diss: TSclust Dissimilarity Computation
In TSclust: Time Series Clustering Utilities
Description
Usage
Arguments
Details
Value
Author(s)
References
See Also
Examples
Description
Computes the dissimilarity matrix of the given numeric matrix, list, data.frame or mts object using the selected TSclust dissimilarity method.
Usage
1
Arguments
SERIES
Numeric matrix, list, data.frame or mts object. Numeric matrices are interpreted row-wise (one series per row) meanwhile data.frame and mts objects are interpredted column-wise.
METHOD
the dissimilarity measure to be used. This must be one of "ACF", "AR.LPC.CEPS", "AR.MAH", "AR.PIC", "CDM", "CID", "COR", "CORT", "DTWARP", "DWT", "EUCL", "FRECHET", INT.PER",
"NCD", "PACF", "PDC", PER", "PRED", "MINDIST.SAX", "SPEC.LLR", "SPEC.GLK" or "SPEC.ISD". Any unambiguous substring can be given.
See details for individual usage.
...
Additional arguments for the selected method.
Details
SERIES argument can be a numeric matrix, with one row per series, a list object with one numeric vector per element, a data.frame or a mts object.
Some methods can have additional arguments. See the individual help page for each dissimilarity method, detailed below.
Methods that have arguments that require one value per time series in series must provide so using a vector, a matrix (in the case of a multivalued argument) or a list when appropiate. In the case of a matrix, the values are conveyed row-wise. See the AR.LPC.CEPS example below.
"ACF" Autocorrelation-based method. See diss.ACF.
"AR.LPC.CEPS" Linear Predictive Coding ARIMA method. This method has two value-per-series arguments, the ARIMA order, and the seasonality.See diss.AR.LPC.CEPS.
"AR.MAH" Model-based ARMA method. See diss.AR.MAH.
"AR.PIC" Model-based ARMA method. This method has a value-per-series argument, the ARIMA order. See diss.AR.PIC.
"CDM" Compression-based dissimilarity method. See diss.CDM.
"CID" Complexity-Invariant distance. See diss.CID.
"COR" Correlation-based method. See diss.COR.
"CORT" Temporal Correlation and Raw values method. See diss.CORT.
"DTWARP" Dynamic Time Warping method. See diss.DTWARP.
"DWT" Discrete wavelet transform method. See diss.DWT.
"EUCL" Euclidean distance. See diss.EUCL. For many more convetional distances, see link[stats]{dist}, though you may need to transpose the dataset.
"FRECHET" Frechet distance. See diss.FRECHET.
"INT.PER" Integrate Periodogram-based method. See diss.INT.PER.
"NCD" Normalized Compression Distance. See diss.NCD.
"PACF" Partial Autocorrelation-based method. See diss.PACF.
"PDC" Permutation distribution divergence. Uses the pdc package. See pdcDist for
additional arguments and details. Note that series given by numeric matrices are interpreted row-wise and not column-wise, opposite as in pdcDist.
"PER" Periodogram-based method. See diss.PER.
"PRED" Prediction Density-based method. This method has two value-per-series agument, the logarithm and difference transform. See diss.PRED.
"MINDIST.SAX" Distance that lower bounds the Euclidean, based on the Symbolic Aggregate approXimation measure. See diss.MINDIST.SAX.
"SPEC.LLR" Spectral Density by Local-Linear Estimation method. See diss.SPEC.LLR.
"SPEC.GLK" Log-Spectra Generalized Likelihood Ratio test method. See diss.SPEC.GLK.
"SPEC.ISD" Intregated Squared Differences between Log-Spectras method. See diss.SPEC.ISD.
Value
dist
A dist object with the pairwise dissimilarities between series.
Some methods produce additional output, see their respective documentation pages for more information.
Author(s)
Pablo Montero Manso, José Antonio Vilar.
References
Montero, P and Vilar, J.A. (2014) TSclust: An R Package for Time Series Clustering. Journal of Statistical Software, 62(1), 1-43. http://www.jstatsoft.org/v62/i01/.
