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R Software Module

rwasp_arimaforecasting.wasp

Title produced by software

ARIMA Forecasting

Date of computation

Tue, 07 Dec 2010 16:19:30 +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/07/t1291738671zmgvfpmwpev4b5y.htm/, Retrieved Mon, 29 Apr 2024 13:38:23 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=106473, Retrieved Mon, 29 Apr 2024 13:38:23 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

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

-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Standard Deviation-Mean Plot] [Unemployment] [2010-11-29 10:34:47] [b98453cac15ba1066b407e146608df68]
- RMP     [ARIMA Forecasting] [Unemployment] [2010-11-29 20:46:45] [b98453cac15ba1066b407e146608df68]
- R PD        [ARIMA Forecasting] [WS9 5 Arima s24] [2010-12-07 16:19:30] [aa6b599ccd367bc74fed0d8f67004a46] [Current]
- R PD          [ARIMA Forecasting] [] [2011-12-06 21:27:02] [e21b9c93af4eb9605ecfaf58a559e5ab] 
- R P           [ARIMA Forecasting] [] [2012-12-04 16:49:49] [74be16979710d4c4e7c6647856088456] 
-   P             [ARIMA Forecasting] [Verbeterde versie...] [2012-12-06 15:37:06] [b43eb6e2e60f3928e6b8367ff6c5b484] 
- R P           [ARIMA Forecasting] [WS 9 - ARIMA] [2012-12-04 19:05:50] [74be16979710d4c4e7c6647856088456] 
- RMP             [ARIMA Forecasting] [] [2012-12-04 20:08:33] [74be16979710d4c4e7c6647856088456] 

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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	3 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=106473&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=106473&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=106473&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	3 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

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[72])
60	9451	-	-	-	-	-	-	-
61	10041	-	-	-	-	-	-	-
62	9411	-	-	-	-	-	-	-
63	10405	-	-	-	-	-	-	-
64	8467	-	-	-	-	-	-	-
65	8464	-	-	-	-	-	-	-
66	8102	-	-	-	-	-	-	-
67	7627	-	-	-	-	-	-	-
68	7513	-	-	-	-	-	-	-
69	7510	-	-	-	-	-	-	-
70	8291	-	-	-	-	-	-	-
71	8064	-	-	-	-	-	-	-
72	9383	-	-	-	-	-	-	-
73	9706	9615.146	8752.2365	10478.0556	0.4183	0.701	0.1667	0.701
74	8579	8305.4224	7274.1581	9336.6868	0.3015	0.0039	0.0178	0.0203
75	9474	8919.1709	7737.2503	10101.0915	0.1788	0.7137	0.0069	0.2209
76	8318	7641.8884	6326.6928	8957.0839	0.1568	0.0032	0.1094	0.0047
77	8213	7579.7396	6143.5749	9015.9043	0.1937	0.1568	0.1138	0.0069
78	8059	7331.3048	5783.5975	8879.0121	0.1784	0.1321	0.1645	0.0047
79	9111	7201.9869	5550.2525	8853.7213	0.0117	0.1546	0.307	0.0048
80	7708	6917.4714	5167.8843	8667.0586	0.1879	0.007	0.2523	0.0029
81	7680	6927.5635	5085.3139	8769.8132	0.2117	0.2032	0.2677	0.0045
82	8014	7687.2858	5756.8163	9617.7553	0.3701	0.503	0.27	0.0426
83	8007	7447.9014	5433.071	9462.7317	0.2933	0.2909	0.2745	0.0299
84	8718	8643.4507	6547.6525	10739.2489	0.4722	0.7241	0.2446	0.2446
85	9486	9155.0625	6832.264	11477.861	0.39	0.6439	0.321	0.4237
86	9113	8389.0225	5913.9402	10864.1048	0.2832	0.1925	0.4402	0.2156
87	9025	9322.6171	6702.0413	11943.193	0.4119	0.5623	0.4549	0.482
88	8476	7429.8867	4671.5642	10188.2091	0.2286	0.1285	0.264	0.0826
89	7952	7456.6587	4567.1454	10346.1721	0.3684	0.2446	0.304	0.0957
90	7759	7120.1428	4105.1418	10135.1438	0.339	0.2943	0.2708	0.0706
91	7835	6684.2234	3548.753	9819.6938	0.236	0.2508	0.0646	0.0458
92	7600	6496.5211	3245.0416	9748.0005	0.253	0.2099	0.2326	0.0409
93	7651	6536.6528	3173.1632	9900.1424	0.2581	0.2677	0.2526	0.0486
94	8319	7338.0083	3866.1203	10809.8963	0.2899	0.4299	0.3514	0.1242
95	8812	7091.648	3514.645	10668.6509	0.1729	0.2506	0.308	0.1046
96	8630	8342.4753	4663.3594	12021.5913	0.4391	0.4012	0.4207	0.2897

