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R Software Module

rwasp_exponentialsmoothing.wasp

Title produced by software

Exponential Smoothing

Date of computation

Mon, 01 May 2017 12:26:15 +0100

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/2017/May/01/t14936383157jttnq1hrg9q8ar.htm/, Retrieved Thu, 16 May 2024 09:16:59 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Thu, 16 May 2024 09:16:59 +0200

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Estimated Impact

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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	1 seconds
R Server	'Sir Maurice George Kendall' @ kendall.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 1 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	1 seconds
R Server	'Sir Maurice George Kendall' @ kendall.wessa.net

Estimated Parameters of Exponential Smoothing
Parameter	Value
alpha	1
beta	0
gamma	FALSE

\begin{tabular}{lllllllll}
\hline
Estimated Parameters of Exponential Smoothing \tabularnewline
Parameter & Value \tabularnewline
alpha & 1 \tabularnewline
beta & 0 \tabularnewline
gamma & FALSE \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=1

[TABLE]
[ROW][C]Estimated Parameters of Exponential Smoothing[/C][/ROW]
[ROW][C]Parameter[/C][C]Value[/C][/ROW]
[ROW][C]alpha[/C][C]1[/C][/ROW]
[ROW][C]beta[/C][C]0[/C][/ROW]
[ROW][C]gamma[/C][C]FALSE[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=1

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

As an alternative you can also use a QR Code:

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

Estimated Parameters of Exponential Smoothing
Parameter	Value
alpha	1
beta	0
gamma	FALSE

Interpolation Forecasts of Exponential Smoothing
t	Observed	Fitted	Residuals
3	95.32	95.48	-0.160000000000011
4	96.04	95.46	0.580000000000013
5	99.65	96.18	3.47
6	100.85	99.79	1.05999999999999
7	108.18	100.99	7.19000000000001
8	108.18	108.32	-0.140000000000001
9	103.14	108.32	-5.18000000000001
10	99.71	103.28	-3.57000000000001
11	99.39	99.85	-0.459999999999994
12	98.99	99.53	-0.540000000000006
13	98.83	99.13	-0.299999999999997
14	99.52	98.97	0.549999999999997
15	99.5	99.66	-0.159999999999997
16	99.5	99.64	-0.140000000000001
17	99.39	99.64	-0.25
18	101.79	99.53	2.26000000000001
19	106.03	101.93	4.09999999999999
20	105.41	106.17	-0.760000000000005
21	104.32	105.55	-1.23
22	101.17	104.46	-3.28999999999999
23	99.79	101.31	-1.52
24	100.08	99.93	0.149999999999991
25	100.27	100.22	0.0499999999999972
26	101.63	100.41	1.22
27	101.74	101.77	-0.0300000000000011
28	103.73	101.88	1.85000000000001
29	103.29	103.87	-0.579999999999998
30	105.71	103.43	2.27999999999999
31	107.42	105.85	1.57000000000001
32	107.57	107.56	0.00999999999999091
33	105.13	107.71	-2.58
34	103.61	105.27	-1.66
35	102.35	103.75	-1.40000000000001
36	102.14	102.49	-0.349999999999994
37	104.32	102.28	2.03999999999999
38	104.69	104.46	0.230000000000004
39	106.02	104.83	1.19
40	104.78	106.16	-1.38
41	106.36	104.92	1.44
42	109.27	106.5	2.77
43	113.46	109.41	4.05
44	113.46	113.6	-0.140000000000001
45	110.61	113.6	-2.98999999999999
46	104.37	110.75	-6.38
47	103.82	104.51	-0.690000000000012
48	104.1	103.96	0.140000000000001

