',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.smape1[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.element(a,round(perf.scalederr[i],4))
a<-table.element(a,round(perf.mase1[i],4))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')