\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[72]) \tabularnewline
60 & 9451 & - & - & - & - & - & - & - \tabularnewline
61 & 10041 & - & - & - & - & - & - & - \tabularnewline
62 & 9411 & - & - & - & - & - & - & - \tabularnewline
63 & 10405 & - & - & - & - & - & - & - \tabularnewline
64 & 8467 & - & - & - & - & - & - & - \tabularnewline
65 & 8464 & - & - & - & - & - & - & - \tabularnewline
66 & 8102 & - & - & - & - & - & - & - \tabularnewline
67 & 7627 & - & - & - & - & - & - & - \tabularnewline
68 & 7513 & - & - & - & - & - & - & - \tabularnewline
69 & 7510 & - & - & - & - & - & - & - \tabularnewline
70 & 8291 & - & - & - & - & - & - & - \tabularnewline
71 & 8064 & - & - & - & - & - & - & - \tabularnewline
72 & 9383 & - & - & - & - & - & - & - \tabularnewline
73 & 9706 & 9615.146 & 8752.2365 & 10478.0556 & 0.4183 & 0.701 & 0.1667 & 0.701 \tabularnewline
74 & 8579 & 8305.4224 & 7274.1581 & 9336.6868 & 0.3015 & 0.0039 & 0.0178 & 0.0203 \tabularnewline
75 & 9474 & 8919.1709 & 7737.2503 & 10101.0915 & 0.1788 & 0.7137 & 0.0069 & 0.2209 \tabularnewline
76 & 8318 & 7641.8884 & 6326.6928 & 8957.0839 & 0.1568 & 0.0032 & 0.1094 & 0.0047 \tabularnewline
77 & 8213 & 7579.7396 & 6143.5749 & 9015.9043 & 0.1937 & 0.1568 & 0.1138 & 0.0069 \tabularnewline
78 & 8059 & 7331.3048 & 5783.5975 & 8879.0121 & 0.1784 & 0.1321 & 0.1645 & 0.0047 \tabularnewline
79 & 9111 & 7201.9869 & 5550.2525 & 8853.7213 & 0.0117 & 0.1546 & 0.307 & 0.0048 \tabularnewline
80 & 7708 & 6917.4714 & 5167.8843 & 8667.0586 & 0.1879 & 0.007 & 0.2523 & 0.0029 \tabularnewline
81 & 7680 & 6927.5635 & 5085.3139 & 8769.8132 & 0.2117 & 0.2032 & 0.2677 & 0.0045 \tabularnewline
82 & 8014 & 7687.2858 & 5756.8163 & 9617.7553 & 0.3701 & 0.503 & 0.27 & 0.0426 \tabularnewline
83 & 8007 & 7447.9014 & 5433.071 & 9462.7317 & 0.2933 & 0.2909 & 0.2745 & 0.0299 \tabularnewline
84 & 8718 & 8643.4507 & 6547.6525 & 10739.2489 & 0.4722 & 0.7241 & 0.2446 & 0.2446 \tabularnewline
85 & 9486 & 9155.0625 & 6832.264 & 11477.861 & 0.39 & 0.6439 & 0.321 & 0.4237 \tabularnewline
86 & 9113 & 8389.0225 & 5913.9402 & 10864.1048 & 0.2832 & 0.1925 & 0.4402 & 0.2156 \tabularnewline
87 & 9025 & 9322.6171 & 6702.0413 & 11943.193 & 0.4119 & 0.5623 & 0.4549 & 0.482 \tabularnewline
88 & 8476 & 7429.8867 & 4671.5642 & 10188.2091 & 0.2286 & 0.1285 & 0.264 & 0.0826 \tabularnewline
