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R Software Module

rwasp_arimaforecasting.wasp

Title produced by software

ARIMA Forecasting

Date of computation

Sun, 19 Dec 2010 16:45:16 +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/19/t1292777052zan2eb9ph1eodiz.htm/, Retrieved Tue, 30 Apr 2024 03:44:38 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112608, Retrieved Tue, 30 Apr 2024 03:44:38 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

190

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

-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [WS7] [2009-11-18 17:01:04] [8b1aef4e7013bd33fbc2a5833375c5f5]
-   PD      [Multiple Regression] [WS7(2)] [2009-11-20 19:01:46] [7d268329e554b8694908ba13e6e6f258]
-   P         [Multiple Regression] [WS7(3)] [2009-11-21 10:22:47] [7d268329e554b8694908ba13e6e6f258]
-   PD          [Multiple Regression] [WS7(4)] [2009-11-21 10:55:20] [7d268329e554b8694908ba13e6e6f258]
- RMPD            [Univariate Data Series] [Niet-werkende wer...] [2009-11-25 19:16:52] [9717cb857c153ca3061376906953b329]
-   PD              [Univariate Data Series] [] [2010-12-16 17:58:43] [bcc4ad4a6c0f95d5b548de29638ac6c2]
-   PD                [Univariate Data Series] [] [2010-12-19 14:40:10] [bcc4ad4a6c0f95d5b548de29638ac6c2]
- RMP                     [ARIMA Forecasting] [] [2010-12-19 16:45:16] [4e3652732e77bb1a104cdb5f8d687d01] [Current]

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Dataseries X:

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'George Udny Yule' @ 72.249.76.132

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 2 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112608&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112608&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112608&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Univariate ARIMA Extrapolation Forecast
time	Y[t]	F[t]	95% LB	95% UB	p-value(H0: Y[t] = F[t])	P(F[t]>Y[t-1])	P(F[t]>Y[t-s])	P(F[t]>Y[65])
53	469357	-	-	-	-	-	-	-
54	477580	-	-	-	-	-	-	-
55	528379	-	-	-	-	-	-	-
56	533590	-	-	-	-	-	-	-
57	517945	-	-	-	-	-	-	-
58	506174	-	-	-	-	-	-	-
59	501866	-	-	-	-	-	-	-
60	516141	-	-	-	-	-	-	-
61	528222	-	-	-	-	-	-	-
62	532638	-	-	-	-	-	-	-
63	536322	-	-	-	-	-	-	-
64	536535	-	-	-	-	-	-	-
65	523597	-	-	-	-	-	-	-
66	536214	532877.5129	517610.5327	548144.4931	0.3342	0.8833	1	0.8833
67	586570	590278.8524	566768.2994	613789.4055	0.3786	1	1	1
68	596594	601767.9847	570697.3535	632838.6158	0.3721	0.8312	1	1
69	580523	590494.0868	552084.0459	628904.1276	0.3054	0.3778	0.9999	0.9997
70	564478	581381.2046	535716.9407	627045.4685	0.2341	0.5147	0.9994	0.9934
71	557560	577168.1126	524288.0188	630048.2064	0.2337	0.681	0.9974	0.9765
72	575093	591562.2152	531490.049	651634.3814	0.2955	0.8664	0.9931	0.9867
73	580112	604373.1896	537131.2564	671615.1227	0.2397	0.8033	0.9868	0.9907
74	574761	608126.7986	533741.5017	682512.0954	0.1897	0.7698	0.9767	0.987
75	563250	609465.9831	527970.0301	690961.9362	0.1332	0.798	0.9607	0.9805
76	551531	611535.7165	522968.6887	700102.7442	0.0921	0.8574	0.9515	0.9742
77	537034	599852.4847	504260.6517	695444.3176	0.0989	0.8391	0.941	0.941
78	544686	609917.8447	503005.6058	716830.0836	0.1159	0.9093	0.9117	0.9432
79	600991	668050.5806	549669.2597	786431.9014	0.1334	0.9794	0.9113	0.9916
80	604378	680221.2985	550277.5571	810165.04	0.1263	0.884	0.8964	0.9909
81	586111	669582.568	528026.8993	811138.2367	0.1239	0.8167	0.8912	0.9784
82	563668	661061.5962	507879.6588	814243.5336	0.1064	0.8312	0.8917	0.9607
83	548604	657400.1033	492606.0267	822194.1799	0.0978	0.8675	0.8825	0.9442

