| Title: | Functional Data Sets |
|---|---|
| Description: | Functional data sets. |
| Authors: | Han Lin Shang and Rob J Hyndman |
| Maintainer: | Han Lin Shang <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.8 |
| Built: | 2024-11-21 06:40:37 UTC |
| Source: | CRAN |
This package contains a list of functional time series, sliced functional time series, and functional data sets. Functional time series is a special type of functional data observed over time. Sliced functional time series is a special type of functional time series with a time variable observed over time.
Han Lin Shang and Rob J Hyndman
Maintainer: Han Lin Shang <[email protected]>
R. J. Hyndman and H. L. Shang. (2010) "Rainbow plots, bagplots, and boxplots for functional data", Journal of Computational and Graphical Statistics, 19(1), 29-45.
R. J. Hyndman and H. L. Shang (2009) "Forecasting functional time series (with discussion)", Journal of the Korean Statistical Society, 38(3), 199-221.
H. L. Shang and R. J. Hyndman (2009) "Nonparametric time series forecasting with dynamic updating", Mathematics and Computers in Simulation, 81(7), 1310-1324.
Age-specific mortality rates for Australia and Australian states.
An object of class fts.
The following data sets are included:
ausmale: Australia male log mortality rates (1901-2003).
ausfemale: Australia female log mortality rates (1901-2003).
austotal: Australia total log mortality rates (1901-2003).
nswmale: New South Wales male log mortality rates (1901-2003).
nswfemale: New South Wales female log mortality rates (1901-2003).
nswtotal: New South Wales total log mortality rates (1901-2003).
vicmale: Victoria male log mortality rates (1901-2003).
vicfemale: Victoria female log mortality rates (1901-2003).
victotal: Victoria total log mortality rates (1901-2003).
qldmale: Queensland male log mortality rates (1901-2003).
qldfemale: Queensland female log mortality rates (1901-2003).
qldtotal: Queensland total log mortality rates (1901-2003).
samale: South Australia male log mortality rates (1901-2003).
safemale: South Australia female log mortality rates (1901-2003).
satotal: South Australia total log mortality rates (1901-2003).
wamale: Western Australia male log mortality rates (1901-2003).
wafemale: Western Australia female log mortality rates (1901-2003).
watotal: Western Australia total log mortality rates (1901-2003).
ntmale: Northern Territory male mortality rates (1901-2003).
ntfemale: Northern Territory female mortality rates (1901-2003).
ntotal: Northern Territory total mortality rates (1901-2003).
actmale: Australian Capital Territory male mortality rates (1901-2003).
actfemale: Australian Capital Territory female mortality rates (1901-2003).
actotal: Australian Capital Territory total mortality rates (1901-2003).
tasmale: Tasmania male mortality rates (1901-2003).
tasfemale: Tasmania female mortality rates (1901-2003).
tastotal: Tasmania total mortality rates (1901-2003).
Mortality rates are in logarithm form for Australia, New South Wales, Victoria, Queensland, South Australia, and Western Australia.
Mortality rates without log transformation are: Northern Territory, Australian Captial Territory and Tasmania. These three states have either missing data or zero mortality rates.
All data are from v3.2b of the Australian Demographic Data Bank released 10 February 2005.
Rob J Hyndman
The Australian Demographic Data Bank (courtesy of Len Smith).
H. Booth and R. J. Hyndman and L. Tickle and P. De Jong (2006) "Lee-Carter mortality forecasting: A multi-country comparison of variants and extensions", Demographic Research, 15, 289-310.
R. J. Hyndman and M. S. Ullah (2007) "Robust forecasting of mortality and fertility rates: A functional data approach", Computational Statistics and Data Analysis, 51(10), 4942-4956.
R. J. Hyndman and H. Booth (2008) "Stochastic population forecasts using functional data models for mortality, fertility and migration", International Journal of Forecasting, 24(3), 323-342.
R. J. Hyndman and H. Shang (2009) "Functional time series forecasting" (with discussion), Journal of the Korean Statistical Society, 38(3), 199-221.
J-M. Chiou and H-G. Muller (2009) "Modeling hazard rates as functional data for the analysis of cohort lifetables and mortality forecasting", Journal of the American Statistical Association, 104(486), 572-585.
plot(victotal)plot(victotal)
The experiment involved varying the composition of biscuit dough pieces. Two sets of dough pieces were measured, a calibration set and a prediction set. They were created and measured as two distinct sets, on separate occasions, and do not result from a random (or any other) split of a larger set.
data(labp) data(labc) data(nirp) data(nirc)data(labp) data(labc) data(nirp) data(nirc)
nirp and nirc are objects of class fds.
labp and labc are objects of class matrix.