89 & 7952 & 7456.6587 & 4567.1454 & 10346.1721 & 0.3684 & 0.2446 & 0.304 & 0.0957 \tabularnewline
90 & 7759 & 7120.1428 & 4105.1418 & 10135.1438 & 0.339 & 0.2943 & 0.2708 & 0.0706 \tabularnewline
91 & 7835 & 6684.2234 & 3548.753 & 9819.6938 & 0.236 & 0.2508 & 0.0646 & 0.0458 \tabularnewline
92 & 7600 & 6496.5211 & 3245.0416 & 9748.0005 & 0.253 & 0.2099 & 0.2326 & 0.0409 \tabularnewline
93 & 7651 & 6536.6528 & 3173.1632 & 9900.1424 & 0.2581 & 0.2677 & 0.2526 & 0.0486 \tabularnewline
94 & 8319 & 7338.0083 & 3866.1203 & 10809.8963 & 0.2899 & 0.4299 & 0.3514 & 0.1242 \tabularnewline
95 & 8812 & 7091.648 & 3514.645 & 10668.6509 & 0.1729 & 0.2506 & 0.308 & 0.1046 \tabularnewline
96 & 8630 & 8342.4753 & 4663.3594 & 12021.5913 & 0.4391 & 0.4012 & 0.4207 & 0.2897 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=106473&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[72])[/C][/ROW]
[ROW][C]60[/C][C]9451[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]61[/C][C]10041[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]62[/C][C]9411[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]63[/C][C]10405[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]64[/C][C]8467[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]65[/C][C]8464[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]66[/C][C]8102[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]67[/C][C]7627[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]68[/C][C]7513[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]69[/C][C]7510[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]70[/C][C]8291[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]71[/C][C]8064[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]72[/C][C]9383[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]73[/C][C]9706[/C][C]9615.146[/C][C]8752.2365[/C][C]10478.0556[/C][C]0.4183[/C][C]0.701[/C][C]0.1667[/C][C]0.701[/C][/ROW]
[ROW][C]74[/C][C]8579[/C][C]8305.4224[/C][C]7274.1581[/C][C]9336.6868[/C][C]0.3015[/C][C]0.0039[/C][C]0.0178[/C][C]0.0203[/C][/ROW]
[ROW][C]75[/C][C]9474[/C][C]8919.1709[/C][C]7737.2503[/C][C]10101.0915[/C][C]0.1788[/C][C]0.7137[/C][C]0.0069[/C][C]0.2209[/C][/ROW]
[ROW][C]76[/C][C]8318[/C][C]7641.8884[/C][C]6326.6928[/C][C]8957.0839[/C][C]0.1568[/C][C]0.0032[/C][C]0.1094[/C][C]0.0047[/C][/ROW]