\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[65]) \tabularnewline
53 & 469357 & - & - & - & - & - & - & - \tabularnewline
54 & 477580 & - & - & - & - & - & - & - \tabularnewline
55 & 528379 & - & - & - & - & - & - & - \tabularnewline
56 & 533590 & - & - & - & - & - & - & - \tabularnewline
57 & 517945 & - & - & - & - & - & - & - \tabularnewline
58 & 506174 & - & - & - & - & - & - & - \tabularnewline
59 & 501866 & - & - & - & - & - & - & - \tabularnewline
60 & 516141 & - & - & - & - & - & - & - \tabularnewline
61 & 528222 & - & - & - & - & - & - & - \tabularnewline
62 & 532638 & - & - & - & - & - & - & - \tabularnewline
63 & 536322 & - & - & - & - & - & - & - \tabularnewline
64 & 536535 & - & - & - & - & - & - & - \tabularnewline
65 & 523597 & - & - & - & - & - & - & - \tabularnewline
66 & 536214 & 532877.5129 & 517610.5327 & 548144.4931 & 0.3342 & 0.8833 & 1 & 0.8833 \tabularnewline
67 & 586570 & 590278.8524 & 566768.2994 & 613789.4055 & 0.3786 & 1 & 1 & 1 \tabularnewline
68 & 596594 & 601767.9847 & 570697.3535 & 632838.6158 & 0.3721 & 0.8312 & 1 & 1 \tabularnewline
69 & 580523 & 590494.0868 & 552084.0459 & 628904.1276 & 0.3054 & 0.3778 & 0.9999 & 0.9997 \tabularnewline
70 & 564478 & 581381.2046 & 535716.9407 & 627045.4685 & 0.2341 & 0.5147 & 0.9994 & 0.9934 \tabularnewline
71 & 557560 & 577168.1126 & 524288.0188 & 630048.2064 & 0.2337 & 0.681 & 0.9974 & 0.9765 \tabularnewline
72 & 575093 & 591562.2152 & 531490.049 & 651634.3814 & 0.2955 & 0.8664 & 0.9931 & 0.9867 \tabularnewline
73 & 580112 & 604373.1896 & 537131.2564 & 671615.1227 & 0.2397 & 0.8033 & 0.9868 & 0.9907 \tabularnewline
74 & 574761 & 608126.7986 & 533741.5017 & 682512.0954 & 0.1897 & 0.7698 & 0.9767 & 0.987 \tabularnewline
75 & 563250 & 609465.9831 & 527970.0301 & 690961.9362 & 0.1332 & 0.798 & 0.9607 & 0.9805 \tabularnewline
76 & 551531 & 611535.7165 & 522968.6887 & 700102.7442 & 0.0921 & 0.8574 & 0.9515 & 0.9742 \tabularnewline
77 & 537034 & 599852.4847 & 504260.6517 & 695444.3176 & 0.0989 & 0.8391 & 0.941 & 0.941 \tabularnewline
78 & 544686 & 609917.8447 & 503005.6058 & 716830.0836 & 0.1159 & 0.9093 & 0.9117 & 0.9432 \tabularnewline
79 & 600991 & 668050.5806 & 549669.2597 & 786431.9014 & 0.1334 & 0.9794 & 0.9113 & 0.9916 \tabularnewline
80 & 604378 & 680221.2985 & 550277.5571 & 810165.04 & 0.1263 & 0.884 & 0.8964 & 0.9909 \tabularnewline
81 & 586111 & 669582.568 & 528026.8993 & 811138.2367 & 0.1239 & 0.8167 & 0.8912 & 0.9784 \tabularnewline
82 & 563668 & 661061.5962 & 507879.6588 & 814243.5336 & 0.1064 & 0.8312 & 0.8917 & 0.9607 \tabularnewline
83 & 548604 & 657400.1033 & 492606.0267 & 822194.1799 & 0.0978 & 0.8675 & 0.8825 & 0.9442 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112608&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[65])[/C][/ROW]
[ROW][C]53[/C][C]469357[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]54[/C][C]477580[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]55[/C][C]528379[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]56[/C][C]533590[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]57[/C][C]517945[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]58[/C][C]506174[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]59[/C][C]501866[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]60[/C][C]516141[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]61[/C][C]528222[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]62[/C][C]532638[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]63[/C][C]536322[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]64[/C][C]536535[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]65[/C][C]523597[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]66[/C][C]536214[/C][C]532877.5129[/C][C]517610.5327[/C][C]548144.4931[/C][C]0.3342[/C][C]0.8833[/C][C]1[/C][C]0.8833[/C][/ROW]
