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 computationTue, 22 Dec 2009 11:20:51 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/22/t1261506141h62uj61n9lwld9s.htm/, Retrieved Thu, 31 Oct 2024 23:54:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=70471, Retrieved Thu, 31 Oct 2024 23:54:45 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact246
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [ARIMA Forecasting] [Paper: ARIMA: For...] [2009-12-22 18:20:51] [762da55b2e2304daaed24a7cc507d14d] [Current]
-   P     [ARIMA Forecasting] [] [2010-01-12 11:28:06] [3f4f41103e3954821da64ba9e2554371]
-   P     [ARIMA Forecasting] [] [2010-01-12 11:30:52] [f1bd7399181c649098ca7b814ee0e027]
-   P     [ARIMA Forecasting] [examenvraag3] [2010-01-26 08:41:15] [90e6802d28d0afa9b030a19cd25ed2b0]
- R P     [ARIMA Forecasting] [] [2010-01-26 08:41:41] [023d83ebdf42a2acf423907b4076e8a1]
-   P     [ARIMA Forecasting] [] [2010-01-26 08:42:02] [101f710c1bf3d900563184d79f7da6e1]
-   P     [ARIMA Forecasting] [exaam statistiek] [2010-01-26 09:00:52] [6e4e01d7eb22a9f33d58ebb35753a195]
Feedback Forum

Post a new message
Dataseries X:
90.2
90
88.8
85.8
84.2
80
77.8
76.8
86.4
89.2
86.2
84.6
83.2
83.2
82.6
79.8
77.2
74.8
73
73
83.6
85.6
84.8
84.2
83.4
84.6
84.6
83.8
81.2
79.6
78
78.2
88.8
92
91
91.2
90.4
91.8
92.2
90.2
88.6
87.8
86
87.2
97.6
101.2
100.4
100.2
100.2
103
104.2
104
102.4
101.8
101
102.2
114
118.4
118.8
117.2
117.2
118.4
118.8
117.2
114.4
112.6
111
110.8
120.2
124.4
123.4
121.2
119
119.8
120
118.4
115
113.4
111
111
121.6
126.2
125.8
124.8
122
123.2
124.2
120.8
116.8
114.8
111
109
119.8
124
121.6
118
115.8
116
115.8
114.4
112
110.2
107.4
108.2
117.6
121.4
119.8
115.6
112.6
113.2
112.2
110.8
108
105.2
102.4
101
110.8
116.8
113.8
108
104.4
105.2
105.4
103.2
100.6
97.8
95.8
95
104.8
110.4
106.4
102.2
98.4
98.4
98.6
96.2
92.4
91.4
88.4
87.8
97.6
104.2
100.2
97
92.8
92
93.4
92
89.6
88.6
87.2
86.2
96.8
102
102.6
100.6
94.2
94.2
95.2
95
94
92.2
91
91.2
103.4
105
104.6
103.8
101.8
102.4
103.8
103.4
102
101.8
100.2
101.4
113.8
116
115.6
113
109.4
111
112.4
112.2
111
108.8
107.4
108.6
118.8
122.2
122.6
122.2
118.8
119
118.2
117.8
116.8
114.6
113.4
113.8
124.2
125.8
125.6
122.4
119
119.4
118.6
118
116
114.8
114.6
114.6
124
125.2
124
117.6
113.2
111.4
112.2
109.8
106.4
105.2
102.2
99.8
111
113
108.4
105.4
102
102.8
103.4
101.6
98.6
98
93.8
95.6
105.6
106.8
103.6
101.2
100.4
103.2
105.6
106.6
107.2
107.4
104.8
107.2
117.4
119.4
116.2
112.8
111.6




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70471&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' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70471&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70471&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







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[229])