[ROW][C]77[/C][C]8213[/C][C]7579.7396[/C][C]6143.5749[/C][C]9015.9043[/C][C]0.1937[/C][C]0.1568[/C][C]0.1138[/C][C]0.0069[/C][/ROW]
[ROW][C]78[/C][C]8059[/C][C]7331.3048[/C][C]5783.5975[/C][C]8879.0121[/C][C]0.1784[/C][C]0.1321[/C][C]0.1645[/C][C]0.0047[/C][/ROW]
[ROW][C]79[/C][C]9111[/C][C]7201.9869[/C][C]5550.2525[/C][C]8853.7213[/C][C]0.0117[/C][C]0.1546[/C][C]0.307[/C][C]0.0048[/C][/ROW]
[ROW][C]80[/C][C]7708[/C][C]6917.4714[/C][C]5167.8843[/C][C]8667.0586[/C][C]0.1879[/C][C]0.007[/C][C]0.2523[/C][C]0.0029[/C][/ROW]
[ROW][C]81[/C][C]7680[/C][C]6927.5635[/C][C]5085.3139[/C][C]8769.8132[/C][C]0.2117[/C][C]0.2032[/C][C]0.2677[/C][C]0.0045[/C][/ROW]
[ROW][C]82[/C][C]8014[/C][C]7687.2858[/C][C]5756.8163[/C][C]9617.7553[/C][C]0.3701[/C][C]0.503[/C][C]0.27[/C][C]0.0426[/C][/ROW]
[ROW][C]83[/C][C]8007[/C][C]7447.9014[/C][C]5433.071[/C][C]9462.7317[/C][C]0.2933[/C][C]0.2909[/C][C]0.2745[/C][C]0.0299[/C][/ROW]
[ROW][C]84[/C][C]8718[/C][C]8643.4507[/C][C]6547.6525[/C][C]10739.2489[/C][C]0.4722[/C][C]0.7241[/C][C]0.2446[/C][C]0.2446[/C][/ROW]
[ROW][C]85[/C][C]9486[/C][C]9155.0625[/C][C]6832.264[/C][C]11477.861[/C][C]0.39[/C][C]0.6439[/C][C]0.321[/C][C]0.4237[/C][/ROW]
[ROW][C]86[/C][C]9113[/C][C]8389.0225[/C][C]5913.9402[/C][C]10864.1048[/C][C]0.2832[/C][C]0.1925[/C][C]0.4402[/C][C]0.2156[/C][/ROW]
[ROW][C]87[/C][C]9025[/C][C]9322.6171[/C][C]6702.0413[/C][C]11943.193[/C][C]0.4119[/C][C]0.5623[/C][C]0.4549[/C][C]0.482[/C][/ROW]
[ROW][C]88[/C][C]8476[/C][C]7429.8867[/C][C]4671.5642[/C][C]10188.2091[/C][C]0.2286[/C][C]0.1285[/C][C]0.264[/C][C]0.0826[/C][/ROW]
[ROW][C]89[/C][C]7952[/C][C]7456.6587[/C][C]4567.1454[/C][C]10346.1721[/C][C]0.3684[/C][C]0.2446[/C][C]0.304[/C][C]0.0957[/C][/ROW]
[ROW][C]90[/C][C]7759[/C][C]7120.1428[/C][C]4105.1418[/C][C]10135.1438[/C][C]0.339[/C][C]0.2943[/C][C]0.2708[/C][C]0.0706[/C][/ROW]
[ROW][C]91[/C][C]7835[/C][C]6684.2234[/C][C]3548.753[/C][C]9819.6938[/C][C]0.236[/C][C]0.2508[/C][C]0.0646[/C][C]0.0458[/C][/ROW]
[ROW][C]92[/C][C]7600[/C][C]6496.5211[/C][C]3245.0416[/C][C]9748.0005[/C][C]0.253[/C][C]0.2099[/C][C]0.2326[/C][C]0.0409[/C][/ROW]
[ROW][C]93[/C][C]7651[/C][C]6536.6528[/C][C]3173.1632[/C][C]9900.1424[/C][C]0.2581[/C][C]0.2677[/C][C]0.2526[/C][C]0.0486[/C][/ROW]
[ROW][C]94[/C][C]8319[/C][C]7338.0083[/C][C]3866.1203[/C][C]10809.8963[/C][C]0.2899[/C][C]0.4299[/C][C]0.3514[/C][C]0.1242[/C][/ROW]