[ROW][C]67[/C][C]586570[/C][C]590278.8524[/C][C]566768.2994[/C][C]613789.4055[/C][C]0.3786[/C][C]1[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]68[/C][C]596594[/C][C]601767.9847[/C][C]570697.3535[/C][C]632838.6158[/C][C]0.3721[/C][C]0.8312[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]69[/C][C]580523[/C][C]590494.0868[/C][C]552084.0459[/C][C]628904.1276[/C][C]0.3054[/C][C]0.3778[/C][C]0.9999[/C][C]0.9997[/C][/ROW]
[ROW][C]70[/C][C]564478[/C][C]581381.2046[/C][C]535716.9407[/C][C]627045.4685[/C][C]0.2341[/C][C]0.5147[/C][C]0.9994[/C][C]0.9934[/C][/ROW]
[ROW][C]71[/C][C]557560[/C][C]577168.1126[/C][C]524288.0188[/C][C]630048.2064[/C][C]0.2337[/C][C]0.681[/C][C]0.9974[/C][C]0.9765[/C][/ROW]
[ROW][C]72[/C][C]575093[/C][C]591562.2152[/C][C]531490.049[/C][C]651634.3814[/C][C]0.2955[/C][C]0.8664[/C][C]0.9931[/C][C]0.9867[/C][/ROW]
[ROW][C]73[/C][C]580112[/C][C]604373.1896[/C][C]537131.2564[/C][C]671615.1227[/C][C]0.2397[/C][C]0.8033[/C][C]0.9868[/C][C]0.9907[/C][/ROW]
[ROW][C]74[/C][C]574761[/C][C]608126.7986[/C][C]533741.5017[/C][C]682512.0954[/C][C]0.1897[/C][C]0.7698[/C][C]0.9767[/C][C]0.987[/C][/ROW]
[ROW][C]75[/C][C]563250[/C][C]609465.9831[/C][C]527970.0301[/C][C]690961.9362[/C][C]0.1332[/C][C]0.798[/C][C]0.9607[/C][C]0.9805[/C][/ROW]
[ROW][C]76[/C][C]551531[/C][C]611535.7165[/C][C]522968.6887[/C][C]700102.7442[/C][C]0.0921[/C][C]0.8574[/C][C]0.9515[/C][C]0.9742[/C][/ROW]
[ROW][C]77[/C][C]537034[/C][C]599852.4847[/C][C]504260.6517[/C][C]695444.3176[/C][C]0.0989[/C][C]0.8391[/C][C]0.941[/C][C]0.941[/C][/ROW]
[ROW][C]78[/C][C]544686[/C][C]609917.8447[/C][C]503005.6058[/C][C]716830.0836[/C][C]0.1159[/C][C]0.9093[/C][C]0.9117[/C][C]0.9432[/C][/ROW]
[ROW][C]79[/C][C]600991[/C][C]668050.5806[/C][C]549669.2597[/C][C]786431.9014[/C][C]0.1334[/C][C]0.9794[/C][C]0.9113[/C][C]0.9916[/C][/ROW]
[ROW][C]80[/C][C]604378[/C][C]680221.2985[/C][C]550277.5571[/C][C]810165.04[/C][C]0.1263[/C][C]0.884[/C][C]0.8964[/C][C]0.9909[/C][/ROW]
[ROW][C]81[/C][C]586111[/C][C]669582.568[/C][C]528026.8993[/C][C]811138.2367[/C][C]0.1239[/C][C]0.8167[/C][C]0.8912[/C][C]0.9784[/C][/ROW]
[ROW][C]82[/C][C]563668[/C][C]661061.5962[/C][C]507879.6588[/C][C]814243.5336[/C][C]0.1064[/C][C]0.8312[/C][C]0.8917[/C][C]0.9607[/C][/ROW]
[ROW][C]83[/C][C]548604[/C][C]657400.1033[/C][C]492606.0267[/C][C]822194.1799[/C][C]0.0978[/C][C]0.8675[/C][C]0.8825[/C][C]0.9442[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112608&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112608&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[65])
53	469357	-	-	-	-	-	-	-
54	477580	-	-	-	-	-	-	-
55	528379	-	-	-	-	-	-	-
56	533590	-	-	-	-	-	-	-
57	517945	-	-	-	-	-	-	-
58	506174	-	-	-	-	-	-	-
59	501866	-	-	-	-	-	-	-
60	516141	-	-	-	-	-	-	-
61	528222	-	-	-	-	-	-	-
62	532638	-	-	-	-	-	-	-
63	536322	-	-	-	-	-	-	-
64	536535	-	-	-	-	-	-	-
65	523597	-	-	-	-	-	-	-
66	536214	532877.5129	517610.5327	548144.4931	0.3342	0.8833	1	0.8833
67	586570	590278.8524	566768.2994	613789.4055	0.3786	1	1	1
68	596594	601767.9847	570697.3535	632838.6158	0.3721	0.8312	1	1
69	580523	590494.0868	552084.0459	628904.1276	0.3054	0.3778	0.9999	0.9997
70	564478	581381.2046	535716.9407	627045.4685	0.2341	0.5147	0.9994	0.9934
71	557560	577168.1126	524288.0188	630048.2064	0.2337	0.681	0.9974	0.9765
72	575093	591562.2152	531490.049	651634.3814	0.2955	0.8664	0.9931	0.9867
73	580112	604373.1896	537131.2564	671615.1227	0.2397	0.8033	0.9868	0.9907
74	574761	608126.7986	533741.5017	682512.0954	0.1897	0.7698	0.9767	0.987
75	563250	609465.9831	527970.0301	690961.9362	0.1332	0.798	0.9607	0.9805
76	551531	611535.7165	522968.6887	700102.7442	0.0921	0.8574	0.9515	0.9742
77	537034	599852.4847	504260.6517	695444.3176	0.0989	0.8391	0.941	0.941
78	544686	609917.8447	503005.6058	716830.0836	0.1159	0.9093	0.9117	0.9432
79	600991	668050.5806	549669.2597	786431.9014	0.1334	0.9794	0.9113	0.9916
80	604378	680221.2985	550277.5571	810165.04	0.1263	0.884	0.8964	0.9909
81	586111	669582.568	528026.8993	811138.2367	0.1239	0.8167	0.8912	0.9784
82	563668	661061.5962	507879.6588	814243.5336	0.1064	0.8312	0.8917	0.9607
83	548604	657400.1033	492606.0267	822194.1799	0.0978	0.8675	0.8825	0.9442

