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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationMon, 12 Dec 2016 19:44:07 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/12/t14815682615bsekvgzdxlgwhm.htm/, Retrieved Thu, 03 Apr 2025 05:06:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=298963, Retrieved Thu, 03 Apr 2025 05:06:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact116

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [classical decompo...] [2016-12-12 18:44:07] [130d73899007e5ff8a4f636b9bcfb397] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4766
4815
4920
4936
4947
4904
4877
4899
4896
4937
5155
5119
5119
5147
5136
5135
5119
5153
5111
5109
5032
4989
4929
4919
4883
4850
4857
4850
4831
4793
4782
4809
4725
4698
4639
4528
4459
4405
4314
4252
4245
4177
4122
4034
3955
3928
3884
3826
3713
3672
3682
3615
3529
3529
3479
3446
3385
3296
3234
3188
3078
3018
2983

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time1 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298963&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]1 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=298963&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298963&T=0

As an alternative you can also use a QR Code:  
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 Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center






Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
14766NANA-19.9707NA
24815NANA-15.2727NA
34920NANA-5.64774NA
44936NANA-7.06441NA
54947NANA-1.96024NA
64904NANA20.1648NA
748774947.954945.622.32934-70.9543
848994990.744974.1716.571-91.7377
948964983.894997-13.1123-87.8877
1049375002.315014.29-11.9811-65.3106
1151555060.055029.7530.300294.9498
1251195052.945047.295.6439266.0644
1351195047.455067.42-19.970771.554
1451475070.645085.92-15.272776.3561
1551365094.695100.33-5.6477441.3144
1651355101.15108.17-7.0644133.8977
1751195098.965100.92-1.9602420.0436
1851535103.335083.1720.164849.6686
1951115067.3350652.3293443.6707
2051095059.365042.7916.57149.6373
2150325005.685018.79-13.112326.3207
2249894983.314995.29-11.98115.68941
2349295001.724971.4230.3002-72.7168
2449194950.064944.425.64392-31.0606
2548834895.744915.71-19.9707-12.7377
2648504874.234889.5-15.2727-24.2273
2748574858.564864.21-5.64774-1.56059
2848504832.234839.29-7.0644117.7727
2948314813.124815.08-1.9602417.8769
3047934806.874786.7120.1648-13.8731
3147824755.084752.752.3293426.9207
3248094733.114716.5416.57175.8873
3347254662.264675.38-13.112362.7373
3446984615.854627.83-11.981182.1477
3546394608.84578.530.300230.1998
3645284534.064528.425.64392-6.06059
3744594455.284475.25-19.97073.72066
3844054400.194415.46-15.27274.81441
3943144345.444351.08-5.64774-31.4356
4042524279.854286.92-7.06441-27.8523
4142454221.414223.38-1.9602423.5852
4241774182.834162.6720.1648-5.83142