[ROW][C]95[/C][C]8812[/C][C]7091.648[/C][C]3514.645[/C][C]10668.6509[/C][C]0.1729[/C][C]0.2506[/C][C]0.308[/C][C]0.1046[/C][/ROW]
[ROW][C]96[/C][C]8630[/C][C]8342.4753[/C][C]4663.3594[/C][C]12021.5913[/C][C]0.4391[/C][C]0.4012[/C][C]0.4207[/C][C]0.2897[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=106473&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=106473&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[72])
60	9451	-	-	-	-	-	-	-
61	10041	-	-	-	-	-	-	-
62	9411	-	-	-	-	-	-	-
63	10405	-	-	-	-	-	-	-
64	8467	-	-	-	-	-	-	-
65	8464	-	-	-	-	-	-	-
66	8102	-	-	-	-	-	-	-
67	7627	-	-	-	-	-	-	-
68	7513	-	-	-	-	-	-	-
69	7510	-	-	-	-	-	-	-
70	8291	-	-	-	-	-	-	-
71	8064	-	-	-	-	-	-	-
72	9383	-	-	-	-	-	-	-
73	9706	9615.146	8752.2365	10478.0556	0.4183	0.701	0.1667	0.701
74	8579	8305.4224	7274.1581	9336.6868	0.3015	0.0039	0.0178	0.0203
75	9474	8919.1709	7737.2503	10101.0915	0.1788	0.7137	0.0069	0.2209
76	8318	7641.8884	6326.6928	8957.0839	0.1568	0.0032	0.1094	0.0047
77	8213	7579.7396	6143.5749	9015.9043	0.1937	0.1568	0.1138	0.0069
78	8059	7331.3048	5783.5975	8879.0121	0.1784	0.1321	0.1645	0.0047
79	9111	7201.9869	5550.2525	8853.7213	0.0117	0.1546	0.307	0.0048
80	7708	6917.4714	5167.8843	8667.0586	0.1879	0.007	0.2523	0.0029
81	7680	6927.5635	5085.3139	8769.8132	0.2117	0.2032	0.2677	0.0045
82	8014	7687.2858	5756.8163	9617.7553	0.3701	0.503	0.27	0.0426
83	8007	7447.9014	5433.071	9462.7317	0.2933	0.2909	0.2745	0.0299
84	8718	8643.4507	6547.6525	10739.2489	0.4722	0.7241	0.2446	0.2446
85	9486	9155.0625	6832.264	11477.861	0.39	0.6439	0.321	0.4237
86	9113	8389.0225	5913.9402	10864.1048	0.2832	0.1925	0.4402	0.2156
87	9025	9322.6171	6702.0413	11943.193	0.4119	0.5623	0.4549	0.482
88	8476	7429.8867	4671.5642	10188.2091	0.2286	0.1285	0.264	0.0826
89	7952	7456.6587	4567.1454	10346.1721	0.3684	0.2446	0.304	0.0957
90	7759	7120.1428	4105.1418	10135.1438	0.339	0.2943	0.2708	0.0706
91	7835	6684.2234	3548.753	9819.6938	0.236	0.2508	0.0646	0.0458
92	7600	6496.5211	3245.0416	9748.0005	0.253	0.2099	0.2326	0.0409
93	7651	6536.6528	3173.1632	9900.1424	0.2581	0.2677	0.2526	0.0486
94	8319	7338.0083	3866.1203	10809.8963	0.2899	0.4299	0.3514	0.1242
95	8812	7091.648	3514.645	10668.6509	0.1729	0.2506	0.308	0.1046
96	8630	8342.4753	4663.3594	12021.5913	0.4391	0.4012	0.4207	0.2897