217113.2-------
218111.4-------
219112.2-------
220109.8-------
221106.4-------
222105.2-------
223102.2-------
22499.8-------
225111-------
226113-------
227108.4-------
228105.4-------
229102-------
230102.8100.907198.7558103.05840.04230.159700.1597
231103.4100.997597.8076104.18740.06990.13400.269
232101.698.716294.6315102.8010.08320.012300.0576
23398.695.531790.6132100.45010.11070.007800.005
2349893.666187.945799.38640.06880.045500.0021
23593.890.898284.394497.4020.19090.01623e-044e-04
23695.689.299182.023296.57490.04480.11270.00233e-04
237105.699.779491.7388107.820.0780.84580.00310.2941
238106.8102.154993.3547110.9550.15040.22140.00790.5138
239103.698.871989.3159108.4280.16610.0520.02530.2606
240101.295.735385.4264106.04420.14940.06740.03310.1168
241100.492.238481.1792103.29770.0740.05610.04180.0418
242103.291.569679.33103.80910.03130.07870.03610.0474
243105.691.477578.069104.8860.01950.04330.04070.062
244106.689.36174.792103.92990.01020.01450.04980.0445
245107.286.377870.655102.10050.00470.00590.06380.0257
246107.484.33567.4641101.20590.00370.00390.05620.0201
247104.881.767363.753399.78130.00610.00260.09520.0139
248107.280.605961.453699.75810.00320.00660.06250.0143
249117.490.874370.5884111.16020.00520.05740.07740.1412
250119.493.499472.0844114.91430.00890.01440.11170.2183
251116.290.862568.3233113.40170.01380.00650.1340.1664
252112.887.751164.0925111.40970.0190.00920.13260.1189
253111.684.293159.5202109.0660.01540.01210.10130.0806

\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[229]) \tabularnewline
217 & 113.2 & - & - & - & - & - & - & - \tabularnewline
218 & 111.4 & - & - & - & - & - & - & - \tabularnewline
219 & 112.2 & - & - & - & - & - & - & - \tabularnewline
220 & 109.8 & - & - & - & - & - & - & - \tabularnewline
221 & 106.4 & - & - & - & - & - & - & - \tabularnewline
222 & 105.2 & - & - & - & - & - & - & - \tabularnewline
223 & 102.2 & - & - & - & - & - & - & - \tabularnewline
224 & 99.8 & - & - & - & - & - & - & - \tabularnewline
225 & 111 & - & - & - & - & - & - & - \tabularnewline
226 & 113 & - & - & - & - & - & - & - \tabularnewline
227 & 108.4 & - & - & - & - & - & - & - \tabularnewline
228 & 105.4 & - & - & - & - & - & - & - \tabularnewline
229 & 102 & - & - & - & - & - & - & - \tabularnewline
230 & 102.8 & 100.9071 & 98.7558 & 103.0584 & 0.0423 & 0.1597 & 0 & 0.1597 \tabularnewline
231 & 103.4 & 100.9975 & 97.8076 & 104.1874 & 0.0699 & 0.134 & 0 & 0.269 \tabularnewline
232 & 101.6 & 98.7162 & 94.6315 & 102.801 & 0.0832 & 0.0123 & 0 & 0.0576 \tabularnewline
233 & 98.6 & 95.5317 & 90.6132 & 100.4501 & 0.1107 & 0.0078 & 0 & 0.005 \tabularnewline
234 & 98 & 93.6661 & 87.9457 & 99.3864 & 0.0688 & 0.0455 & 0 & 0.0021 \tabularnewline
235 & 93.8 & 90.8982 & 84.3944 & 97.402 & 0.1909 & 0.0162 & 3e-04 & 4e-04 \tabularnewline
236 & 95.6 & 89.2991 & 82.0232 & 96.5749 & 0.0448 & 0.1127 & 0.0023 & 3e-04 \tabularnewline