4341224104.664102.332.3293417.3373
4440344057.284040.7116.571-23.2793
4539553970.723983.83-13.1123-15.721
4639283918.983930.96-11.98119.02274
4738843904.883874.5830.3002-20.8835
4838263823.393817.755.643922.60608
4937133743.993763.96-19.9707-30.9877
5036723697.393712.67-15.2727-25.3939
5136823658.773664.42-5.6477423.2311
5236153607.273614.33-7.064417.73108
5335293558.963560.92-1.96024-29.9564
5435293527.413507.2520.16481.58524
5534793456.543454.212.3293422.4623
5634463417.073400.516.57128.929
5733853331.013344.12-13.112353.9873
583296NANA-11.9811NA
593234NANA30.3002NA
603188NANA5.64392NA
613078NANA-19.9707NA
623018NANA-15.2727NA
632983NANA-5.64774NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 4766 & NA & NA & -19.9707 & NA \tabularnewline
2 & 4815 & NA & NA & -15.2727 & NA \tabularnewline
3 & 4920 & NA & NA & -5.64774 & NA \tabularnewline
4 & 4936 & NA & NA & -7.06441 & NA \tabularnewline
5 & 4947 & NA & NA & -1.96024 & NA \tabularnewline
6 & 4904 & NA & NA & 20.1648 & NA \tabularnewline
7 & 4877 & 4947.95 & 4945.62 & 2.32934 & -70.9543 \tabularnewline
8 & 4899 & 4990.74 & 4974.17 & 16.571 & -91.7377 \tabularnewline
9 & 4896 & 4983.89 & 4997 & -13.1123 & -87.8877 \tabularnewline
10 & 4937 & 5002.31 & 5014.29 & -11.9811 & -65.3106 \tabularnewline
11 & 5155 & 5060.05 & 5029.75 & 30.3002 & 94.9498 \tabularnewline
12 & 5119 & 5052.94 & 5047.29 & 5.64392 & 66.0644 \tabularnewline
13 & 5119 & 5047.45 & 5067.42 & -19.9707 & 71.554 \tabularnewline
14 & 5147 & 5070.64 & 5085.92 & -15.2727 & 76.3561 \tabularnewline
15 & 5136 & 5094.69 & 5100.33 & -5.64774 & 41.3144 \tabularnewline
16 & 5135 & 5101.1 & 5108.17 & -7.06441 & 33.8977 \tabularnewline
17 & 5119 & 5098.96 & 5100.92 & -1.96024 & 20.0436 \tabularnewline
18 & 5153 & 5103.33 & 5083.17 & 20.1648 & 49.6686 \tabularnewline
19 & 5111 & 5067.33 & 5065 & 2.32934 & 43.6707 \tabularnewline
20 & 5109 & 5059.36 & 5042.79 & 16.571 & 49.6373 \tabularnewline
21 & 5032 & 5005.68 & 5018.79 & -13.1123 & 26.3207 \tabularnewline
22 & 4989 & 4983.31 & 4995.29 & -11.9811 & 5.68941 \tabularnewline
23 & 4929 & 5001.72 & 4971.42 & 30.3002 & -72.7168 \tabularnewline
24 & 4919 & 4950.06 & 4944.42 & 5.64392 & -31.0606 \tabularnewline
25 & 4883 & 4895.74 & 4915.71 & -19.9707 & -12.7377 \tabularnewline
26 & 4850 & 4874.23 & 4889.5 & -15.2727 & -24.2273 \tabularnewline
27 & 4857 & 4858.56 & 4864.21 & -5.64774 & -1.56059 \tabularnewline
28 & 4850 & 4832.23 & 4839.29 & -7.06441 & 17.7727 \tabularnewline
29 & 4831 & 4813.12 & 4815.08 & -1.96024 & 17.8769 \tabularnewline
30 & 4793 & 4806.87 & 4786.71 & 20.1648 & -13.8731 \tabularnewline
31 & 4782 & 4755.08 & 4752.75 & 2.32934 & 26.9207 \tabularnewline
32 & 4809 & 4733.11 & 4716.54 & 16.571 & 75.8873 \tabularnewline
33 & 4725 & 4662.26 & 4675.38 & -13.1123 & 62.7373 \tabularnewline
34 & 4698 & 4615.85 & 4627.83 & -11.9811 & 82.1477 \tabularnewline