Univariate ARIMA Extrapolation Forecast Performance
time	% S.E.	PE	MAPE	Sq.E	MSE	RMSE
73	0.0458	0.0094	0	8254.4465	0	0
74	0.0634	0.0329	0.0212	74844.6924	41549.5694	203.8371
75	0.0676	0.0622	0.0349	307835.3249	130311.4879	360.9868
76	0.0878	0.0885	0.0483	457126.9352	212015.3497	460.4512
77	0.0967	0.0835	0.0553	401018.7432	249816.0284	499.816
78	0.1077	0.0993	0.0626	529540.3148	296436.7428	544.46
79	0.117	0.2651	0.0916	3644331.0224	774707.3542	880.1746
80	0.129	0.1143	0.0944	624935.3939	755985.8592	869.4745
81	0.1357	0.1086	0.096	566160.6148	734894.1653	857.2597
82	0.1281	0.0425	0.0906	106742.1907	672078.9679	819.8042
83	0.138	0.0751	0.0892	312591.2798	639398.269	799.6238
84	0.1237	0.0086	0.0825	5557.5981	586578.2131	765.8839
85	0.1294	0.0361	0.0789	109519.6523	549881.4007	741.5399
86	0.1505	0.0863	0.0795	524143.4312	548042.9743	740.2992
87	0.1434	-0.0319	0.0763	88575.9565	517411.8398	719.3135
88	0.1894	0.1408	0.0803	1094353.1068	553470.669	743.9561
89	0.1977	0.0664	0.0795	245362.9821	535346.6874	731.6739
90	0.216	0.0897	0.0801	408138.5647	528279.5695	726.8284
91	0.2393	0.1722	0.0849	1324286.7968	570174.6867	755.0991
92	0.2554	0.1699	0.0892	1217665.7814	602549.2414	776.2405
93	0.2625	0.1705	0.093	1241769.7017	632988.311	795.6056
94	0.2414	0.1337	0.0949	962344.7275	647959.0572	804.959
95	0.2573	0.2426	0.1013	2959611.1434	748465.6696	865.1391
96	0.225	0.0345	0.0985	82670.435	720724.2015	848.9548