237 & 105.6 & 99.7794 & 91.7388 & 107.82 & 0.078 & 0.8458 & 0.0031 & 0.2941 \tabularnewline
238 & 106.8 & 102.1549 & 93.3547 & 110.955 & 0.1504 & 0.2214 & 0.0079 & 0.5138 \tabularnewline
239 & 103.6 & 98.8719 & 89.3159 & 108.428 & 0.1661 & 0.052 & 0.0253 & 0.2606 \tabularnewline
240 & 101.2 & 95.7353 & 85.4264 & 106.0442 & 0.1494 & 0.0674 & 0.0331 & 0.1168 \tabularnewline
241 & 100.4 & 92.2384 & 81.1792 & 103.2977 & 0.074 & 0.0561 & 0.0418 & 0.0418 \tabularnewline
242 & 103.2 & 91.5696 & 79.33 & 103.8091 & 0.0313 & 0.0787 & 0.0361 & 0.0474 \tabularnewline
243 & 105.6 & 91.4775 & 78.069 & 104.886 & 0.0195 & 0.0433 & 0.0407 & 0.062 \tabularnewline
244 & 106.6 & 89.361 & 74.792 & 103.9299 & 0.0102 & 0.0145 & 0.0498 & 0.0445 \tabularnewline
245 & 107.2 & 86.3778 & 70.655 & 102.1005 & 0.0047 & 0.0059 & 0.0638 & 0.0257 \tabularnewline
246 & 107.4 & 84.335 & 67.4641 & 101.2059 & 0.0037 & 0.0039 & 0.0562 & 0.0201 \tabularnewline
247 & 104.8 & 81.7673 & 63.7533 & 99.7813 & 0.0061 & 0.0026 & 0.0952 & 0.0139 \tabularnewline
248 & 107.2 & 80.6059 & 61.4536 & 99.7581 & 0.0032 & 0.0066 & 0.0625 & 0.0143 \tabularnewline
249 & 117.4 & 90.8743 & 70.5884 & 111.1602 & 0.0052 & 0.0574 & 0.0774 & 0.1412 \tabularnewline
250 & 119.4 & 93.4994 & 72.0844 & 114.9143 & 0.0089 & 0.0144 & 0.1117 & 0.2183 \tabularnewline
251 & 116.2 & 90.8625 & 68.3233 & 113.4017 & 0.0138 & 0.0065 & 0.134 & 0.1664 \tabularnewline
252 & 112.8 & 87.7511 & 64.0925 & 111.4097 & 0.019 & 0.0092 & 0.1326 & 0.1189 \tabularnewline
253 & 111.6 & 84.2931 & 59.5202 & 109.066 & 0.0154 & 0.0121 & 0.1013 & 0.0806 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70471&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[229])[/C][/ROW]
[ROW][C]217[/C][C]113.2[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]218[/C][C]111.4[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]219[/C][C]112.2[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]220[/C][C]109.8[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]221[/C][C]106.4[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]222[/C][C]105.2[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]223[/C][C]102.2[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]224[/C][C]99.8[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]225[/C][C]111[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]226[/C][C]113[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]227[/C][C]108.4[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]228[/C][C]105.4[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]229[/C][C]102[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]230[/C][C]102.8[/C][C]100.9071[/C][C]98.7558[/C][C]103.0584[/C][C]0.0423[/C][C]0.1597[/C][C]0[/C][C]0.1597[/C][/ROW]
[ROW][C]231[/C][C]103.4[/C][C]100.9975[/C][C]97.8076[/C][C]104.1874[/C][C]0.0699[/C][C]0.134[/C][C]0[/C][C]0.269[/C][/ROW]