35 & 4639 & 4608.8 & 4578.5 & 30.3002 & 30.1998 \tabularnewline
36 & 4528 & 4534.06 & 4528.42 & 5.64392 & -6.06059 \tabularnewline
37 & 4459 & 4455.28 & 4475.25 & -19.9707 & 3.72066 \tabularnewline
38 & 4405 & 4400.19 & 4415.46 & -15.2727 & 4.81441 \tabularnewline
39 & 4314 & 4345.44 & 4351.08 & -5.64774 & -31.4356 \tabularnewline
40 & 4252 & 4279.85 & 4286.92 & -7.06441 & -27.8523 \tabularnewline
41 & 4245 & 4221.41 & 4223.38 & -1.96024 & 23.5852 \tabularnewline
42 & 4177 & 4182.83 & 4162.67 & 20.1648 & -5.83142 \tabularnewline
43 & 4122 & 4104.66 & 4102.33 & 2.32934 & 17.3373 \tabularnewline
44 & 4034 & 4057.28 & 4040.71 & 16.571 & -23.2793 \tabularnewline
45 & 3955 & 3970.72 & 3983.83 & -13.1123 & -15.721 \tabularnewline
46 & 3928 & 3918.98 & 3930.96 & -11.9811 & 9.02274 \tabularnewline
47 & 3884 & 3904.88 & 3874.58 & 30.3002 & -20.8835 \tabularnewline
48 & 3826 & 3823.39 & 3817.75 & 5.64392 & 2.60608 \tabularnewline
49 & 3713 & 3743.99 & 3763.96 & -19.9707 & -30.9877 \tabularnewline
50 & 3672 & 3697.39 & 3712.67 & -15.2727 & -25.3939 \tabularnewline
51 & 3682 & 3658.77 & 3664.42 & -5.64774 & 23.2311 \tabularnewline
52 & 3615 & 3607.27 & 3614.33 & -7.06441 & 7.73108 \tabularnewline
53 & 3529 & 3558.96 & 3560.92 & -1.96024 & -29.9564 \tabularnewline
54 & 3529 & 3527.41 & 3507.25 & 20.1648 & 1.58524 \tabularnewline
55 & 3479 & 3456.54 & 3454.21 & 2.32934 & 22.4623 \tabularnewline
56 & 3446 & 3417.07 & 3400.5 & 16.571 & 28.929 \tabularnewline
57 & 3385 & 3331.01 & 3344.12 & -13.1123 & 53.9873 \tabularnewline
58 & 3296 & NA & NA & -11.9811 & NA \tabularnewline
59 & 3234 & NA & NA & 30.3002 & NA \tabularnewline
60 & 3188 & NA & NA & 5.64392 & NA \tabularnewline
61 & 3078 & NA & NA & -19.9707 & NA \tabularnewline
62 & 3018 & NA & NA & -15.2727 & NA \tabularnewline
63 & 2983 & NA & NA & -5.64774 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298963&T=1

[TABLE]
[ROW][C]Classical Decomposition by Moving Averages[/C][/ROW]
[ROW][C]t[/C][C]Observations[/C][C]Fit[/C][C]Trend[/C][C]Seasonal[/C][C]Random[/C][/ROW]
[ROW][C]1[/C][C]4766[/C][C]NA[/C][C]NA[/C][C]-19.9707[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]4815[/C][C]NA[/C][C]NA[/C][C]-15.2727[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]4920[/C][C]NA[/C][C]NA[/C][C]-5.64774[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]4936[/C][C]NA[/C][C]NA[/C][C]-7.06441[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]4947[/C][C]NA[/C][C]NA[/C][C]-1.96024[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]4904[/C][C]NA[/C][C]NA[/C][C]20.1648[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]4877[/C][C]4947.95[/C][C]4945.62[/C][C]2.32934[/C][C]-70.9543[/C][/ROW]
[ROW][C]8[/C][C]4899[/C][C]4990.74[/C][C]4974.17[/C][C]16.571[/C][C]-91.7377[/C][/ROW]
[ROW][C]9[/C][C]4896[/C][C]4983.89[/C][C]4997[/C][C]-13.1123[/C][C]-87.8877[/C][/ROW]