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
73 & 0.0458 & 0.0094 & 0 & 8254.4465 & 0 & 0 \tabularnewline
74 & 0.0634 & 0.0329 & 0.0212 & 74844.6924 & 41549.5694 & 203.8371 \tabularnewline
75 & 0.0676 & 0.0622 & 0.0349 & 307835.3249 & 130311.4879 & 360.9868 \tabularnewline
76 & 0.0878 & 0.0885 & 0.0483 & 457126.9352 & 212015.3497 & 460.4512 \tabularnewline
77 & 0.0967 & 0.0835 & 0.0553 & 401018.7432 & 249816.0284 & 499.816 \tabularnewline
78 & 0.1077 & 0.0993 & 0.0626 & 529540.3148 & 296436.7428 & 544.46 \tabularnewline
79 & 0.117 & 0.2651 & 0.0916 & 3644331.0224 & 774707.3542 & 880.1746 \tabularnewline
80 & 0.129 & 0.1143 & 0.0944 & 624935.3939 & 755985.8592 & 869.4745 \tabularnewline
81 & 0.1357 & 0.1086 & 0.096 & 566160.6148 & 734894.1653 & 857.2597 \tabularnewline
82 & 0.1281 & 0.0425 & 0.0906 & 106742.1907 & 672078.9679 & 819.8042 \tabularnewline
83 & 0.138 & 0.0751 & 0.0892 & 312591.2798 & 639398.269 & 799.6238 \tabularnewline
84 & 0.1237 & 0.0086 & 0.0825 & 5557.5981 & 586578.2131 & 765.8839 \tabularnewline
85 & 0.1294 & 0.0361 & 0.0789 & 109519.6523 & 549881.4007 & 741.5399 \tabularnewline
86 & 0.1505 & 0.0863 & 0.0795 & 524143.4312 & 548042.9743 & 740.2992 \tabularnewline
87 & 0.1434 & -0.0319 & 0.0763 & 88575.9565 & 517411.8398 & 719.3135 \tabularnewline
88 & 0.1894 & 0.1408 & 0.0803 & 1094353.1068 & 553470.669 & 743.9561 \tabularnewline
89 & 0.1977 & 0.0664 & 0.0795 & 245362.9821 & 535346.6874 & 731.6739 \tabularnewline
90 & 0.216 & 0.0897 & 0.0801 & 408138.5647 & 528279.5695 & 726.8284 \tabularnewline
91 & 0.2393 & 0.1722 & 0.0849 & 1324286.7968 & 570174.6867 & 755.0991 \tabularnewline
92 & 0.2554 & 0.1699 & 0.0892 & 1217665.7814 & 602549.2414 & 776.2405 \tabularnewline
93 & 0.2625 & 0.1705 & 0.093 & 1241769.7017 & 632988.311 & 795.6056 \tabularnewline
94 & 0.2414 & 0.1337 & 0.0949 & 962344.7275 & 647959.0572 & 804.959 \tabularnewline
95 & 0.2573 & 0.2426 & 0.1013 & 2959611.1434 & 748465.6696 & 865.1391 \tabularnewline
96 & 0.225 & 0.0345 & 0.0985 & 82670.435 & 720724.2015 & 848.9548 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=106473&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]73[/C][C]0.0458[/C][C]0.0094[/C][C]0[/C][C]8254.4465[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]74[/C][C]0.0634[/C][C]0.0329[/C][C]0.0212[/C][C]74844.6924[/C][C]41549.5694[/C][C]203.8371[/C][/ROW]
[ROW][C]75[/C][C]0.0676[/C][C]0.0622[/C][C]0.0349[/C][C]307835.3249[/C][C]130311.4879[/C][C]360.9868[/C][/ROW]
[ROW][C]76[/C][C]0.0878[/C][C]0.0885[/C][C]0.0483[/C][C]457126.9352[/C][C]212015.3497[/C][C]460.4512[/C][/ROW]
[ROW][C]77[/C][C]0.0967[/C][C]0.0835[/C][C]0.0553[/C][C]401018.7432[/C][C]249816.0284[/C][C]499.816[/C][/ROW]
[ROW][C]78[/C][C]0.1077[/C][C]0.0993[/C][C]0.0626[/C][C]529540.3148[/C][C]296436.7428[/C][C]544.46[/C][/ROW]
[ROW][C]79[/C][C]0.117[/C][C]0.2651[/C][C]0.0916[/C][C]3644331.0224[/C][C]774707.3542[/C][C]880.1746[/C][/ROW]
[ROW][C]80[/C][C]0.129[/C][C]0.1143[/C][C]0.0944[/C][C]624935.3939[/C][C]755985.8592[/C][C]869.4745[/C][/ROW]
[ROW][C]81[/C][C]0.1357[/C][C]0.1086[/C][C]0.096[/C][C]566160.6148[/C][C]734894.1653[/C][C]857.2597[/C][/ROW]
[ROW][C]82[/C][C]0.1281[/C][C]0.0425[/C][C]0.0906[/C][C]106742.1907[/C][C]672078.9679[/C][C]819.8042[/C][/ROW]