[ROW][C]232[/C][C]101.6[/C][C]98.7162[/C][C]94.6315[/C][C]102.801[/C][C]0.0832[/C][C]0.0123[/C][C]0[/C][C]0.0576[/C][/ROW]
[ROW][C]233[/C][C]98.6[/C][C]95.5317[/C][C]90.6132[/C][C]100.4501[/C][C]0.1107[/C][C]0.0078[/C][C]0[/C][C]0.005[/C][/ROW]
[ROW][C]234[/C][C]98[/C][C]93.6661[/C][C]87.9457[/C][C]99.3864[/C][C]0.0688[/C][C]0.0455[/C][C]0[/C][C]0.0021[/C][/ROW]
[ROW][C]235[/C][C]93.8[/C][C]90.8982[/C][C]84.3944[/C][C]97.402[/C][C]0.1909[/C][C]0.0162[/C][C]3e-04[/C][C]4e-04[/C][/ROW]
[ROW][C]236[/C][C]95.6[/C][C]89.2991[/C][C]82.0232[/C][C]96.5749[/C][C]0.0448[/C][C]0.1127[/C][C]0.0023[/C][C]3e-04[/C][/ROW]
[ROW][C]237[/C][C]105.6[/C][C]99.7794[/C][C]91.7388[/C][C]107.82[/C][C]0.078[/C][C]0.8458[/C][C]0.0031[/C][C]0.2941[/C][/ROW]
[ROW][C]238[/C][C]106.8[/C][C]102.1549[/C][C]93.3547[/C][C]110.955[/C][C]0.1504[/C][C]0.2214[/C][C]0.0079[/C][C]0.5138[/C][/ROW]
[ROW][C]239[/C][C]103.6[/C][C]98.8719[/C][C]89.3159[/C][C]108.428[/C][C]0.1661[/C][C]0.052[/C][C]0.0253[/C][C]0.2606[/C][/ROW]
[ROW][C]240[/C][C]101.2[/C][C]95.7353[/C][C]85.4264[/C][C]106.0442[/C][C]0.1494[/C][C]0.0674[/C][C]0.0331[/C][C]0.1168[/C][/ROW]
[ROW][C]241[/C][C]100.4[/C][C]92.2384[/C][C]81.1792[/C][C]103.2977[/C][C]0.074[/C][C]0.0561[/C][C]0.0418[/C][C]0.0418[/C][/ROW]
[ROW][C]242[/C][C]103.2[/C][C]91.5696[/C][C]79.33[/C][C]103.8091[/C][C]0.0313[/C][C]0.0787[/C][C]0.0361[/C][C]0.0474[/C][/ROW]
[ROW][C]243[/C][C]105.6[/C][C]91.4775[/C][C]78.069[/C][C]104.886[/C][C]0.0195[/C][C]0.0433[/C][C]0.0407[/C][C]0.062[/C][/ROW]
[ROW][C]244[/C][C]106.6[/C][C]89.361[/C][C]74.792[/C][C]103.9299[/C][C]0.0102[/C][C]0.0145[/C][C]0.0498[/C][C]0.0445[/C][/ROW]
[ROW][C]245[/C][C]107.2[/C][C]86.3778[/C][C]70.655[/C][C]102.1005[/C][C]0.0047[/C][C]0.0059[/C][C]0.0638[/C][C]0.0257[/C][/ROW]
[ROW][C]246[/C][C]107.4[/C][C]84.335[/C][C]67.4641[/C][C]101.2059[/C][C]0.0037[/C][C]0.0039[/C][C]0.0562[/C][C]0.0201[/C][/ROW]
[ROW][C]247[/C][C]104.8[/C][C]81.7673[/C][C]63.7533[/C][C]99.7813[/C][C]0.0061[/C][C]0.0026[/C][C]0.0952[/C][C]0.0139[/C][/ROW]
[ROW][C]248[/C][C]107.2[/C][C]80.6059[/C][C]61.4536[/C][C]99.7581[/C][C]0.0032[/C][C]0.0066[/C][C]0.0625[/C][C]0.0143[/C][/ROW]
[ROW][C]249[/C][C]117.4[/C][C]90.8743[/C][C]70.5884[/C][C]111.1602[/C][C]0.0052[/C][C]0.0574[/C][C]0.0774[/C][C]0.1412[/C][/ROW]
[ROW][C]250[/C][C]119.4[/C][C]93.4994[/C][C]72.0844[/C][C]114.9143[/C][C]0.0089[/C][C]0.0144[/C][C]0.1117[/C][C]0.2183[/C][/ROW]
[ROW][C]251[/C][C]116.2[/C][C]90.8625[/C][C]68.3233[/C][C]113.4017[/C][C]0.0138[/C][C]0.0065[/C][C]0.134[/C][C]0.1664[/C][/ROW]
[ROW][C]252[/C][C]112.8[/C][C]87.7511[/C][C]64.0925[/C][C]111.4097[/C][C]0.019[/C][C]0.0092[/C][C]0.1326[/C][C]0.1189[/C][/ROW]
[ROW][C]253[/C][C]111.6[/C][C]84.2931[/C][C]59.5202[/C][C]109.066[/C][C]0.0154[/C][C]0.0121[/C][C]0.1013[/C][C]0.0806[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70471&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70471&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[229])