\begin{tabular}{lllllllll}
\hline
Interpolation Forecasts of Exponential Smoothing \tabularnewline
t & Observed & Fitted & Residuals \tabularnewline
3 & 95.32 & 95.48 & -0.160000000000011 \tabularnewline
4 & 96.04 & 95.46 & 0.580000000000013 \tabularnewline
5 & 99.65 & 96.18 & 3.47 \tabularnewline
6 & 100.85 & 99.79 & 1.05999999999999 \tabularnewline
7 & 108.18 & 100.99 & 7.19000000000001 \tabularnewline
8 & 108.18 & 108.32 & -0.140000000000001 \tabularnewline
9 & 103.14 & 108.32 & -5.18000000000001 \tabularnewline
10 & 99.71 & 103.28 & -3.57000000000001 \tabularnewline
11 & 99.39 & 99.85 & -0.459999999999994 \tabularnewline
12 & 98.99 & 99.53 & -0.540000000000006 \tabularnewline
13 & 98.83 & 99.13 & -0.299999999999997 \tabularnewline
14 & 99.52 & 98.97 & 0.549999999999997 \tabularnewline
15 & 99.5 & 99.66 & -0.159999999999997 \tabularnewline
16 & 99.5 & 99.64 & -0.140000000000001 \tabularnewline
17 & 99.39 & 99.64 & -0.25 \tabularnewline
18 & 101.79 & 99.53 & 2.26000000000001 \tabularnewline
19 & 106.03 & 101.93 & 4.09999999999999 \tabularnewline
20 & 105.41 & 106.17 & -0.760000000000005 \tabularnewline
21 & 104.32 & 105.55 & -1.23 \tabularnewline
22 & 101.17 & 104.46 & -3.28999999999999 \tabularnewline
23 & 99.79 & 101.31 & -1.52 \tabularnewline
24 & 100.08 & 99.93 & 0.149999999999991 \tabularnewline
25 & 100.27 & 100.22 & 0.0499999999999972 \tabularnewline
26 & 101.63 & 100.41 & 1.22 \tabularnewline
27 & 101.74 & 101.77 & -0.0300000000000011 \tabularnewline
28 & 103.73 & 101.88 & 1.85000000000001 \tabularnewline
29 & 103.29 & 103.87 & -0.579999999999998 \tabularnewline
30 & 105.71 & 103.43 & 2.27999999999999 \tabularnewline
31 & 107.42 & 105.85 & 1.57000000000001 \tabularnewline
32 & 107.57 & 107.56 & 0.00999999999999091 \tabularnewline
33 & 105.13 & 107.71 & -2.58 \tabularnewline
34 & 103.61 & 105.27 & -1.66 \tabularnewline
35 & 102.35 & 103.75 & -1.40000000000001 \tabularnewline
36 & 102.14 & 102.49 & -0.349999999999994 \tabularnewline
37 & 104.32 & 102.28 & 2.03999999999999 \tabularnewline
38 & 104.69 & 104.46 & 0.230000000000004 \tabularnewline
39 & 106.02 & 104.83 & 1.19 \tabularnewline
40 & 104.78 & 106.16 & -1.38 \tabularnewline
41 & 106.36 & 104.92 & 1.44 \tabularnewline
42 & 109.27 & 106.5 & 2.77 \tabularnewline
43 & 113.46 & 109.41 & 4.05 \tabularnewline
44 & 113.46 & 113.6 & -0.140000000000001 \tabularnewline
45 & 110.61 & 113.6 & -2.98999999999999 \tabularnewline
46 & 104.37 & 110.75 & -6.38 \tabularnewline
47 & 103.82 & 104.51 & -0.690000000000012 \tabularnewline
48 & 104.1 & 103.96 & 0.140000000000001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=2

