Package overview

fda.tsc Functional Data Sets of UEA & UCR Time Series Classification Repository [Time Series Classification Repository](http://www.timeseriesclassification.com) adapted for using in package fda.usc. The fda.usc package implements methods for exploratory and descriptive analysis of functional data such as depth measurements, atypical curves detection, regression models, supervised classification, unsupervised classification and functional analysis of variance.

Installation

You can install the latest patched version from Github with:

# install.packages("devtools")
library(devtools)
devtools::install_github("moviedo5/fda.tsc")

This package contains a datasets availabe in Time Series Classification Web Page converted in fdata class objects (of packages. The vignette gives a quick introduction to the usage of the fda.tsc package.

Dependencies

The fda.tsc package depends on the fda.usc R-packages. For installation instructions, see below.

A hands on introduction to package can be found in the reference vignette.

Details on specific functions are in the reference manual, Manuel Oviedo PhD thesis Advances in functional regression and classification models

List of TSC datasets:

library("fda.tsc")
# data(package = "fda.tsc") # Shows the package dataset
d <- data(package = "fda.tsc")
## names of data sets in the package
#d$results[, "Item"]
nm <- d$results[, "Item"]
## assign it to use later 
data(list = nm, package = "fda.tsc")
# lfdata: list with the datasets
list.fdata <- mget(nm) 
#names(list.fdata)

aa<-d$results[, "Package"]
#length(aa);class(aa)
ivar <-  c("Package","Item")
tabla<-data.frame(d$results[,ivar])
names(tabla)<-ivar
tabla$ncurves <-as.numeric(unlist(lapply(seq_along(list.fdata), function(i) nrow(list.fdata[[i]]$x))))
tabla$n_argvals <- as.numeric(unlist(lapply(seq_along(list.fdata), function(i) ncol(list.fdata[[i]]$x))))
#names(tabla)
tab1 <-unlist(lapply(seq_along(list.fdata), function(i)   nlevels(list.fdata[[i]]$df[,"class"])))
tab2 <-matrix(unlist(lapply(seq_along(list.fdata), function(i)   table(list.fdata[[i]]$df[,"sample"]))),ncol=2,byrow=T)
tabla<-cbind(tabla,tab1,tab2[,2:1])
names(tabla)[5:7]<-c("nlevels","n_train","n_test")
#head(tabla)
#mapply(function(x, i) paste(i, x), x, names(x))
## call the promised data
#data(list = nm, package = "fda.tsc")
## get the dimensions of each data set
# lapply(mget(nm), dim)
 #lapply(mget(nm)[[1]], class)
#install.packages("vcdExtra")
#vcdExtra::datasets("fda.tsc")

A ver peta

Package	Item	ncurves	n_argvals	nlevels	n_train	n_test
fda.tsc	Adiac	781	176	37	390	391
fda.tsc	ArrowHead	211	251	3	36	175
fda.tsc	Beef	60	470	5	30	30
fda.tsc	BeetleFly	40	512	2	20	20
fda.tsc	BirdChicken	40	512	2	20	20
fda.tsc	CBF	930	128	3	30	900
fda.tsc	Car	120	577	4	60	60
fda.tsc	ChlorineConcentration	4307	166	3	467	3840
fda.tsc	CinCECGtorso	1420	1639	4	40	1380
fda.tsc	Coffee	56	286	2	28	28
fda.tsc	Computers	500	720	2	250	250
fda.tsc	DiatomSizeReduction	322	345	4	16	306
fda.tsc	ECG200	200	96	2	100	100
fda.tsc	ECG5000	5000	140	5	500	4500
fda.tsc	ElectricDevices	16637	96	7	8926	7711
fda.tsc	FaceAll	2250	131	14	560	1690
fda.tsc	FaceFour	112	350	4	24	88
fda.tsc	FacesUCR	2250	131	14	200	2050
fda.tsc	FiftyWords	905	270	50	450	455
fda.tsc	Fish	350	463	7	175	175
fda.tsc	FordA	4921	500	2	3601	1320
fda.tsc	FordB	4446	500	2	3636	810
fda.tsc	GunPoint	200	150	2	50	150
fda.tsc	Ham	214	431	2	109	105
fda.tsc	HandOutlines	1370	2709	2	1000	370
fda.tsc	Haptics	463	1092	5	155	308
fda.tsc	Herring	128	512	2	64	64
fda.tsc	InlineSkate	650	1882	7	100	550
fda.tsc	InsectWingbeatSound	2200	256	11	220	1980
fda.tsc	ItalyPowerDemand	1096	24	2	67	1029
fda.tsc	LargeKitchenAppliances	750	720	3	375	375
fda.tsc	Lightning2	121	637	2	60	61
fda.tsc	Lightning7	143	319	7	70	73
fda.tsc	Mallat	2400	1024	8	55	2345
fda.tsc	Meat	120	448	3	60	60
fda.tsc	MedicalImages	1141	99	10	381	760
fda.tsc	MiddlePhalanxOutlineAgeGroup	554	80	3	400	154
fda.tsc	MiddlePhalanxOutlineCorrect	891	80	2	600	291
fda.tsc	MiddlePhalanxTW	553	80	6	399	154
fda.tsc	MoteStrain	1272	84	2	20	1252
fda.tsc	NonInvasiveFetalECGThorax1	3765	750	42	1800	1965
fda.tsc	NonInvasiveFetalECGThorax2	3765	750	42	1800	1965
fda.tsc	OSULeaf	442	427	6	200	242
fda.tsc	PhalangesOutlinesCorrect	2658	80	2	1800	858
fda.tsc	Phoneme	2110	1024	39	214	1896
fda.tsc	Plane	210	144	7	105	105
fda.tsc	ProximalPhalanxOutlineAgeGroup	605	80	3	400	205
fda.tsc	ProximalPhalanxOutlineCorrect	891	80	2	600	291
fda.tsc	ProximalPhalanxTW	605	80	6	400	205
fda.tsc	RefrigerationDevices	750	720	3	375	375
fda.tsc	ScreenType	750	720	3	375	375
fda.tsc	ShapeletSim	200	500	2	20	180
fda.tsc	ShapesAll	1200	512	60	600	600
fda.tsc	SmallKitchenAppliances	750	720	3	375	375
fda.tsc	SonyAIBORobotSurface1	621	70	2	20	601
fda.tsc	SonyAIBORobotSurface2	980	65	2	27	953
fda.tsc	Strawberry	983	235	2	613	370
fda.tsc	SwedishLeaf	1125	128	15	500	625
fda.tsc	Symbols	1020	398	6	25	995
fda.tsc	SyntheticControl	600	60	6	300	300
fda.tsc	ToeSegmentation1	268	277	2	40	228
fda.tsc	ToeSegmentation2	166	343	2	36	130
fda.tsc	Trace	200	275	4	100	100
fda.tsc	TwoLeadECG	1162	82	2	23	1139
fda.tsc	TwoPatterns	5000	128	4	1000	4000
fda.tsc	UWaveGestureLibraryAll	4478	945	8	896	3582
fda.tsc	UWaveGestureLibraryX	4478	315	8	896	3582
fda.tsc	UWaveGestureLibraryY	4478	315	8	896	3582
fda.tsc	UWaveGestureLibraryZ	4478	315	8	896	3582
fda.tsc	Wafer	7164	152	2	1000	6164
fda.tsc	Wine	111	234	2	57	54
fda.tsc	WordSynonyms	905	270	25	267	638
fda.tsc	Worms	258	900	5	181	77

Table: Time Series Classification Datasets adapted for fda.usc package