217113.2-------
218111.4-------
219112.2-------
220109.8-------
221106.4-------
222105.2-------
223102.2-------
22499.8-------
225111-------
226113-------
227108.4-------
228105.4-------
229102-------
230102.8100.907198.7558103.05840.04230.159700.1597
231103.4100.997597.8076104.18740.06990.13400.269
232101.698.716294.6315102.8010.08320.012300.0576
23398.695.531790.6132100.45010.11070.007800.005
2349893.666187.945799.38640.06880.045500.0021
23593.890.898284.394497.4020.19090.01623e-044e-04
23695.689.299182.023296.57490.04480.11270.00233e-04
237105.699.779491.7388107.820.0780.84580.00310.2941
238106.8102.154993.3547110.9550.15040.22140.00790.5138
239103.698.871989.3159108.4280.16610.0520.02530.2606
240101.295.735385.4264106.04420.14940.06740.03310.1168
241100.492.238481.1792103.29770.0740.05610.04180.0418
242103.291.569679.33103.80910.03130.07870.03610.0474
243105.691.477578.069104.8860.01950.04330.04070.062
244106.689.36174.792103.92990.01020.01450.04980.0445
245107.286.377870.655102.10050.00470.00590.06380.0257
246107.484.33567.4641101.20590.00370.00390.05620.0201
247104.881.767363.753399.78130.00610.00260.09520.0139
248107.280.605961.453699.75810.00320.00660.06250.0143
249117.490.874370.5884111.16020.00520.05740.07740.1412
250119.493.499472.0844114.91430.00890.01440.11170.2183
251116.290.862568.3233113.40170.01380.00650.1340.1664
252112.887.751164.0925111.40970.0190.00920.13260.1189
253111.684.293159.5202109.0660.01540.01210.10130.0806







Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
2300.01090.018803.583100
2310.01610.02380.02135.7724.67752.1628
2320.02110.02920.02398.31615.89042.427
2330.02630.03210.0269.41466.77152.6022
2340.03120.04630.0318.78319.17383.0288
2350.03650.03190.03038.42049.04823.008
2360.04160.07060.036139.701713.42733.6643
2370.04110.05830.038933.879415.98383.998
2380.0440.04550.039621.577316.60534.075
2390.04930.04780.040422.354617.18024.1449
2400.05490.05710.041929.86318.33324.2817
2410.06120.08850.045866.611122.35644.7283
2420.06820.1270.0521135.267231.04185.5715
2430.07480.15440.0594199.444843.07066.5628
2440.08320.19290.0683297.184860.01167.7467
2450.09290.24110.0791433.565483.35879.1301
2460.10210.27350.0905531.9926109.748910.4761
2470.11240.28170.1011530.5055133.124311.5379
2480.12120.32990.1132707.2482163.341312.7805
2490.11390.29190.1221703.6123190.354913.7969
2500.11690.2770.1295670.8422213.235214.6026
2510.12660.27890.1363641.9907232.724115.2553
2520.13760.28550.1428627.4488249.886115.8078
2530.14990.3240.1503745.666270.543516.4482

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
230 & 0.0109 & 0.0188 & 0 & 3.5831 & 0 & 0 \tabularnewline
231 & 0.0161 & 0.0238 & 0.0213 & 5.772 & 4.6775 & 2.1628 \tabularnewline