Univariate ARIMA Extrapolation Forecast Performance
time	% S.E.	PE	MAPE	Sq.E	MSE	RMSE
66	0.0146	0.0063	0	11132146.2376	0	0
67	0.0203	-0.0063	0.0063	13755586.347	12443866.2923	3527.5865
68	0.0263	-0.0086	0.007	26770117.2213	17219283.2686	4149.6124
69	0.0332	-0.0169	0.0095	99422571.0209	37770105.2067	6145.7388
70	0.0401	-0.0291	0.0134	285718326.5806	87359749.4815	9346.6438
71	0.0467	-0.034	0.0168	384478080.0151	136879471.2371	11699.55
72	0.0518	-0.0278	0.0184	271235049.0289	156073125.2073	12492.923
73	0.0568	-0.0401	0.0211	588605318.6757	210139649.3909	14496.1943
74	0.0624	-0.0549	0.0249	1113276515.0044	310488190.0146	17620.6751
75	0.0682	-0.0758	0.03	2135917097.3709	493031080.7502	22204.3032
76	0.0739	-0.0981	0.0362	3600566000.3892	775534255.2629	27848.4157
77	0.0813	-0.1047	0.0419	3946162018.4797	1039753235.5309	32245.2048
78	0.0894	-0.107	0.0469	4255193562.5072	1287094799.1445	35876.1035
79	0.0904	-0.1004	0.0507	4496987345.608	1516372838.1776	38940.6322
80	0.0975	-0.1115	0.0548	5752205929.2727	1798761710.9173	42411.811
81	0.1079	-0.1247	0.0591	6967502663.7311	2121808020.4681	46063.0874
82	0.1182	-0.1473	0.0643	9485512574.9958	2554967111.9109	50546.6825
83	0.1279	-0.1655	0.0699	11836592098.8873	3070612944.5207	55413.1117