The data labc (c stands for calibration) and labp (p stands for prediction) contain the reference data on the composition of the doughs.
The data nirc and nirp contain 700 point near infrared reflectance (NIR) spectra for the same dough. The spectral range is 1100-2498 nm in steps of 2nm.
The data labc$y is 4 rows by 40 columns, the rows being fat, sucrose, flour and water all in percents. The percents do not quite add
up to 100, since there are other minor ingredients present, but they add up to nearly 100 percent.
According to Brown et al. (2001), the observation 23 in the calibration set appears as an outlier.
Sample number 21 in the labp shows up as a validation set.
We thank Professor Marina Vannucci for the permission to re-distribute this data set.
P. J. Brown and T. Fearn and M. Vannucci (2001) "Bayesian wavelet regression on curves with applications to a spectroscopic calibration problem", Journal of the American Statistical Association, 96(454), pp. 398-408.
plot(nirp) plot(nirc)plot(nirp) plot(nirc)
Age-specific breast cancer rates for Australian females with 9 age groups (45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79, 80-84, 85+) from 1921 to 2001.
data(Cancerrate)data(Cancerrate)
An object of class fts.
Australian Institute of Health and Welfare (AIHW) website at https://www.aihw.gov.au/reports-statistics/health-conditions-disability-deaths/cancer/data.
B. Erbas and R. J. Hyndman and D. Gertig (2007) "Forecasting age-specific breast cancer mortality using functional data models", Statistics in Medicine, 26(2), 458-470.
R. J. Hyndman and M. S. Ullah (2007) "Robust forecasting of mortality and fertility rates: A functional data approach", Computational Statistics and Data Analysis, 51(10), 4942-4956.
plot(Cancerrate)plot(Cancerrate)
Provided by European Central Bank, this data set contains daily yield curve spot rate from 29/12/2006 to 24/07/2009 for government bond, nominal, all triple AAA issued companies,with maturity term at 3, 6 months and 1 to 30 years.
data(ECBYieldcurve)data(ECBYieldcurve)
An object of class fts.
We thank Mr Sergio S. Guirreri for the permission to re-distribute this data set.
European Central Bank: http://www.ecb.europa.eu/stats/money/yc/html/index.en.html.
plot(ECBYieldcurve)plot(ECBYieldcurve)
This set of time series focus on the US monthly electricity consumed by the residential and commercial sectors from January 1973 up to February 2001 (336 months). This data set is a part of the original one which can be found at http://www.economagic.com.
data(Electricityconsumption)data(Electricityconsumption)
An object of class sfts.
We eliminated the heteroscedasticity and the linear trend by differencing the log transformed data.
We thank Professor Frederic Ferraty for the permission to re-distribute this data set.
NonParametric Functional Data Analysis website at http://www.math.univ-toulouse.fr/staph/npfda/index.html.
F. Ferraty and A. Rabhi and P. Vieu (2005) "Conditional quantiles for dependent functional data with application to the climate El Nino phenomenon", Sankhya: The Indian Journal of Statistics, 67, 378-398.
F. Ferraty and P. Vieu (2007) Nonparametric functional data analysis, New York: Springer.
plot(Electricityconsumption)plot(Electricityconsumption)
These data sets consist of half-hourly electricity demands from Sunday to Saturday in Adelaide between 6/7/1997 and 31/3/2007.
data(mondaydemand) data(tuesdaydemand) data(wednesdaydemand) data(thursdaydemand) data(fridaydemand) data(saturdaydemand) data(sundaydemand) data(SAelectdemand)data(mondaydemand) data(tuesdaydemand) data(wednesdaydemand) data(thursdaydemand) data(fridaydemand) data(saturdaydemand) data(sundaydemand) data(SAelectdemand)
An object of class sfts.
In Adelaide, the electricity demands in summer are very volatile and highly dependent on their associated temperatures. Analyses were performed to test whether or not, under different temperature scenarios, there will be enough capacity to satisfy the electricity demands.