[TABLE]
[ROW][C]Interpolation Forecasts of Exponential Smoothing[/C][/ROW]
[ROW][C]t[/C][C]Observed[/C][C]Fitted[/C][C]Residuals[/C][/ROW]
[ROW][C]3[/C][C]95.32[/C][C]95.48[/C][C]-0.160000000000011[/C][/ROW]
[ROW][C]4[/C][C]96.04[/C][C]95.46[/C][C]0.580000000000013[/C][/ROW]
[ROW][C]5[/C][C]99.65[/C][C]96.18[/C][C]3.47[/C][/ROW]
[ROW][C]6[/C][C]100.85[/C][C]99.79[/C][C]1.05999999999999[/C][/ROW]
[ROW][C]7[/C][C]108.18[/C][C]100.99[/C][C]7.19000000000001[/C][/ROW]
[ROW][C]8[/C][C]108.18[/C][C]108.32[/C][C]-0.140000000000001[/C][/ROW]
[ROW][C]9[/C][C]103.14[/C][C]108.32[/C][C]-5.18000000000001[/C][/ROW]
[ROW][C]10[/C][C]99.71[/C][C]103.28[/C][C]-3.57000000000001[/C][/ROW]
[ROW][C]11[/C][C]99.39[/C][C]99.85[/C][C]-0.459999999999994[/C][/ROW]
[ROW][C]12[/C][C]98.99[/C][C]99.53[/C][C]-0.540000000000006[/C][/ROW]
[ROW][C]13[/C][C]98.83[/C][C]99.13[/C][C]-0.299999999999997[/C][/ROW]
[ROW][C]14[/C][C]99.52[/C][C]98.97[/C][C]0.549999999999997[/C][/ROW]
[ROW][C]15[/C][C]99.5[/C][C]99.66[/C][C]-0.159999999999997[/C][/ROW]
[ROW][C]16[/C][C]99.5[/C][C]99.64[/C][C]-0.140000000000001[/C][/ROW]
[ROW][C]17[/C][C]99.39[/C][C]99.64[/C][C]-0.25[/C][/ROW]
[ROW][C]18[/C][C]101.79[/C][C]99.53[/C][C]2.26000000000001[/C][/ROW]
[ROW][C]19[/C][C]106.03[/C][C]101.93[/C][C]4.09999999999999[/C][/ROW]
[ROW][C]20[/C][C]105.41[/C][C]106.17[/C][C]-0.760000000000005[/C][/ROW]
[ROW][C]21[/C][C]104.32[/C][C]105.55[/C][C]-1.23[/C][/ROW]
[ROW][C]22[/C][C]101.17[/C][C]104.46[/C][C]-3.28999999999999[/C][/ROW]
[ROW][C]23[/C][C]99.79[/C][C]101.31[/C][C]-1.52[/C][/ROW]
[ROW][C]24[/C][C]100.08[/C][C]99.93[/C][C]0.149999999999991[/C][/ROW]
[ROW][C]25[/C][C]100.27[/C][C]100.22[/C][C]0.0499999999999972[/C][/ROW]
[ROW][C]26[/C][C]101.63[/C][C]100.41[/C][C]1.22[/C][/ROW]
[ROW][C]27[/C][C]101.74[/C][C]101.77[/C][C]-0.0300000000000011[/C][/ROW]
[ROW][C]28[/C][C]103.73[/C][C]101.88[/C][C]1.85000000000001[/C][/ROW]
[ROW][C]29[/C][C]103.29[/C][C]103.87[/C][C]-0.579999999999998[/C][/ROW]
[ROW][C]30[/C][C]105.71[/C][C]103.43[/C][C]2.27999999999999[/C][/ROW]
[ROW][C]31[/C][C]107.42[/C][C]105.85[/C][C]1.57000000000001[/C][/ROW]
[ROW][C]32[/C][C]107.57[/C][C]107.56[/C][C]0.00999999999999091[/C][/ROW]
[ROW][C]33[/C][C]105.13[/C][C]107.71[/C][C]-2.58[/C][/ROW]
[ROW][C]34[/C][C]103.61[/C][C]105.27[/C][C]-1.66[/C][/ROW]
[ROW][C]35[/C][C]102.35[/C][C]103.75[/C][C]-1.40000000000001[/C][/ROW]
[ROW][C]36[/C][C]102.14[/C][C]102.49[/C][C]-0.349999999999994[/C][/ROW]
[ROW][C]37[/C][C]104.32[/C][C]102.28[/C][C]2.03999999999999[/C][/ROW]
[ROW][C]38[/C][C]104.69[/C][C]104.46[/C][C]0.230000000000004[/C][/ROW]
[ROW][C]39[/C][C]106.02[/C][C]104.83[/C][C]1.19[/C][/ROW]
[ROW][C]40[/C][C]104.78[/C][C]106.16[/C][C]-1.38[/C][/ROW]
[ROW][C]41[/C][C]106.36[/C][C]104.92[/C][C]1.44[/C][/ROW]
[ROW][C]42[/C][C]109.27[/C][C]106.5[/C][C]2.77[/C][/ROW]
[ROW][C]43[/C][C]113.46[/C][C]109.41[/C][C]4.05[/C][/ROW]
[ROW][C]44[/C][C]113.46[/C][C]113.6[/C][C]-0.140000000000001[/C][/ROW]
[ROW][C]45[/C][C]110.61[/C][C]113.6[/C][C]-2.98999999999999[/C][/ROW]
[ROW][C]46[/C][C]104.37[/C][C]110.75[/C][C]-6.38[/C][/ROW]
[ROW][C]47[/C][C]103.82[/C][C]104.51[/C][C]-0.690000000000012[/C][/ROW]
[ROW][C]48[/C][C]104.1[/C][C]103.96[/C][C]0.140000000000001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&T=2

As an alternative you can also use a QR Code:

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

Interpolation Forecasts of Exponential Smoothing
t	Observed	Fitted	Residuals
3	95.32	95.48	-0.160000000000011
4	96.04	95.46	0.580000000000013
5	99.65	96.18	3.47
6	100.85	99.79	1.05999999999999
7	108.18	100.99	7.19000000000001
8	108.18	108.32	-0.140000000000001
9	103.14	108.32	-5.18000000000001
10	99.71	103.28	-3.57000000000001
11	99.39	99.85	-0.459999999999994
12	98.99	99.53	-0.540000000000006
13	98.83	99.13	-0.299999999999997
14	99.52	98.97	0.549999999999997
15	99.5	99.66	-0.159999999999997
16	99.5	99.64	-0.140000000000001
17	99.39	99.64	-0.25
18	101.79	99.53	2.26000000000001
19	106.03	101.93	4.09999999999999
20	105.41	106.17	-0.760000000000005
21	104.32	105.55	-1.23
22	101.17	104.46	-3.28999999999999
23	99.79	101.31	-1.52
24	100.08	99.93	0.149999999999991
25	100.27	100.22	0.0499999999999972
26	101.63	100.41	1.22
27	101.74	101.77	-0.0300000000000011
28	103.73	101.88	1.85000000000001
29	103.29	103.87	-0.579999999999998
30	105.71	103.43	2.27999999999999
31	107.42	105.85	1.57000000000001
32	107.57	107.56	0.00999999999999091
33	105.13	107.71	-2.58
34	103.61	105.27	-1.66
35	102.35	103.75	-1.40000000000001
36	102.14	102.49	-0.349999999999994
37	104.32	102.28	2.03999999999999
38	104.69	104.46	0.230000000000004
39	106.02	104.83	1.19
40	104.78	106.16	-1.38
41	106.36	104.92	1.44
42	109.27	106.5	2.77
43	113.46	109.41	4.05
44	113.46	113.6	-0.140000000000001
45	110.61	113.6	-2.98999999999999
46	104.37	110.75	-6.38
47	103.82	104.51	-0.690000000000012
48	104.1	103.96	0.140000000000001

Extrapolation Forecasts of Exponential Smoothing
t	Forecast	95% Lower Bound	95% Upper Bound
49	104.24	99.6099528041553	108.870047195845
50	104.38	97.8321244612089	110.927875538791
51	104.52	96.5005230153552	112.539476984645
52	104.66	95.3999056083107	113.920094391689
53	104.8	94.446899731059	115.153100268941
54	104.94	93.5987468851765	116.281253114824
55	105.08	92.830046561303	117.329953438697
56	105.22	92.1242489224179	118.315751077582
57	105.36	91.469858412466	119.250141587534
58	105.5	90.8585051870552	120.141494812945
59	105.64	90.2838706897461	120.996129310254
60	105.78	89.7410460307105	121.81895396929

\begin{tabular}{lllllllll}
\hline
Extrapolation Forecasts of Exponential Smoothing \tabularnewline
t & Forecast & 95% Lower Bound & 95% Upper Bound \tabularnewline
49 & 104.24 & 99.6099528041553 & 108.870047195845 \tabularnewline
50 & 104.38 & 97.8321244612089 & 110.927875538791 \tabularnewline
51 & 104.52 & 96.5005230153552 & 112.539476984645 \tabularnewline
52 & 104.66 & 95.3999056083107 & 113.920094391689 \tabularnewline
53 & 104.8 & 94.446899731059 & 115.153100268941 \tabularnewline
54 & 104.94 & 93.5987468851765 & 116.281253114824 \tabularnewline
55 & 105.08 & 92.830046561303 & 117.329953438697 \tabularnewline
56 & 105.22 & 92.1242489224179 & 118.315751077582 \tabularnewline
57 & 105.36 & 91.469858412466 & 119.250141587534 \tabularnewline
58 & 105.5 & 90.8585051870552 & 120.141494812945 \tabularnewline
59 & 105.64 & 90.2838706897461 & 120.996129310254 \tabularnewline
60 & 105.78 & 89.7410460307105 & 121.81895396929 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=3