[ROW][C]10[/C][C]4937[/C][C]5002.31[/C][C]5014.29[/C][C]-11.9811[/C][C]-65.3106[/C][/ROW]
[ROW][C]11[/C][C]5155[/C][C]5060.05[/C][C]5029.75[/C][C]30.3002[/C][C]94.9498[/C][/ROW]
[ROW][C]12[/C][C]5119[/C][C]5052.94[/C][C]5047.29[/C][C]5.64392[/C][C]66.0644[/C][/ROW]
[ROW][C]13[/C][C]5119[/C][C]5047.45[/C][C]5067.42[/C][C]-19.9707[/C][C]71.554[/C][/ROW]
[ROW][C]14[/C][C]5147[/C][C]5070.64[/C][C]5085.92[/C][C]-15.2727[/C][C]76.3561[/C][/ROW]
[ROW][C]15[/C][C]5136[/C][C]5094.69[/C][C]5100.33[/C][C]-5.64774[/C][C]41.3144[/C][/ROW]
[ROW][C]16[/C][C]5135[/C][C]5101.1[/C][C]5108.17[/C][C]-7.06441[/C][C]33.8977[/C][/ROW]
[ROW][C]17[/C][C]5119[/C][C]5098.96[/C][C]5100.92[/C][C]-1.96024[/C][C]20.0436[/C][/ROW]
[ROW][C]18[/C][C]5153[/C][C]5103.33[/C][C]5083.17[/C][C]20.1648[/C][C]49.6686[/C][/ROW]
[ROW][C]19[/C][C]5111[/C][C]5067.33[/C][C]5065[/C][C]2.32934[/C][C]43.6707[/C][/ROW]
[ROW][C]20[/C][C]5109[/C][C]5059.36[/C][C]5042.79[/C][C]16.571[/C][C]49.6373[/C][/ROW]
[ROW][C]21[/C][C]5032[/C][C]5005.68[/C][C]5018.79[/C][C]-13.1123[/C][C]26.3207[/C][/ROW]
[ROW][C]22[/C][C]4989[/C][C]4983.31[/C][C]4995.29[/C][C]-11.9811[/C][C]5.68941[/C][/ROW]
[ROW][C]23[/C][C]4929[/C][C]5001.72[/C][C]4971.42[/C][C]30.3002[/C][C]-72.7168[/C][/ROW]
[ROW][C]24[/C][C]4919[/C][C]4950.06[/C][C]4944.42[/C][C]5.64392[/C][C]-31.0606[/C][/ROW]
[ROW][C]25[/C][C]4883[/C][C]4895.74[/C][C]4915.71[/C][C]-19.9707[/C][C]-12.7377[/C][/ROW]
[ROW][C]26[/C][C]4850[/C][C]4874.23[/C][C]4889.5[/C][C]-15.2727[/C][C]-24.2273[/C][/ROW]
[ROW][C]27[/C][C]4857[/C][C]4858.56[/C][C]4864.21[/C][C]-5.64774[/C][C]-1.56059[/C][/ROW]
[ROW][C]28[/C][C]4850[/C][C]4832.23[/C][C]4839.29[/C][C]-7.06441[/C][C]17.7727[/C][/ROW]
[ROW][C]29[/C][C]4831[/C][C]4813.12[/C][C]4815.08[/C][C]-1.96024[/C][C]17.8769[/C][/ROW]
[ROW][C]30[/C][C]4793[/C][C]4806.87[/C][C]4786.71[/C][C]20.1648[/C][C]-13.8731[/C][/ROW]
[ROW][C]31[/C][C]4782[/C][C]4755.08[/C][C]4752.75[/C][C]2.32934[/C][C]26.9207[/C][/ROW]
[ROW][C]32[/C][C]4809[/C][C]4733.11[/C][C]4716.54[/C][C]16.571[/C][C]75.8873[/C][/ROW]
[ROW][C]33[/C][C]4725[/C][C]4662.26[/C][C]4675.38[/C][C]-13.1123[/C][C]62.7373[/C][/ROW]
[ROW][C]34[/C][C]4698[/C][C]4615.85[/C][C]4627.83[/C][C]-11.9811[/C][C]82.1477[/C][/ROW]
[ROW][C]35[/C][C]4639[/C][C]4608.8[/C][C]4578.5[/C][C]30.3002[/C][C]30.1998[/C][/ROW]
[ROW][C]36[/C][C]4528[/C][C]4534.06[/C][C]4528.42[/C][C]5.64392[/C][C]-6.06059[/C][/ROW]
[ROW][C]37[/C][C]4459[/C][C]4455.28[/C][C]4475.25[/C][C]-19.9707[/C][C]3.72066[/C][/ROW]
[ROW][C]38[/C][C]4405[/C][C]4400.19[/C][C]4415.46[/C][C]-15.2727[/C][C]4.81441[/C][/ROW]
[ROW][C]39[/C][C]4314[/C][C]4345.44[/C][C]4351.08[/C][C]-5.64774[/C][C]-31.4356[/C][/ROW]
[ROW][C]40[/C][C]4252[/C][C]4279.85[/C][C]4286.92[/C][C]-7.06441[/C][C]-27.8523[/C][/ROW]
[ROW][C]41[/C][C]4245[/C][C]4221.41[/C][C]4223.38[/C][C]-1.96024[/C][C]23.5852[/C][/ROW]