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
66 & 0.0146 & 0.0063 & 0 & 11132146.2376 & 0 & 0 \tabularnewline
67 & 0.0203 & -0.0063 & 0.0063 & 13755586.347 & 12443866.2923 & 3527.5865 \tabularnewline
68 & 0.0263 & -0.0086 & 0.007 & 26770117.2213 & 17219283.2686 & 4149.6124 \tabularnewline
69 & 0.0332 & -0.0169 & 0.0095 & 99422571.0209 & 37770105.2067 & 6145.7388 \tabularnewline
70 & 0.0401 & -0.0291 & 0.0134 & 285718326.5806 & 87359749.4815 & 9346.6438 \tabularnewline
71 & 0.0467 & -0.034 & 0.0168 & 384478080.0151 & 136879471.2371 & 11699.55 \tabularnewline
72 & 0.0518 & -0.0278 & 0.0184 & 271235049.0289 & 156073125.2073 & 12492.923 \tabularnewline
73 & 0.0568 & -0.0401 & 0.0211 & 588605318.6757 & 210139649.3909 & 14496.1943 \tabularnewline
74 & 0.0624 & -0.0549 & 0.0249 & 1113276515.0044 & 310488190.0146 & 17620.6751 \tabularnewline
75 & 0.0682 & -0.0758 & 0.03 & 2135917097.3709 & 493031080.7502 & 22204.3032 \tabularnewline
76 & 0.0739 & -0.0981 & 0.0362 & 3600566000.3892 & 775534255.2629 & 27848.4157 \tabularnewline
77 & 0.0813 & -0.1047 & 0.0419 & 3946162018.4797 & 1039753235.5309 & 32245.2048 \tabularnewline
78 & 0.0894 & -0.107 & 0.0469 & 4255193562.5072 & 1287094799.1445 & 35876.1035 \tabularnewline
79 & 0.0904 & -0.1004 & 0.0507 & 4496987345.608 & 1516372838.1776 & 38940.6322 \tabularnewline
80 & 0.0975 & -0.1115 & 0.0548 & 5752205929.2727 & 1798761710.9173 & 42411.811 \tabularnewline
81 & 0.1079 & -0.1247 & 0.0591 & 6967502663.7311 & 2121808020.4681 & 46063.0874 \tabularnewline
82 & 0.1182 & -0.1473 & 0.0643 & 9485512574.9958 & 2554967111.9109 & 50546.6825 \tabularnewline
83 & 0.1279 & -0.1655 & 0.0699 & 11836592098.8873 & 3070612944.5207 & 55413.1117 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112608&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]66[/C][C]0.0146[/C][C]0.0063[/C][C]0[/C][C]11132146.2376[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]67[/C][C]0.0203[/C][C]-0.0063[/C][C]0.0063[/C][C]13755586.347[/C][C]12443866.2923[/C][C]3527.5865[/C][/ROW]
[ROW][C]68[/C][C]0.0263[/C][C]-0.0086[/C][C]0.007[/C][C]26770117.2213[/C][C]17219283.2686[/C][C]4149.6124[/C][/ROW]
[ROW][C]69[/C][C]0.0332[/C][C]-0.0169[/C][C]0.0095[/C][C]99422571.0209[/C][C]37770105.2067[/C][C]6145.7388[/C][/ROW]
[ROW][C]70[/C][C]0.0401[/C][C]-0.0291[/C][C]0.0134[/C][C]285718326.5806[/C][C]87359749.4815[/C][C]9346.6438[/C][/ROW]
[ROW][C]71[/C][C]0.0467[/C][C]-0.034[/C][C]0.0168[/C][C]384478080.0151[/C][C]136879471.2371[/C][C]11699.55[/C][/ROW]
[ROW][C]72[/C][C]0.0518[/C][C]-0.0278[/C][C]0.0184[/C][C]271235049.0289[/C][C]156073125.2073[/C][C]12492.923[/C][/ROW]