See Also
Examples
1
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3
4
5
6
7
8
9
10
11
12
13
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23
data(electricity)
diss(electricity, METHOD="INT.PER", normalize=FALSE)
## Example of multivalued, one per series argument
## The AR.LPC.CEPS dissimilarity allows the specification of the ARIMA model for each series
## Create three sample time series and a mts object
x <- arima.sim(model=list(ar=c(0.4,-0.1)), n =100, n.start=100)
y <- arima.sim(model=list(ar=c(0.9)), n =100, n.start=100)
z <- arima.sim(model=list(ar=c(0.5, 0.2)), n =100, n.start=100)
seriests <- rbind(x,y,z)
## If we want to provide the ARIMA order for each series
## and use it with AR.LPC.CEPS, we create a matrix with the row-wise orders
orderx <- c(2,0,0)
ordery <- c(1,0,0)
orderz <- c(2,0,0)
orders = rbind(orderx, ordery, orderz)
diss( seriests, METHOD="AR.LPC.CEPS", k=30, order= orders )
##other examples
diss( seriests, METHOD="MINDIST.SAX", w=10, alpha=4 )
diss( seriests, METHOD="PDC" )
Example output
Loading required package: wmtsa
Loading required package: pdc
Loading required package: cluster
Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl_init' failed, running with rgl.useNULL = TRUE
3: .onUnload failed in unloadNamespace() for 'rgl', details:
call: fun(...)
error: object 'rgl_quit' not found
H1 H2 H3 H4 H5 H6
H2 994.5329
H3 1828.2394 1688.9145
H4 5776.5579 4796.1143 4105.4534
H5 7801.2038 6806.6709 6130.0993 2024.6459
H6 6096.6543 5102.1214 4425.5498 393.7324 1745.1878
H7 6461.8366 5467.3037 4790.7321 2352.6167 2831.8113 1979.5745
H8 14426.8555 13432.3226 12755.7510 8650.2976 6625.6517 8330.2012
H9 15034.2728 14039.7399 13363.1683 9257.7149 7233.0691 8937.6186
H10 18330.2062 17335.6734 16659.1018 12553.6484 10529.0025 12233.5520
H11 22302.9829 21308.4500 20631.8784 16526.4250 14501.7791 16206.3286
H12 23927.9553 22933.4224 22256.8508 18151.3974 16126.7515 17831.3010
H13 28991.9060 27997.3731 27320.8015 23215.3481 21190.7023 22895.2517
H14 29451.2339 28456.7010 27780.1294 23674.6760 21650.0301 23354.5796
H15 23863.8493 22869.3164 22192.7448 18087.2914 16062.6455 17767.1950
H16 21463.0068 20468.4739 19791.9023 15686.4489 13661.8031 15366.3526
H17 26163.8247 25169.2919 24492.7203 20387.2669 18362.6210 20067.1705
H18 33097.5059 32102.9730 31426.4014 27320.9480 25296.3021 27000.8516
H19 55440.4270 54445.8942 53769.3226 49663.8692 47639.2233 49343.7728
H20 64974.8172 63980.2843 63303.7127 59198.2593 57173.6134 58878.1629
H21 53023.8451 52029.3122 51352.7406 47247.2872 45222.6413 46927.1908
H22 25104.6839 24110.1510 23433.5794 19328.1260 17303.4801 19008.0296
H23 18947.0301 17952.4972 17275.9256 13170.4722 11145.8263 12850.3758
H24 6353.4183 5358.8854 4682.3138 1986.9202 2689.6643 1634.0095
H7 H8 H9 H10 H11 H12
H2
H3
H4
H5
H6
H7
H8 7971.7588
H9 8592.4687 723.1394
H10 11873.1384 3903.3508 3295.9334
H11 15841.1463 7876.1274 7268.7101 3972.7767
H12 17466.1187 9501.0998 8893.6825 5597.7490 1624.9724
H13 22530.0694 14565.0505 13957.6332 10661.6998 6688.9231 5063.9507