[ROW][C]42[/C][C]4177[/C][C]4182.83[/C][C]4162.67[/C][C]20.1648[/C][C]-5.83142[/C][/ROW]
[ROW][C]43[/C][C]4122[/C][C]4104.66[/C][C]4102.33[/C][C]2.32934[/C][C]17.3373[/C][/ROW]
[ROW][C]44[/C][C]4034[/C][C]4057.28[/C][C]4040.71[/C][C]16.571[/C][C]-23.2793[/C][/ROW]
[ROW][C]45[/C][C]3955[/C][C]3970.72[/C][C]3983.83[/C][C]-13.1123[/C][C]-15.721[/C][/ROW]
[ROW][C]46[/C][C]3928[/C][C]3918.98[/C][C]3930.96[/C][C]-11.9811[/C][C]9.02274[/C][/ROW]
[ROW][C]47[/C][C]3884[/C][C]3904.88[/C][C]3874.58[/C][C]30.3002[/C][C]-20.8835[/C][/ROW]
[ROW][C]48[/C][C]3826[/C][C]3823.39[/C][C]3817.75[/C][C]5.64392[/C][C]2.60608[/C][/ROW]
[ROW][C]49[/C][C]3713[/C][C]3743.99[/C][C]3763.96[/C][C]-19.9707[/C][C]-30.9877[/C][/ROW]
[ROW][C]50[/C][C]3672[/C][C]3697.39[/C][C]3712.67[/C][C]-15.2727[/C][C]-25.3939[/C][/ROW]
[ROW][C]51[/C][C]3682[/C][C]3658.77[/C][C]3664.42[/C][C]-5.64774[/C][C]23.2311[/C][/ROW]
[ROW][C]52[/C][C]3615[/C][C]3607.27[/C][C]3614.33[/C][C]-7.06441[/C][C]7.73108[/C][/ROW]
[ROW][C]53[/C][C]3529[/C][C]3558.96[/C][C]3560.92[/C][C]-1.96024[/C][C]-29.9564[/C][/ROW]
[ROW][C]54[/C][C]3529[/C][C]3527.41[/C][C]3507.25[/C][C]20.1648[/C][C]1.58524[/C][/ROW]
[ROW][C]55[/C][C]3479[/C][C]3456.54[/C][C]3454.21[/C][C]2.32934[/C][C]22.4623[/C][/ROW]
[ROW][C]56[/C][C]3446[/C][C]3417.07[/C][C]3400.5[/C][C]16.571[/C][C]28.929[/C][/ROW]
[ROW][C]57[/C][C]3385[/C][C]3331.01[/C][C]3344.12[/C][C]-13.1123[/C][C]53.9873[/C][/ROW]
[ROW][C]58[/C][C]3296[/C][C]NA[/C][C]NA[/C][C]-11.9811[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]3234[/C][C]NA[/C][C]NA[/C][C]30.3002[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]3188[/C][C]NA[/C][C]NA[/C][C]5.64392[/C][C]NA[/C][/ROW]
[ROW][C]61[/C][C]3078[/C][C]NA[/C][C]NA[/C][C]-19.9707[/C][C]NA[/C][/ROW]
[ROW][C]62[/C][C]3018[/C][C]NA[/C][C]NA[/C][C]-15.2727[/C][C]NA[/C][/ROW]
[ROW][C]63[/C][C]2983[/C][C]NA[/C][C]NA[/C][C]-5.64774[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298963&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298963&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
14766NANA-19.9707NA
24815NANA-15.2727NA
34920NANA-5.64774NA
44936NANA-7.06441NA
54947NANA-1.96024NA
64904NANA20.1648NA
748774947.954945.622.32934-70.9543
848994990.744974.1716.571-91.7377
948964983.894997-13.1123-87.8877
1049375002.315014.29-11.9811-65.3106
1151555060.055029.7530.300294.9498
1251195052.945047.295.6439266.0644
1351195047.455067.42-19.970771.554
1451475070.645085.92-15.272776.3561
1551365094.695100.33-5.6477441.3144
1651355101.15108.17-7.0644133.8977
1751195098.965100.92-1.9602420.0436
1851535103.335083.1720.164849.6686
1951115067.3350652.3293443.6707
2051095059.365042.7916.57149.6373
2150325005.685018.79-13.112326.3207
2249894983.314995.29-11.98115.68941
2349295001.724971.4230.3002-72.7168
2449194950.064944.425.64392-31.0606