[ROW][C]83[/C][C]0.138[/C][C]0.0751[/C][C]0.0892[/C][C]312591.2798[/C][C]639398.269[/C][C]799.6238[/C][/ROW]
[ROW][C]84[/C][C]0.1237[/C][C]0.0086[/C][C]0.0825[/C][C]5557.5981[/C][C]586578.2131[/C][C]765.8839[/C][/ROW]
[ROW][C]85[/C][C]0.1294[/C][C]0.0361[/C][C]0.0789[/C][C]109519.6523[/C][C]549881.4007[/C][C]741.5399[/C][/ROW]
[ROW][C]86[/C][C]0.1505[/C][C]0.0863[/C][C]0.0795[/C][C]524143.4312[/C][C]548042.9743[/C][C]740.2992[/C][/ROW]
[ROW][C]87[/C][C]0.1434[/C][C]-0.0319[/C][C]0.0763[/C][C]88575.9565[/C][C]517411.8398[/C][C]719.3135[/C][/ROW]
[ROW][C]88[/C][C]0.1894[/C][C]0.1408[/C][C]0.0803[/C][C]1094353.1068[/C][C]553470.669[/C][C]743.9561[/C][/ROW]
[ROW][C]89[/C][C]0.1977[/C][C]0.0664[/C][C]0.0795[/C][C]245362.9821[/C][C]535346.6874[/C][C]731.6739[/C][/ROW]
[ROW][C]90[/C][C]0.216[/C][C]0.0897[/C][C]0.0801[/C][C]408138.5647[/C][C]528279.5695[/C][C]726.8284[/C][/ROW]
[ROW][C]91[/C][C]0.2393[/C][C]0.1722[/C][C]0.0849[/C][C]1324286.7968[/C][C]570174.6867[/C][C]755.0991[/C][/ROW]
[ROW][C]92[/C][C]0.2554[/C][C]0.1699[/C][C]0.0892[/C][C]1217665.7814[/C][C]602549.2414[/C][C]776.2405[/C][/ROW]
[ROW][C]93[/C][C]0.2625[/C][C]0.1705[/C][C]0.093[/C][C]1241769.7017[/C][C]632988.311[/C][C]795.6056[/C][/ROW]
[ROW][C]94[/C][C]0.2414[/C][C]0.1337[/C][C]0.0949[/C][C]962344.7275[/C][C]647959.0572[/C][C]804.959[/C][/ROW]
[ROW][C]95[/C][C]0.2573[/C][C]0.2426[/C][C]0.1013[/C][C]2959611.1434[/C][C]748465.6696[/C][C]865.1391[/C][/ROW]
[ROW][C]96[/C][C]0.225[/C][C]0.0345[/C][C]0.0985[/C][C]82670.435[/C][C]720724.2015[/C][C]848.9548[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=106473&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=106473&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
73	0.0458	0.0094	0	8254.4465	0	0
74	0.0634	0.0329	0.0212	74844.6924	41549.5694	203.8371
75	0.0676	0.0622	0.0349	307835.3249	130311.4879	360.9868
76	0.0878	0.0885	0.0483	457126.9352	212015.3497	460.4512
77	0.0967	0.0835	0.0553	401018.7432	249816.0284	499.816
78	0.1077	0.0993	0.0626	529540.3148	296436.7428	544.46
79	0.117	0.2651	0.0916	3644331.0224	774707.3542	880.1746
80	0.129	0.1143	0.0944	624935.3939	755985.8592	869.4745
81	0.1357	0.1086	0.096	566160.6148	734894.1653	857.2597
82	0.1281	0.0425	0.0906	106742.1907	672078.9679	819.8042
83	0.138	0.0751	0.0892	312591.2798	639398.269	799.6238
84	0.1237	0.0086	0.0825	5557.5981	586578.2131	765.8839
85	0.1294	0.0361	0.0789	109519.6523	549881.4007	741.5399
86	0.1505	0.0863	0.0795	524143.4312	548042.9743	740.2992
87	0.1434	-0.0319	0.0763	88575.9565	517411.8398	719.3135
88	0.1894	0.1408	0.0803	1094353.1068	553470.669	743.9561
89	0.1977	0.0664	0.0795	245362.9821	535346.6874	731.6739
90	0.216	0.0897	0.0801	408138.5647	528279.5695	726.8284
91	0.2393	0.1722	0.0849	1324286.7968	570174.6867	755.0991
92	0.2554	0.1699	0.0892	1217665.7814	602549.2414	776.2405
93	0.2625	0.1705	0.093	1241769.7017	632988.311	795.6056
94	0.2414	0.1337	0.0949	962344.7275	647959.0572	804.959
95	0.2573	0.2426	0.1013	2959611.1434	748465.6696	865.1391
96	0.225	0.0345	0.0985	82670.435	720724.2015	848.9548

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = FALSE ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;

Parameters (R input):

par1 = 24 ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 1 ; par7 = 1 ; par8 = 2 ; 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