232 & 0.0211 & 0.0292 & 0.0239 & 8.3161 & 5.8904 & 2.427 \tabularnewline
233 & 0.0263 & 0.0321 & 0.026 & 9.4146 & 6.7715 & 2.6022 \tabularnewline
234 & 0.0312 & 0.0463 & 0.03 & 18.7831 & 9.1738 & 3.0288 \tabularnewline
235 & 0.0365 & 0.0319 & 0.0303 & 8.4204 & 9.0482 & 3.008 \tabularnewline
236 & 0.0416 & 0.0706 & 0.0361 & 39.7017 & 13.4273 & 3.6643 \tabularnewline
237 & 0.0411 & 0.0583 & 0.0389 & 33.8794 & 15.9838 & 3.998 \tabularnewline
238 & 0.044 & 0.0455 & 0.0396 & 21.5773 & 16.6053 & 4.075 \tabularnewline
239 & 0.0493 & 0.0478 & 0.0404 & 22.3546 & 17.1802 & 4.1449 \tabularnewline
240 & 0.0549 & 0.0571 & 0.0419 & 29.863 & 18.3332 & 4.2817 \tabularnewline
241 & 0.0612 & 0.0885 & 0.0458 & 66.6111 & 22.3564 & 4.7283 \tabularnewline
242 & 0.0682 & 0.127 & 0.0521 & 135.2672 & 31.0418 & 5.5715 \tabularnewline
243 & 0.0748 & 0.1544 & 0.0594 & 199.4448 & 43.0706 & 6.5628 \tabularnewline
244 & 0.0832 & 0.1929 & 0.0683 & 297.1848 & 60.0116 & 7.7467 \tabularnewline
245 & 0.0929 & 0.2411 & 0.0791 & 433.5654 & 83.3587 & 9.1301 \tabularnewline
246 & 0.1021 & 0.2735 & 0.0905 & 531.9926 & 109.7489 & 10.4761 \tabularnewline
247 & 0.1124 & 0.2817 & 0.1011 & 530.5055 & 133.1243 & 11.5379 \tabularnewline
248 & 0.1212 & 0.3299 & 0.1132 & 707.2482 & 163.3413 & 12.7805 \tabularnewline
249 & 0.1139 & 0.2919 & 0.1221 & 703.6123 & 190.3549 & 13.7969 \tabularnewline
250 & 0.1169 & 0.277 & 0.1295 & 670.8422 & 213.2352 & 14.6026 \tabularnewline
251 & 0.1266 & 0.2789 & 0.1363 & 641.9907 & 232.7241 & 15.2553 \tabularnewline
252 & 0.1376 & 0.2855 & 0.1428 & 627.4488 & 249.8861 & 15.8078 \tabularnewline
253 & 0.1499 & 0.324 & 0.1503 & 745.666 & 270.5435 & 16.4482 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70471&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]230[/C][C]0.0109[/C][C]0.0188[/C][C]0[/C][C]3.5831[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]231[/C][C]0.0161[/C][C]0.0238[/C][C]0.0213[/C][C]5.772[/C][C]4.6775[/C][C]2.1628[/C][/ROW]
[ROW][C]232[/C][C]0.0211[/C][C]0.0292[/C][C]0.0239[/C][C]8.3161[/C][C]5.8904[/C][C]2.427[/C][/ROW]
[ROW][C]233[/C][C]0.0263[/C][C]0.0321[/C][C]0.026[/C][C]9.4146[/C][C]6.7715[/C][C]2.6022[/C][/ROW]
[ROW][C]234[/C][C]0.0312[/C][C]0.0463[/C][C]0.03[/C][C]18.7831[/C][C]9.1738[/C][C]3.0288[/C][/ROW]
[ROW][C]235[/C][C]0.0365[/C][C]0.0319[/C][C]0.0303[/C][C]8.4204[/C][C]9.0482[/C][C]3.008[/C][/ROW]
[ROW][C]236[/C][C]0.0416[/C][C]0.0706[/C][C]0.0361[/C][C]39.7017[/C][C]13.4273[/C][C]3.6643[/C][/ROW]
[ROW][C]237[/C][C]0.0411[/C][C]0.0583[/C][C]0.0389[/C][C]33.8794[/C][C]15.9838[/C][C]3.998[/C][/ROW]
[ROW][C]238[/C][C]0.044[/C][C]0.0455[/C][C]0.0396[/C][C]21.5773[/C][C]16.6053[/C][C]4.075[/C][/ROW]
[ROW][C]239[/C][C]0.0493[/C][C]0.0478[/C][C]0.0404[/C][C]22.3546[/C][C]17.1802[/C][C]4.1449[/C][/ROW]
[ROW][C]240[/C][C]0.0549[/C][C]0.0571[/C][C]0.0419[/C][C]29.863[/C][C]18.3332[/C][C]4.2817[/C][/ROW]