H14 22989.3973 15024.3784 14416.9611 11121.0277 7148.2510 5523.2786
H15 17402.0127 9436.9938 8829.5765 5533.6431 1560.8664 387.8042
H16 15001.1702 7036.1513 6428.7340 3132.8006 911.4519 2497.1351
H17 19701.9882 11736.9693 11129.5519 7833.6185 3860.8418 2235.8695
H18 26635.6693 18670.6504 18063.2331 14767.2997 10794.5230 9169.5506
H19 48978.5905 41013.5716 40406.1542 37110.2208 33137.4441 31512.4718
H20 58512.9806 50547.9617 49940.5443 46644.6109 42671.8343 41046.8619
H21 46562.0085 38596.9896 37989.5723 34693.6389 30720.8622 29095.8898
H22 18642.8473 10677.8284 10070.4111 6774.4777 2801.7010 1191.3747
H23 12485.1935 4520.1746 3912.7572 745.3083 3403.6173 5006.4268
H24 469.6939 8076.7738 8697.4838 11978.1535 15949.5646 17574.5370
H13 H14 H15 H16 H17 H18
H2
H3
H4
H5
H6
H7
H8
H9
H10
H11
H12
H13
H14 459.3279
H15 5128.0567 5587.3846
H16 7528.8992 7988.2271 2400.8425
H17 2861.6808 3302.2374 2299.9754 4700.8179
H18 4105.5999 3646.2720 9233.6566 11634.4991 6933.6812
H19 26448.5210 25989.1932 31576.5778 33977.4202 29276.6023 22342.9212
H20 35982.9112 35523.5833 41110.9679 43511.8103 38810.9924 31877.3113
H21 24031.9391 23572.6112 29159.9958 31560.8383 26860.0204 19936.4869
H22 3887.2221 4346.5500 1292.2739 3688.5098 1060.6784 7992.8220
H23 10044.8759 10504.2038 4916.8192 2515.9768 7216.7947 14150.4758
H24 22638.4877 23097.8156 17510.4310 15109.5885 19810.4064 26744.0876
H19 H20 H21 H22 H23
H2
H3
H4
H5
H6
H7
H8
H9
H10
H11
H12
H13
H14
H15
H16
H17
H18
H19
H20 9547.4716
H21 2425.1197 11950.9721
H22 30335.7432 39870.1333 27919.1612
H23 36493.3970 46027.7871 34076.8150 6197.8015
H24 49087.0087 58621.3988 46670.4268 18751.2656 12593.6117
x y
y 0.6147099
z 0.5347802 1.0301534
x y
y 3.694333
z 0.000000 2.132924
x y
y 0.4806243
z 0.2695998 0.3742622
TSclust documentation built on July 23, 2020, 1:07 a.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Details Value Author(s) References See Also Examples
Description
Computes the dissimilarity matrix of the given numeric matrix, list, data.frame or mts object using the selected TSclust dissimilarity method.
Usage
1 |
Arguments
SERIES |
Numeric matrix, |
METHOD |
the dissimilarity measure to be used. This must be one of "ACF", "AR.LPC.CEPS", "AR.MAH", "AR.PIC", "CDM", "CID", "COR", "CORT", "DTWARP", "DWT", "EUCL", "FRECHET", INT.PER", "NCD", "PACF", "PDC", PER", "PRED", "MINDIST.SAX", "SPEC.LLR", "SPEC.GLK" or "SPEC.ISD". Any unambiguous substring can be given. See details for individual usage. |
... |
Additional arguments for the selected method. |
Details
SERIES argument can be a numeric matrix, with one row per series, a list object with one numeric vector per element, a data.frame or a mts object.
Some methods can have additional arguments. See the individual help page for each dissimilarity method, detailed below.
Methods that have arguments that require one value per time series in series must provide so using a vector, a matrix (in the case of a multivalued argument) or a list when appropiate. In the case of a matrix, the values are conveyed row-wise. See the AR.LPC.CEPS example below.