[ROW][C]73[/C][C]0.0568[/C][C]-0.0401[/C][C]0.0211[/C][C]588605318.6757[/C][C]210139649.3909[/C][C]14496.1943[/C][/ROW]
[ROW][C]74[/C][C]0.0624[/C][C]-0.0549[/C][C]0.0249[/C][C]1113276515.0044[/C][C]310488190.0146[/C][C]17620.6751[/C][/ROW]
[ROW][C]75[/C][C]0.0682[/C][C]-0.0758[/C][C]0.03[/C][C]2135917097.3709[/C][C]493031080.7502[/C][C]22204.3032[/C][/ROW]
[ROW][C]76[/C][C]0.0739[/C][C]-0.0981[/C][C]0.0362[/C][C]3600566000.3892[/C][C]775534255.2629[/C][C]27848.4157[/C][/ROW]
[ROW][C]77[/C][C]0.0813[/C][C]-0.1047[/C][C]0.0419[/C][C]3946162018.4797[/C][C]1039753235.5309[/C][C]32245.2048[/C][/ROW]
[ROW][C]78[/C][C]0.0894[/C][C]-0.107[/C][C]0.0469[/C][C]4255193562.5072[/C][C]1287094799.1445[/C][C]35876.1035[/C][/ROW]
[ROW][C]79[/C][C]0.0904[/C][C]-0.1004[/C][C]0.0507[/C][C]4496987345.608[/C][C]1516372838.1776[/C][C]38940.6322[/C][/ROW]
[ROW][C]80[/C][C]0.0975[/C][C]-0.1115[/C][C]0.0548[/C][C]5752205929.2727[/C][C]1798761710.9173[/C][C]42411.811[/C][/ROW]
[ROW][C]81[/C][C]0.1079[/C][C]-0.1247[/C][C]0.0591[/C][C]6967502663.7311[/C][C]2121808020.4681[/C][C]46063.0874[/C][/ROW]
[ROW][C]82[/C][C]0.1182[/C][C]-0.1473[/C][C]0.0643[/C][C]9485512574.9958[/C][C]2554967111.9109[/C][C]50546.6825[/C][/ROW]
[ROW][C]83[/C][C]0.1279[/C][C]-0.1655[/C][C]0.0699[/C][C]11836592098.8873[/C][C]3070612944.5207[/C][C]55413.1117[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112608&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112608&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
66	0.0146	0.0063	0	11132146.2376	0	0
67	0.0203	-0.0063	0.0063	13755586.347	12443866.2923	3527.5865
68	0.0263	-0.0086	0.007	26770117.2213	17219283.2686	4149.6124
69	0.0332	-0.0169	0.0095	99422571.0209	37770105.2067	6145.7388
70	0.0401	-0.0291	0.0134	285718326.5806	87359749.4815	9346.6438
71	0.0467	-0.034	0.0168	384478080.0151	136879471.2371	11699.55
72	0.0518	-0.0278	0.0184	271235049.0289	156073125.2073	12492.923
73	0.0568	-0.0401	0.0211	588605318.6757	210139649.3909	14496.1943
74	0.0624	-0.0549	0.0249	1113276515.0044	310488190.0146	17620.6751
75	0.0682	-0.0758	0.03	2135917097.3709	493031080.7502	22204.3032
76	0.0739	-0.0981	0.0362	3600566000.3892	775534255.2629	27848.4157
77	0.0813	-0.1047	0.0419	3946162018.4797	1039753235.5309	32245.2048
78	0.0894	-0.107	0.0469	4255193562.5072	1287094799.1445	35876.1035
79	0.0904	-0.1004	0.0507	4496987345.608	1516372838.1776	38940.6322
80	0.0975	-0.1115	0.0548	5752205929.2727	1798761710.9173	42411.811
81	0.1079	-0.1247	0.0591	6967502663.7311	2121808020.4681	46063.0874
82	0.1182	-0.1473	0.0643	9485512574.9958	2554967111.9109	50546.6825
83	0.1279	-0.1655	0.0699	11836592098.8873	3070612944.5207	55413.1117