L. Magnano and J. Boland and R. J. Hyndman (2008) "Generation of symthetic sequences of half-hourly temperature", Environmetrics, 19(8), 818-835.
plot(mondaydemand) plot(tuesdaydemand) plot(wednesdaydemand) plot(thursdaydemand) plot(fridaydemand) plot(saturdaydemand) plot(sundaydemand) plot(SAelectdemand)plot(mondaydemand) plot(tuesdaydemand) plot(wednesdaydemand) plot(thursdaydemand) plot(fridaydemand) plot(saturdaydemand) plot(sundaydemand) plot(SAelectdemand)
This data set is a part of the original one which can be found at http://lib.stat.cmu.edu/datasets/tecator.
data(Fatspectrum) data(Fatvalues)data(Fatspectrum) data(Fatvalues)
Fatspectrum is an object of class fds.
Fatvalues is a numeric object.
For each unit, we observe one spectrometric curve which corresponds to the absorbance measured at 100 wavelengths (from 852 to 1050 in step of 2nm). For each measurement, we have at hand its fat content obtained by an analytic chemical processing.
We thank Professor Frederic Ferraty for the permission to re-distribute this data set.
NonParametric Functional Data Analysis website at http://www.lsp.ups-tlse.fr/staph/npfda/.
C. Goutis (1998) "Second-derivative functional regression with applications to near infra-red spectroscopy", Journal of the Royal Statistical Society: Series B, 60(1), 103-114.
F. Ferraty and P. Vieu (2002) "The functional nonparametric model and application to spectrometric data", Computational Statistics, 17(4), 545-564.
F. Ferraty and P. Vieu (2003) "Curve discrimination: A nonparametric functional approach", Computational Statistics and Data Analysis, 44(1-2), 161-173.
F. Ferraty and P. Vieu (2003) "Functional nonparametric statistics: A double infinite dimensional framework", Recent advances and trends in nonparametric statistics, Ed M. G. Akritas and D. N. Politis, Amsterdam, The Netherlands, 61-76.
F. Rossi and N. Delannay and B. Conan-Guez and M. Verleysen (2005) "Representation of functional data in neural networks", Neurocomputing, 64, 183-210.
F. Ferraty and P. Vieu (2007) Nonparametric functional data analysis, New York: Springer.
H. Matsui and Y. Araki and S. Konishi (2008) "Multivariate regression modeling for functional data", Journal of Data Science, 6, 313-331.
plot(Fatspectrum)plot(Fatspectrum)
This data set contains monthly interest rate of the Federal Reserve from January 1982 to June 2009.
data(FedYieldcurve)data(FedYieldcurve)
An object of class fts.
We thank Mr Sergio S. Guirreri for the permission to re-distribute this data set.
Board of Governors of the Federal Reserve System at http://www.federalreserve.gov/.
Data set is also available in Excel format at http://www.guirreri.host22.com/web_documents/fedrates.xls.
plot(FedYieldcurve)plot(FedYieldcurve)
This function returns a list of relevant demographic data currently available in the HMD, related to a specified country.
hmdcountry(Country, sex, username, password)hmdcountry(Country, sex, username, password)
Country |
A specified country. |
sex |
Possible options are "Male", "Female", "Total". |
username |
Authenticate username. |
password |
Authenticate password. |
In order to read the data sets, users are required to create their account via the HMD website (http://www.mortality.org/), and obtain a valid username and password.
List of objects of class fts.
Han Lin Shang and Rob J Hyndman
This function returns a list of all the countries currently available in the HMD, related to a specified data type.
hmdstatistic(sex, type = c("birth count", "death count", "population", "exposure", "mortality rate", "life expectancy"), username, password)hmdstatistic(sex, type = c("birth count", "death count", "population", "exposure", "mortality rate", "life expectancy"), username, password)
sex |
Possible options are "Male", "Female", "Total". |
type |
Type of data. |
username |
Authenticate username. |
password |
Authenticate password. |
In order to read the data sets, users are required to create their account via the HMD website (http://www.mortality.org/), and obtain a valid username and password.
List of objects of class fts.
Han Lin Shang and Rob J Hyndman
This data set consists of near-infrared reflectance spectra of 100 wheat samples, measured in 2 nm intervals from 1100 to 2500nm, and an associated response variables, the samples' moisture content.
data(Moisturespectrum) data(Moisturevalues)data(Moisturespectrum) data(Moisturevalues)
Moisturespectrum is an object of class fds.
Moisturevalues is a numeric object.
We thank Professor John Kalivas for the permission to re-distribute this data set.
J. H. Kalivas (1997) "Two data sets of near infrared spectra", Chemometrics and Intelligent Laboratory Systems, 37(2), 255-259.