[ROW][C]241[/C][C]0.0612[/C][C]0.0885[/C][C]0.0458[/C][C]66.6111[/C][C]22.3564[/C][C]4.7283[/C][/ROW]
[ROW][C]242[/C][C]0.0682[/C][C]0.127[/C][C]0.0521[/C][C]135.2672[/C][C]31.0418[/C][C]5.5715[/C][/ROW]
[ROW][C]243[/C][C]0.0748[/C][C]0.1544[/C][C]0.0594[/C][C]199.4448[/C][C]43.0706[/C][C]6.5628[/C][/ROW]
[ROW][C]244[/C][C]0.0832[/C][C]0.1929[/C][C]0.0683[/C][C]297.1848[/C][C]60.0116[/C][C]7.7467[/C][/ROW]
[ROW][C]245[/C][C]0.0929[/C][C]0.2411[/C][C]0.0791[/C][C]433.5654[/C][C]83.3587[/C][C]9.1301[/C][/ROW]
[ROW][C]246[/C][C]0.1021[/C][C]0.2735[/C][C]0.0905[/C][C]531.9926[/C][C]109.7489[/C][C]10.4761[/C][/ROW]
[ROW][C]247[/C][C]0.1124[/C][C]0.2817[/C][C]0.1011[/C][C]530.5055[/C][C]133.1243[/C][C]11.5379[/C][/ROW]
[ROW][C]248[/C][C]0.1212[/C][C]0.3299[/C][C]0.1132[/C][C]707.2482[/C][C]163.3413[/C][C]12.7805[/C][/ROW]
[ROW][C]249[/C][C]0.1139[/C][C]0.2919[/C][C]0.1221[/C][C]703.6123[/C][C]190.3549[/C][C]13.7969[/C][/ROW]
[ROW][C]250[/C][C]0.1169[/C][C]0.277[/C][C]0.1295[/C][C]670.8422[/C][C]213.2352[/C][C]14.6026[/C][/ROW]
[ROW][C]251[/C][C]0.1266[/C][C]0.2789[/C][C]0.1363[/C][C]641.9907[/C][C]232.7241[/C][C]15.2553[/C][/ROW]
[ROW][C]252[/C][C]0.1376[/C][C]0.2855[/C][C]0.1428[/C][C]627.4488[/C][C]249.8861[/C][C]15.8078[/C][/ROW]
[ROW][C]253[/C][C]0.1499[/C][C]0.324[/C][C]0.1503[/C][C]745.666[/C][C]270.5435[/C][C]16.4482[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70471&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70471&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
2300.01090.018803.583100
2310.01610.02380.02135.7724.67752.1628
2320.02110.02920.02398.31615.89042.427
2330.02630.03210.0269.41466.77152.6022
2340.03120.04630.0318.78319.17383.0288
2350.03650.03190.03038.42049.04823.008
2360.04160.07060.036139.701713.42733.6643
2370.04110.05830.038933.879415.98383.998
2380.0440.04550.039621.577316.60534.075
2390.04930.04780.040422.354617.18024.1449
2400.05490.05710.041929.86318.33324.2817
2410.06120.08850.045866.611122.35644.7283
2420.06820.1270.0521135.267231.04185.5715
2430.07480.15440.0594199.444843.07066.5628
2440.08320.19290.0683297.184860.01167.7467
2450.09290.24110.0791433.565483.35879.1301
2460.10210.27350.0905531.9926109.748910.4761
2470.11240.28170.1011530.5055133.124311.5379
2480.12120.32990.1132707.2482163.341312.7805
2490.11390.29190.1221703.6123190.354913.7969
2500.11690.2770.1295670.8422213.235214.6026
2510.12660.27890.1363641.9907232.724115.2553
2520.13760.28550.1428627.4488249.886115.8078
2530.14990.3240.1503745.666270.543516.4482



Parameters (Session):
par1 = 24 ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 1 ; par7 = 1 ; par8 = 1 ; par9 = 1 ; par10 = FALSE ;
Parameters (R input):
par1 = 24 ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 1 ; par7 = 1 ; par8 = 1 ; 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')