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Author*The author of this computation has been verified*
R Software Modulerwasp_arimaforecasting.wasp
Title produced by softwareARIMA Forecasting
Date of computationTue, 04 Dec 2012 14:16:28 -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/2012/Dec/04/t13546486070skabhxauxesaxi.htm/, Retrieved Fri, 29 Mar 2024 10:46:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=196533, Retrieved Fri, 29 Mar 2024 10:46:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact97
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [ARIMA Forecasting] [ARIMA2] [2012-11-30 14:26:31] [3dc52aaca1c2323e282536a0c7c26bc2]
- R P     [ARIMA Forecasting] [ARIMA2] [2012-12-04 19:16:28] [2f047a68beb18e789d06219c4ebd4599] [Current]
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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 time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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 & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=196533&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]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=196533&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196533&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 time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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-------
613953.001235.771870.23060.05560.28480.2130.2848
624952.400135.170669.62950.34950.93630.34110.262
635865.799148.569783.02850.18750.9720.81250.8125
644750.799933.570568.02930.33280.20640.53630.2064
654251.199333.969968.42870.14770.68360.5090.2196
666259.199741.970376.42920.3750.97480.75970.5543
673938.799121.569756.02860.49090.00420.58110.0145
684028.399411.1745.62890.09350.11390.76674e-04
697254.799537.570172.02890.02520.95390.49090.3579
707062.600345.370979.82980.20.14250.20.6996
715448.400931.171465.63030.26210.0070.06090.1374
726555.400638.171272.630.13740.56330.38370.3837

\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 & 53.0012 & 35.7718 & 70.2306 & 0.0556 & 0.2848 & 0.213 & 0.2848 \tabularnewline
62 & 49 & 52.4001 & 35.1706 & 69.6295 & 0.3495 & 0.9363 & 0.3411 & 0.262 \tabularnewline
63 & 58 & 65.7991 & 48.5697 & 83.0285 & 0.1875 & 0.972 & 0.8125 & 0.8125 \tabularnewline
64 & 47 & 50.7999 & 33.5705 & 68.0293 & 0.3328 & 0.2064 & 0.5363 & 0.2064 \tabularnewline
65 & 42 & 51.1993 & 33.9699 & 68.4287 & 0.1477 & 0.6836 & 0.509 & 0.2196 \tabularnewline
66 & 62 & 59.1997 & 41.9703 & 76.4292 & 0.375 & 0.9748 & 0.7597 & 0.5543 \tabularnewline
67 & 39 & 38.7991 & 21.5697 & 56.0286 & 0.4909 & 0.0042 & 0.5811 & 0.0145 \tabularnewline
68 & 40 & 28.3994 & 11.17 & 45.6289 & 0.0935 & 0.1139 & 0.7667 & 4e-04 \tabularnewline
69 & 72 & 54.7995 & 37.5701 & 72.0289 & 0.0252 & 0.9539 & 0.4909 & 0.3579 \tabularnewline
70 & 70 & 62.6003 & 45.3709 & 79.8298 & 0.2 & 0.1425 & 0.2 & 0.6996 \tabularnewline
71 & 54 & 48.4009 & 31.1714 & 65.6303 & 0.2621 & 0.007 & 0.0609 & 0.1374 \tabularnewline
72 & 65 & 55.4006 & 38.1712 & 72.63 & 0.1374 & 0.5633 & 0.3837 & 0.3837 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=196533&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]53.0012[/C][C]35.7718[/C][C]70.2306[/C][C]0.0556[/C][C]0.2848[/C][C]0.213[/C][C]0.2848[/C][/ROW]
[ROW][C]62[/C][C]49[/C][C]52.4001[/C][C]35.1706[/C][C]69.6295[/C][C]0.3495[/C][C]0.9363[/C][C]0.3411[/C][C]0.262[/C][/ROW]
[ROW][C]63[/C][C]58[/C][C]65.7991[/C][C]48.5697[/C][C]83.0285[/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]50.7999[/C][C]33.5705[/C][C]68.0293[/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]51.1993[/C][C]33.9699[/C][C]68.4287[/C][C]0.1477[/C][C]0.6836[/C][C]0.509[/C][C]0.2196[/C][/ROW]
[ROW][C]66[/C][C]62[/C][C]59.1997[/C][C]41.9703[/C][C]76.4292[/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]38.7991[/C][C]21.5697[/C][C]56.0286[/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]28.3994[/C][C]11.17[/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]54.7995[/C][C]37.5701[/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]62.6003[/C][C]45.3709[/C][C]79.8298[/C][C]0.2[/C][C]0.1425[/C][C]0.2[/C][C]0.6996[/C][/ROW]
[ROW][C]71[/C][C]54[/C][C]48.4009[/C][C]31.1714[/C][C]65.6303[/C][C]0.2621[/C][C]0.007[/C][C]0.0609[/C][C]0.1374[/C][/ROW]
[ROW][C]72[/C][C]65[/C][C]55.4006[/C][C]38.1712[/C][C]72.63[/C][C]0.1374[/C][C]0.5633[/C][C]0.3837[/C][C]0.3837[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=196533&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196533&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-------
613953.001235.771870.23060.05560.28480.2130.2848
624952.400135.170669.62950.34950.93630.34110.262
635865.799148.569783.02850.18750.9720.81250.8125
644750.799933.570568.02930.33280.20640.53630.2064
654251.199333.969968.42870.14770.68360.5090.2196
666259.199741.970376.42920.3750.97480.75970.5543
673938.799121.569756.02860.49090.00420.58110.0145
684028.399411.1745.62890.09350.11390.76674e-04
697254.799537.570172.02890.02520.95390.49090.3579
707062.600345.370979.82980.20.14250.20.6996
715448.400931.171465.63030.26210.0070.06090.1374
726555.400638.171272.630.13740.56330.38370.3837







Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
610.1659-0.26420196.033400
620.1678-0.06490.164511.5604103.796910.1881
630.1336-0.11850.149260.825889.47329.459
640.173-0.07480.130614.439570.71488.4092
650.1717-0.17970.140484.627673.49738.5731
660.14850.04730.12497.841462.55477.9092
670.22660.00520.10780.040353.62417.3228
680.30950.40850.1454134.573163.74277.9839
690.16040.31390.1641295.857989.53339.4622
700.14040.11820.159554.755186.05559.2766
710.18160.11570.155531.350381.08239.0046
720.15870.17330.15792.149182.00459.0556

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
61 & 0.1659 & -0.2642 & 0 & 196.0334 & 0 & 0 \tabularnewline
62 & 0.1678 & -0.0649 & 0.1645 & 11.5604 & 103.7969 & 10.1881 \tabularnewline
63 & 0.1336 & -0.1185 & 0.1492 & 60.8258 & 89.4732 & 9.459 \tabularnewline
64 & 0.173 & -0.0748 & 0.1306 & 14.4395 & 70.7148 & 8.4092 \tabularnewline
65 & 0.1717 & -0.1797 & 0.1404 & 84.6276 & 73.4973 & 8.5731 \tabularnewline
66 & 0.1485 & 0.0473 & 0.1249 & 7.8414 & 62.5547 & 7.9092 \tabularnewline
67 & 0.2266 & 0.0052 & 0.1078 & 0.0403 & 53.6241 & 7.3228 \tabularnewline
68 & 0.3095 & 0.4085 & 0.1454 & 134.5731 & 63.7427 & 7.9839 \tabularnewline
69 & 0.1604 & 0.3139 & 0.1641 & 295.8579 & 89.5333 & 9.4622 \tabularnewline
70 & 0.1404 & 0.1182 & 0.1595 & 54.7551 & 86.0555 & 9.2766 \tabularnewline
71 & 0.1816 & 0.1157 & 0.1555 & 31.3503 & 81.0823 & 9.0046 \tabularnewline
72 & 0.1587 & 0.1733 & 0.157 & 92.1491 & 82.0045 & 9.0556 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=196533&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]0.1659[/C][C]-0.2642[/C][C]0[/C][C]196.0334[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]62[/C][C]0.1678[/C][C]-0.0649[/C][C]0.1645[/C][C]11.5604[/C][C]103.7969[/C][C]10.1881[/C][/ROW]
[ROW][C]63[/C][C]0.1336[/C][C]-0.1185[/C][C]0.1492[/C][C]60.8258[/C][C]89.4732[/C][C]9.459[/C][/ROW]
[ROW][C]64[/C][C]0.173[/C][C]-0.0748[/C][C]0.1306[/C][C]14.4395[/C][C]70.7148[/C][C]8.4092[/C][/ROW]
[ROW][C]65[/C][C]0.1717[/C][C]-0.1797[/C][C]0.1404[/C][C]84.6276[/C][C]73.4973[/C][C]8.5731[/C][/ROW]
[ROW][C]66[/C][C]0.1485[/C][C]0.0473[/C][C]0.1249[/C][C]7.8414[/C][C]62.5547[/C][C]7.9092[/C][/ROW]
[ROW][C]67[/C][C]0.2266[/C][C]0.0052[/C][C]0.1078[/C][C]0.0403[/C][C]53.6241[/C][C]7.3228[/C][/ROW]
[ROW][C]68[/C][C]0.3095[/C][C]0.4085[/C][C]0.1454[/C][C]134.5731[/C][C]63.7427[/C][C]7.9839[/C][/ROW]
[ROW][C]69[/C][C]0.1604[/C][C]0.3139[/C][C]0.1641[/C][C]295.8579[/C][C]89.5333[/C][C]9.4622[/C][/ROW]
[ROW][C]70[/C][C]0.1404[/C][C]0.1182[/C][C]0.1595[/C][C]54.7551[/C][C]86.0555[/C][C]9.2766[/C][/ROW]
[ROW][C]71[/C][C]0.1816[/C][C]0.1157[/C][C]0.1555[/C][C]31.3503[/C][C]81.0823[/C][C]9.0046[/C][/ROW]
[ROW][C]72[/C][C]0.1587[/C][C]0.1733[/C][C]0.157[/C][C]92.1491[/C][C]82.0045[/C][C]9.0556[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=196533&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196533&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.PEMAPESq.EMSERMSE
610.1659-0.26420196.033400
620.1678-0.06490.164511.5604103.796910.1881
630.1336-0.11850.149260.825889.47329.459
640.173-0.07480.130614.439570.71488.4092
650.1717-0.17970.140484.627673.49738.5731
660.14850.04730.12497.841462.55477.9092
670.22660.00520.10780.040353.62417.3228
680.30950.40850.1454134.573163.74277.9839
690.16040.31390.1641295.857989.53339.4622
700.14040.11820.159554.755186.05559.2766
710.18160.11570.155531.350381.08239.0046
720.15870.17330.15792.149182.00459.0556



Parameters (Session):
par1 = 12 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 0 ; par7 = 0 ; par8 = 0 ; par9 = 1 ; par10 = FALSE ;
Parameters (R input):
par1 = 12 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; 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')