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = Niet-werkende werkzoekenden in Belgie ; par2 = http://www.nbb.be/belgostat/ ; par3 = Niet-werkende werkzoekenden in Belgie ; par4 = 12 ;

Parameters (R input):

par1 = 18 ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 1 ; par7 = 1 ; par8 = 0 ; par9 = 1 ; par10 = FALSE ;

R code (references can be found in the software module):

par1 <- as.numeric(par1) #cut off periods
par2 <- as.numeric(par2) #lambda
par3 <- as.numeric(par3) #degree of non-seasonal differencing
par4 <- as.numeric(par4) #degree of seasonal differencing
par5 <- as.numeric(par5) #seasonal period
par6 <- as.numeric(par6) #p
par7 <- as.numeric(par7) #q
par8 <- as.numeric(par8) #P
par9 <- as.numeric(par9) #Q
if (par10 == 'TRUE') par10 <- TRUE
if (par10 == 'FALSE') par10 <- FALSE
if (par2 == 0) x <- log(x)
if (par2 != 0) x <- x^par2
lx <- length(x)
first <- lx - 2*par1
nx <- lx - par1
nx1 <- nx + 1
fx <- lx - nx
if (fx < 1) {
fx <- par5
nx1 <- lx + fx - 1
first <- lx - 2*fx
}
first <- 1
if (fx < 3) fx <- round(lx/10,0)
(arima.out <- arima(x[1:nx], order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5), include.mean=par10, method='ML'))
(forecast <- predict(arima.out,par1))
(lb <- forecast$pred - 1.96 * forecast$se)
(ub <- forecast$pred + 1.96 * forecast$se)
if (par2 == 0) {
x <- exp(x)
forecast$pred <- exp(forecast$pred)
lb <- exp(lb)
ub <- exp(ub)
}
if (par2 != 0) {
x <- x^(1/par2)
forecast$pred <- forecast$pred^(1/par2)
lb <- lb^(1/par2)
ub <- ub^(1/par2)
}
if (par2 < 0) {
olb <- lb
lb <- ub
ub <- olb
}
(actandfor <- c(x[1:nx], forecast$pred))
(perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
bitmap(file='test1.png')
opar <- par(mar=c(4,4,2,2),las=1)
ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
plot(x,ylim=ylim,type='n',xlim=c(first,lx))
usr <- par('usr')
rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
abline(h= (-3:3)*2 , col ='gray', lty =3)
polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
lines(nx1:lx, lb , lty=2)
lines(nx1:lx, ub , lty=2)
lines(x, lwd=2)
lines(nx1:lx, forecast$pred , lwd=2 , col ='white')
box()
par(opar)
dev.off()
prob.dec <- array(NA, dim=fx)
prob.sdec <- array(NA, dim=fx)
prob.ldec <- array(NA, dim=fx)
prob.pval <- array(NA, dim=fx)
perf.pe <- array(0, dim=fx)
perf.mape <- array(0, dim=fx)
perf.mape1 <- array(0, dim=fx)
perf.se <- array(0, dim=fx)
perf.mse <- array(0, dim=fx)
perf.mse1 <- array(0, dim=fx)
perf.rmse <- array(0, dim=fx)
for (i in 1:fx) {
locSD <- (ub[i] - forecast$pred[i]) / 1.96
perf.pe[i] = (x[nx+i] - forecast$pred[i]) / forecast$pred[i]
perf.se[i] = (x[nx+i] - forecast$pred[i])^2
prob.dec[i] = pnorm((x[nx+i-1] - forecast$pred[i]) / locSD)