"ACF" Autocorrelation-based method. See
diss.ACF."AR.LPC.CEPS" Linear Predictive Coding ARIMA method. This method has two value-per-series arguments, the ARIMA order, and the seasonality.See
diss.AR.LPC.CEPS."AR.MAH" Model-based ARMA method. See
diss.AR.MAH."AR.PIC" Model-based ARMA method. This method has a value-per-series argument, the ARIMA order. See
diss.AR.PIC."CDM" Compression-based dissimilarity method. See
diss.CDM."CID" Complexity-Invariant distance. See
diss.CID."COR" Correlation-based method. See
diss.COR."CORT" Temporal Correlation and Raw values method. See
diss.CORT."DTWARP" Dynamic Time Warping method. See
diss.DTWARP."DWT" Discrete wavelet transform method. See
diss.DWT."EUCL" Euclidean distance. See
diss.EUCL. For many more convetional distances, seelink[stats]{dist}, though you may need to transpose the dataset."FRECHET" Frechet distance. See
diss.FRECHET."INT.PER" Integrate Periodogram-based method. See
diss.INT.PER."NCD" Normalized Compression Distance. See
diss.NCD."PACF" Partial Autocorrelation-based method. See
diss.PACF."PDC" Permutation distribution divergence. Uses the
pdcpackage. SeepdcDistfor additional arguments and details. Note that series given by numeric matrices are interpreted row-wise and not column-wise, opposite as inpdcDist."PER" Periodogram-based method. See
diss.PER."PRED" Prediction Density-based method. This method has two value-per-series agument, the logarithm and difference transform. See
diss.PRED."MINDIST.SAX" Distance that lower bounds the Euclidean, based on the Symbolic Aggregate approXimation measure. See
diss.MINDIST.SAX."SPEC.LLR" Spectral Density by Local-Linear Estimation method. See
diss.SPEC.LLR."SPEC.GLK" Log-Spectra Generalized Likelihood Ratio test method. See
diss.SPEC.GLK."SPEC.ISD" Intregated Squared Differences between Log-Spectras method. See
diss.SPEC.ISD.
Value
dist |
A |
Some methods produce additional output, see their respective documentation pages for more information.
Author(s)
Pablo Montero Manso, José Antonio Vilar.
References
Montero, P and Vilar, J.A. (2014) TSclust: An R Package for Time Series Clustering. Journal of Statistical Software, 62(1), 1-43. http://www.jstatsoft.org/v62/i01/.
See Also
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | data(electricity)
diss(electricity, METHOD="INT.PER", normalize=FALSE)
## Example of multivalued, one per series argument
## The AR.LPC.CEPS dissimilarity allows the specification of the ARIMA model for each series
## Create three sample time series and a mts object
x <- arima.sim(model=list(ar=c(0.4,-0.1)), n =100, n.start=100)
y <- arima.sim(model=list(ar=c(0.9)), n =100, n.start=100)
z <- arima.sim(model=list(ar=c(0.5, 0.2)), n =100, n.start=100)
seriests <- rbind(x,y,z)
## If we want to provide the ARIMA order for each series
## and use it with AR.LPC.CEPS, we create a matrix with the row-wise orders
orderx <- c(2,0,0)
ordery <- c(1,0,0)
orderz <- c(2,0,0)
orders = rbind(orderx, ordery, orderz)
diss( seriests, METHOD="AR.LPC.CEPS", k=30, order= orders )
##other examples
diss( seriests, METHOD="MINDIST.SAX", w=10, alpha=4 )
diss( seriests, METHOD="PDC" )
|
Example output
Loading required package: wmtsa
Loading required package: pdc
Loading required package: cluster
Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl_init' failed, running with rgl.useNULL = TRUE
3: .onUnload failed in unloadNamespace() for 'rgl', details:
call: fun(...)