2548834895.744915.71-19.9707-12.7377
2648504874.234889.5-15.2727-24.2273
2748574858.564864.21-5.64774-1.56059
2848504832.234839.29-7.0644117.7727
2948314813.124815.08-1.9602417.8769
3047934806.874786.7120.1648-13.8731
3147824755.084752.752.3293426.9207
3248094733.114716.5416.57175.8873
3347254662.264675.38-13.112362.7373
3446984615.854627.83-11.981182.1477
3546394608.84578.530.300230.1998
3645284534.064528.425.64392-6.06059
3744594455.284475.25-19.97073.72066
3844054400.194415.46-15.27274.81441
3943144345.444351.08-5.64774-31.4356
4042524279.854286.92-7.06441-27.8523
4142454221.414223.38-1.9602423.5852
4241774182.834162.6720.1648-5.83142
4341224104.664102.332.3293417.3373
4440344057.284040.7116.571-23.2793
4539553970.723983.83-13.1123-15.721
4639283918.983930.96-11.98119.02274
4738843904.883874.5830.3002-20.8835
4838263823.393817.755.643922.60608
4937133743.993763.96-19.9707-30.9877
5036723697.393712.67-15.2727-25.3939
5136823658.773664.42-5.6477423.2311
5236153607.273614.33-7.064417.73108
5335293558.963560.92-1.96024-29.9564
5435293527.413507.2520.16481.58524
5534793456.543454.212.3293422.4623
5634463417.073400.516.57128.929
5733853331.013344.12-13.112353.9873
583296NANA-11.9811NA
593234NANA30.3002NA
603188NANA5.64392NA
613078NANA-19.9707NA
623018NANA-15.2727NA
632983NANA-5.64774NA


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = additive ; par2 = 12 ;
Parameters (R input):
par1 = additive ; par2 = 12 ;
R code (references can be found in the software module):
par2 <- '12'
par1 <- 'additive'
par2 <- as.numeric(par2)
x <- ts(x,freq=par2)
m <- decompose(x,type=par1)
m$figure
bitmap(file='test1.png')
plot(m)
dev.off()
mylagmax <- length(x)/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(as.numeric(m$trend),na.action=na.pass,lag.max = mylagmax,main='Trend')
acf(as.numeric(m$seasonal),na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(as.numeric(m$random),na.action=na.pass,lag.max = mylagmax,main='Random')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
spectrum(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
spectrum(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
cpgram(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
cpgram(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Classical Decomposition by Moving Averages',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observations',header=TRUE)
a<-table.element(a,'Fit',header=TRUE)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Random',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(m$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,signif(m$trend[i]+m$seasonal[i],6)) else a<-table.element(a,signif(m$trend[i]*m$seasonal[i],6))
a<-table.element(a,signif(m$trend[i],6))
a<-table.element(a,signif(m$seasonal[i],6))
a<-table.element(a,signif(m$random[i],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')