[TABLE]
[ROW][C]Extrapolation Forecasts of Exponential Smoothing[/C][/ROW]
[ROW][C]t[/C][C]Forecast[/C][C]95% Lower Bound[/C][C]95% Upper Bound[/C][/ROW]
[ROW][C]49[/C][C]104.24[/C][C]99.6099528041553[/C][C]108.870047195845[/C][/ROW]
[ROW][C]50[/C][C]104.38[/C][C]97.8321244612089[/C][C]110.927875538791[/C][/ROW]
[ROW][C]51[/C][C]104.52[/C][C]96.5005230153552[/C][C]112.539476984645[/C][/ROW]
[ROW][C]52[/C][C]104.66[/C][C]95.3999056083107[/C][C]113.920094391689[/C][/ROW]
[ROW][C]53[/C][C]104.8[/C][C]94.446899731059[/C][C]115.153100268941[/C][/ROW]
[ROW][C]54[/C][C]104.94[/C][C]93.5987468851765[/C][C]116.281253114824[/C][/ROW]
[ROW][C]55[/C][C]105.08[/C][C]92.830046561303[/C][C]117.329953438697[/C][/ROW]
[ROW][C]56[/C][C]105.22[/C][C]92.1242489224179[/C][C]118.315751077582[/C][/ROW]
[ROW][C]57[/C][C]105.36[/C][C]91.469858412466[/C][C]119.250141587534[/C][/ROW]
[ROW][C]58[/C][C]105.5[/C][C]90.8585051870552[/C][C]120.141494812945[/C][/ROW]
[ROW][C]59[/C][C]105.64[/C][C]90.2838706897461[/C][C]120.996129310254[/C][/ROW]
[ROW][C]60[/C][C]105.78[/C][C]89.7410460307105[/C][C]121.81895396929[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&T=3

As an alternative you can also use a QR Code:

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

Extrapolation Forecasts of Exponential Smoothing
t	Forecast	95% Lower Bound	95% Upper Bound
49	104.24	99.6099528041553	108.870047195845
50	104.38	97.8321244612089	110.927875538791
51	104.52	96.5005230153552	112.539476984645
52	104.66	95.3999056083107	113.920094391689
53	104.8	94.446899731059	115.153100268941
54	104.94	93.5987468851765	116.281253114824
55	105.08	92.830046561303	117.329953438697
56	105.22	92.1242489224179	118.315751077582
57	105.36	91.469858412466	119.250141587534
58	105.5	90.8585051870552	120.141494812945
59	105.64	90.2838706897461	120.996129310254
60	105.78	89.7410460307105	121.81895396929

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 12 ; par2 = Double ; par3 = additive ;

Parameters (R input):

par1 = 12 ; par2 = Double ; par3 = additive ;

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

par1 <- as.numeric(par1)
if (par2 == 'Single') K <- 1
if (par2 == 'Double') K <- 2
if (par2 == 'Triple') K <- par1
nx <- length(x)
nxmK <- nx - K
x <- ts(x, frequency = par1)
if (par2 == 'Single') fit <- HoltWinters(x, gamma=F, beta=F)
if (par2 == 'Double') fit <- HoltWinters(x, gamma=F)
if (par2 == 'Triple') fit <- HoltWinters(x, seasonal=par3)
fit
myresid <- x - fit$fitted[,'xhat']
bitmap(file='test1.png')
op <- par(mfrow=c(2,1))
plot(fit,ylab='Observed (black) / Fitted (red)',main='Interpolation Fit of Exponential Smoothing')
plot(myresid,ylab='Residuals',main='Interpolation Prediction Errors')
par(op)
dev.off()
bitmap(file='test2.png')
p <- predict(fit, par1, prediction.interval=TRUE)
np <- length(p[,1])
plot(fit,p,ylab='Observed (black) / Fitted (red)',main='Extrapolation Fit of Exponential Smoothing')
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(myresid),lag.max = nx/2,main='Residual ACF')
spectrum(myresid,main='Residals Periodogram')
cpgram(myresid,main='Residal Cumulative Periodogram')
qqnorm(myresid,main='Residual Normal QQ Plot')
qqline(myresid)
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimated Parameters of Exponential Smoothing',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'Value',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'alpha',header=TRUE)
a<-table.element(a,fit$alpha)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'beta',header=TRUE)
a<-table.element(a,fit$beta)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'gamma',header=TRUE)
a<-table.element(a,fit$gamma)
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,'Interpolation Forecasts of Exponential Smoothing',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observed',header=TRUE)
a<-table.element(a,'Fitted',header=TRUE)
a<-table.element(a,'Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:nxmK) {
a<-table.row.start(a)
a<-table.element(a,i+K,header=TRUE)
a<-table.element(a,x[i+K])
a<-table.element(a,fit$fitted[i,'xhat'])
a<-table.element(a,myresid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Extrapolation Forecasts of Exponential Smoothing',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Forecast',header=TRUE)
a<-table.element(a,'95% Lower Bound',header=TRUE)
a<-table.element(a,'95% Upper Bound',header=TRUE)
a<-table.row.end(a)
for (i in 1:np) {
a<-table.row.start(a)
a<-table.element(a,nx+i,header=TRUE)
a<-table.element(a,p[i,'fit'])
a<-table.element(a,p[i,'lwr'])
a<-table.element(a,p[i,'upr'])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code