error: object 'rgl_quit' not found
H1 H2 H3 H4 H5 H6
H2 994.5329
H3 1828.2394 1688.9145
H4 5776.5579 4796.1143 4105.4534
H5 7801.2038 6806.6709 6130.0993 2024.6459
H6 6096.6543 5102.1214 4425.5498 393.7324 1745.1878
H7 6461.8366 5467.3037 4790.7321 2352.6167 2831.8113 1979.5745
H8 14426.8555 13432.3226 12755.7510 8650.2976 6625.6517 8330.2012
H9 15034.2728 14039.7399 13363.1683 9257.7149 7233.0691 8937.6186
H10 18330.2062 17335.6734 16659.1018 12553.6484 10529.0025 12233.5520
H11 22302.9829 21308.4500 20631.8784 16526.4250 14501.7791 16206.3286
H12 23927.9553 22933.4224 22256.8508 18151.3974 16126.7515 17831.3010
H13 28991.9060 27997.3731 27320.8015 23215.3481 21190.7023 22895.2517
H14 29451.2339 28456.7010 27780.1294 23674.6760 21650.0301 23354.5796
H15 23863.8493 22869.3164 22192.7448 18087.2914 16062.6455 17767.1950
H16 21463.0068 20468.4739 19791.9023 15686.4489 13661.8031 15366.3526
H17 26163.8247 25169.2919 24492.7203 20387.2669 18362.6210 20067.1705
H18 33097.5059 32102.9730 31426.4014 27320.9480 25296.3021 27000.8516
H19 55440.4270 54445.8942 53769.3226 49663.8692 47639.2233 49343.7728
H20 64974.8172 63980.2843 63303.7127 59198.2593 57173.6134 58878.1629
H21 53023.8451 52029.3122 51352.7406 47247.2872 45222.6413 46927.1908
H22 25104.6839 24110.1510 23433.5794 19328.1260 17303.4801 19008.0296
H23 18947.0301 17952.4972 17275.9256 13170.4722 11145.8263 12850.3758
H24 6353.4183 5358.8854 4682.3138 1986.9202 2689.6643 1634.0095
H7 H8 H9 H10 H11 H12
H2
H3
H4
H5
H6
H7
H8 7971.7588
H9 8592.4687 723.1394
H10 11873.1384 3903.3508 3295.9334
H11 15841.1463 7876.1274 7268.7101 3972.7767
H12 17466.1187 9501.0998 8893.6825 5597.7490 1624.9724
H13 22530.0694 14565.0505 13957.6332 10661.6998 6688.9231 5063.9507
H14 22989.3973 15024.3784 14416.9611 11121.0277 7148.2510 5523.2786
H15 17402.0127 9436.9938 8829.5765 5533.6431 1560.8664 387.8042
H16 15001.1702 7036.1513 6428.7340 3132.8006 911.4519 2497.1351
H17 19701.9882 11736.9693 11129.5519 7833.6185 3860.8418 2235.8695
H18 26635.6693 18670.6504 18063.2331 14767.2997 10794.5230 9169.5506
H19 48978.5905 41013.5716 40406.1542 37110.2208 33137.4441 31512.4718
H20 58512.9806 50547.9617 49940.5443 46644.6109 42671.8343 41046.8619
H21 46562.0085 38596.9896 37989.5723 34693.6389 30720.8622 29095.8898
H22 18642.8473 10677.8284 10070.4111 6774.4777 2801.7010 1191.3747
H23 12485.1935 4520.1746 3912.7572 745.3083 3403.6173 5006.4268
H24 469.6939 8076.7738 8697.4838 11978.1535 15949.5646 17574.5370
H13 H14 H15 H16 H17 H18
H2
H3
H4
H5
H6
H7
H8
H9
H10
H11
H12
H13
H14 459.3279
H15 5128.0567 5587.3846
H16 7528.8992 7988.2271 2400.8425
H17 2861.6808 3302.2374 2299.9754 4700.8179
H18 4105.5999 3646.2720 9233.6566 11634.4991 6933.6812
H19 26448.5210 25989.1932 31576.5778 33977.4202 29276.6023 22342.9212
H20 35982.9112 35523.5833 41110.9679 43511.8103 38810.9924 31877.3113
H21 24031.9391 23572.6112 29159.9958 31560.8383 26860.0204 19936.4869
H22 3887.2221 4346.5500 1292.2739 3688.5098 1060.6784 7992.8220
H23 10044.8759 10504.2038 4916.8192 2515.9768 7216.7947 14150.4758
H24 22638.4877 23097.8156 17510.4310 15109.5885 19810.4064 26744.0876
H19 H20 H21 H22 H23
H2
H3
H4
H5
H6
H7
H8
H9
H10
H11
H12
H13
H14
H15
H16
H17
H18
H19
H20 9547.4716
H21 2425.1197 11950.9721
H22 30335.7432 39870.1333 27919.1612
H23 36493.3970 46027.7871 34076.8150 6197.8015
H24 49087.0087 58621.3988 46670.4268 18751.2656 12593.6117
x y
y 0.6147099
z 0.5347802 1.0301534
x y
y 3.694333
z 0.000000 2.132924
x y
y 0.4806243
z 0.2695998 0.3742622
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