P. Reiss and T. Odgen (2007) "Functional principal component regression and functional partial least squares", Journal of the American Statistical Association, 102(479), 984-996.
P. Reiss and T. Odgen (2008) "Smoothing parameter selection for a class of semiparametric linear models", Journal of Royal Statistical Society: Series B, 71(2), 505-523.
plot(Moisturespectrum)plot(Moisturespectrum)
This data set comprises spectra from 60 gasoline samples, measured in 2 nm intervals from 900 to 1700 nm. The response variable is the octane numbers of the samples.
data(Octanespectrum) data(Octanevalues)data(Octanespectrum) data(Octanevalues)
Octanespectrum is an object of class fds.
Octanevalues is a numeric object.
We thank Professor John Kalivas for the permission to re-distribute this data set.
J. H. Kalivas (1997) "Two data sets of near infrared spectra", Chemometrics and Intelligent Laboratory Systems, 37(2), 255-259.
P. Reiss and T. Odgen (2008) "Smoothing parameter selection for a class of semiparametric linear models", Journal of Royal Statistical Society: Series B, 71(2), 505-523.
plot(Octanespectrum)plot(Octanespectrum)
This data set was formed by selecting five phonemes for classification based on digitized speech.
There are pairs , where corresponds to the discretized log-periodograms whereas the gives the class membership (five phonemes: aa, ao, dcl, iy, sh).
data(aa) data(ao) data(dcl) data(iy) data(sh)data(aa) data(ao) data(dcl) data(iy) data(sh)
An object of class fds.
The phonemes are transcribed as follows: "sh" as in "she", "dcl" as in "dark", "iy" as the vowel in "she", "aa" as the vowel in "dark", and "ao" as the first vowel in "water".
We thank Professor Frederic Ferraty for the permission to re-distribute this data set.
This data set is a part of the original one from the elements of statistical learning website at http://www-stat.stanford.edu/ElemStatLearn.
This data set can also be found at the NonParametric Functional Data Analysis website (http://www.lsp.ups-tlse.fr/staph/npfda/).
F. Ferraty and P. Vieu (2003) "Curve discrimination: a nonparametric functional approach", Computational Statistics and Data Analysis, 44(1-2), 161-173.
F. Ferraty and P. Vieu (2006) Nonparametric functional data analysis, New York: Springer.
T. Hastie and R. Tibshirani and J. Friedman (2009) The elements of statistical learning: Data mining, inference and prediction, 2nd edn, New York: Springer.
plot(aa) plot(ao) plot(dcl) plot(iy) plot(sh)plot(aa) plot(ao) plot(dcl) plot(iy) plot(sh)
The pig weight data set has 9 repeated weight measures on 48 pigs.
data(Pigweight)data(Pigweight)
An object of class fds.
Pigweight$x: Number of weeks since measurements commenced.
Pigweight$y: Bodyweight(kg) of pig after weeks.
We thank Professor Matt Wand for the permission to re-distribute this data set.
P. J. Diggle and P. Heagerty and K. Liang and S. Zeger (2002) Analysis of Longitudinal Data, 2nd edn, Oxford: Oxford University Press.
D. Ruppert and M. Wand and R. Carroll. (2003) Semiparametric Regression, New York: Cambridge University Press.
plot(Pigweight)plot(Pigweight)
This function allows users to read any data set from the Human Mortality Database (HMD).
read.hmd(country, sex, file = "Mx_1x1.txt", username, password, yname)read.hmd(country, sex, file = "Mx_1x1.txt", username, password, yname)
country |
Directory abbreviation from the HMD. For instance, Australia = "AUS". |
sex |
Possible options are "Male", "Female", "Total". |
file |
Directory abbreviation from the HMD. For instance, mortality rate = "Mx_1x1.txt". |
username |
Authenticate username. |
password |
Authenticate password. |
yname |
Type of data. |
In order to read the data sets, users are required to create their account via the HMD website (http://www.mortality.org/), and obtain a valid username and password.
An object of class fts.
Han Lin Shang and Rob J Hyndman
The data were registered by the satellite topex/poseidon around an area of 25 kilometers upon the Amazon River. Each row of the data matrix is represented by its wave (i.e. curve) on the range (0, 70), and the satellite is registering 10 curves each second.
data(Satellite)data(Satellite)
An object of class fds.