prob.sdec[i] = pnorm((x[nx+i-par5] - forecast$pred[i]) / locSD)
prob.ldec[i] = pnorm((x[nx] - forecast$pred[i]) / locSD)
prob.pval[i] = pnorm(abs(x[nx+i] - forecast$pred[i]) / locSD)
}
perf.mape[1] = abs(perf.pe[1])
perf.mse[1] = abs(perf.se[1])
for (i in 2:fx) {
perf.mape[i] = perf.mape[i-1] + abs(perf.pe[i])
perf.mape1[i] = perf.mape[i] / i
perf.mse[i] = perf.mse[i-1] + perf.se[i]
perf.mse1[i] = perf.mse[i] / i
}
perf.rmse = sqrt(perf.mse1)
bitmap(file='test2.png')
plot(forecast$pred, pch=19, type='b',main='ARIMA Extrapolation Forecast', ylab='Forecast and 95% CI', xlab='time',ylim=c(min(lb),max(ub)))
dum <- forecast$pred
dum[1:par1] <- x[(nx+1):lx]
lines(dum, lty=1)
lines(ub,lty=3)
lines(lb,lty=3)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Univariate ARIMA Extrapolation Forecast',9,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'time',1,header=TRUE)
a<-table.element(a,'Y[t]',1,header=TRUE)
a<-table.element(a,'F[t]',1,header=TRUE)
a<-table.element(a,'95% LB',1,header=TRUE)
a<-table.element(a,'95% UB',1,header=TRUE)
a<-table.element(a,'p-value
(H0: Y[t] = F[t])',1,header=TRUE)
a<-table.element(a,'P(F[t]>Y[t-1])',1,header=TRUE)
a<-table.element(a,'P(F[t]>Y[t-s])',1,header=TRUE)
mylab <- paste('P(F[t]>Y[',nx,sep='')
mylab <- paste(mylab,'])',sep='')
a<-table.element(a,mylab,1,header=TRUE)
a<-table.row.end(a)
for (i in (nx-par5):nx) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.row.end(a)
}
for (i in 1:fx) {
a<-table.row.start(a)
a<-table.element(a,nx+i,header=TRUE)
a<-table.element(a,round(x[nx+i],4))
a<-table.element(a,round(forecast$pred[i],4))
a<-table.element(a,round(lb[i],4))
a<-table.element(a,round(ub[i],4))
a<-table.element(a,round((1-prob.pval[i]),4))
a<-table.element(a,round((1-prob.dec[i]),4))
a<-table.element(a,round((1-prob.sdec[i]),4))
a<-table.element(a,round((1-prob.ldec[i]),4))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Univariate ARIMA Extrapolation Forecast Performance',7,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'time',1,header=TRUE)
a<-table.element(a,'% S.E.',1,header=TRUE)
a<-table.element(a,'PE',1,header=TRUE)
a<-table.element(a,'MAPE',1,header=TRUE)
a<-table.element(a,'Sq.E',1,header=TRUE)
a<-table.element(a,'MSE',1,header=TRUE)
a<-table.element(a,'RMSE',1,header=TRUE)
a<-table.row.end(a)
for (i in 1:fx) {
a<-table.row.start(a)
a<-table.element(a,nx+i,header=TRUE)
a<-table.element(a,round(perc.se[i],4))
a<-table.element(a,round(perf.pe[i],4))
a<-table.element(a,round(perf.mape1[i],4))
a<-table.element(a,round(perf.se[i],4))
a<-table.element(a,round(perf.mse1[i],4))
a<-table.element(a,round(perf.rmse[i],4))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code