README.md

In moviedo5/fda.tsc: Functional Data Sets for Time Series Classification

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

moviedo5/fda.tsc documentation built on May 16, 2019, 12:10 a.m.