Note that each wave is linked with the kind of ground treated by the satellite, and the idea for the Amazonian basin is to use these waveforms for altimetric and hydrological purposes.
We thank Professor Frederic Ferraty for the permission to re-distribute this data set.
F. Frappart (2003). Catalogue des formes d'onde de l'altimetre topex/poseidon sur le bassin amazonien. Technical Report, CNES, Toulouse, France.
This data set can also be found at the NonParametric Functional Data Analysis website (http://www.lsp.ups-tlse.fr/staph/npfda/).
F. Ferraty and P. Vieu (2006) Nonparametric functional data analysis, New York: Springer.
S. Dabo-Niang and F. Ferraty and P. Vieu (2007) "On the using of modal curves for radar waveforms classification", Computational Statistics and Data Analysis, 51(10), 4878-4890.
plot(Satellite)plot(Satellite)
These data sets consist of half-hourly temperatures measured at Kent Town and Adelaide airport from Sunday to Saturday in Adelaide between 6/7/1997 and 31/3/2007.
data(mondaytempkent) data(mondaytempairport) data(tuesdaytempkent) data(tuesdaytempairport) data(wednesdaytempkent) data(wednesdaytempairport) data(thursdaytempkent) data(thursdaytempairport) data(fridaytempkent) data(fridaytempairport) data(saturdaytempkent) data(saturdaytempairport) data(sundaytempkent) data(sundaytempairport) data(tempkent) data(tempairport)data(mondaytempkent) data(mondaytempairport) data(tuesdaytempkent) data(tuesdaytempairport) data(wednesdaytempkent) data(wednesdaytempairport) data(thursdaytempkent) data(thursdaytempairport) data(fridaytempkent) data(fridaytempairport) data(saturdaytempkent) data(saturdaytempairport) data(sundaytempkent) data(sundaytempairport) data(tempkent) data(tempairport)
An object of class sfts.
In Adelaide, the electricity demands in summer are very volatile and highly dependent on their associated temperatures. Analyses were performed to test whether, under different temperature scenarios, there will be enough capacity to satisfy the demands.
L. Magnano and J. Boland and R. Hyndman (2008) "Generation of symthetic sequences of half-hourly temperature", Environmetrics, 19(8), 818-835.
plot(mondaytempkent) plot(tuesdaytempkent) plot(wednesdaytempkent) plot(thursdaytempkent) plot(fridaytempkent) plot(saturdaytempkent) plot(sundaytempkent) plot(tempkent)plot(mondaytempkent) plot(tuesdaytempkent) plot(wednesdaytempkent) plot(thursdaytempkent) plot(fridaytempkent) plot(saturdaytempkent) plot(sundaytempkent) plot(tempkent)
Annual measures on Southern Oscillation Index (SOI): observed annual cycles in period 1900-2004.
data(SOI)data(SOI)
An object of class fts.
The data are available at the Australian Meteorological Office (http://www.environment.gov.au).
plot(SOI)plot(SOI)
This data set consists of migration number (in thousands) in Spain from 1999 to 2003. This data set contains the migration rates of 9 age groups, namely 0-9, 10-15, 16-19, 20-29, 30-39, 40-49, 50-59, 60-65, and 65+ for both females and males.
data(femalemigration) data(malemigration)data(femalemigration) data(malemigration)
An object of class fts.
This data set was calculated at the time in accordance with the European studies of population (EAPS https://www.eaps.nl) methodology of 2002, which is hetergeneous with the results calculated using the methodology EAPS in 2005.
Instituto Nacional de Estadistica website at http://www.ine.es/jaxi/menu.do?type=pcaxis&path=/t20/p311&file=inebase.
D. Reher and M. Requena (2009) "The national immigration survey of Spain. A new data source for migration studies in Europe", Demographic Research, 20, 253-278.
plot(femalemigration) plot(malemigration)plot(femalemigration) plot(malemigration)
This data set contains monthly US Treasury bonds from January 1970 through December 2002. Based on the bid-ask midpoint average, the data consist of end of the month price quotes.
data(Yieldcurve)data(Yieldcurve)
An object of class fts.
This data set is filtered to eliminate bonds with special option futures, such as callable and flower bonds. Illiquid securities, such as treasury bills with less than one month on maturity and treasury notes and bonds with less than one year to maturity, are excluded from the samples.
CRSP US Treasury Database (http://www.crsp.com/products/research-products/crsp-us-treasury-database).
plot(Yieldcurve)plot(Yieldcurve)