Nothing
R/series.r
In tsapp: Time Series, Analysis and Application
#' Monthly numbers of road traffic accidents with personal injury in BRD
#'
#' @format ACCIDENT is a univariate time series of length 528, start January 1974, frequency = 12
#' \describe{
#' \item{ACCIDENT}{Monthly numbers of road traffic accidents with personal injury}
#' }
#' @source < https://www-genesis.destatis.de/genesis//online?operation=table&code=46241-0002& \cr
#' levelindex=0&levelid=1583749114977>
#' @examples
#' data(ACCIDENT)
#' ## maybe tsp(ACCIDENT) ; plot(ACCIDENT)
"ACCIDENT"
#' Alcohol Demand, UK, 1870-1938.
#'
#' @format ALCINCOME is a threevariate time series of length 69 and 3 variables; start 1870, frequency = 1
#' \describe{
#' \item{Y}{log consumption per head}
#' \item{Z}{log real income per head}
#' \item{X}{log real price}
#' }
#' @examples
#' data(ALCINCOME)
#' ## maybe tsp(ALCINCOME) ; plot(ALCINCOME)
#' @source Durbin & Watson (1951) <https://doi.org/10.1093/biomet/38.1-2.159>
"ALCINCOME"
#' Monthly beer production in Australia: megalitres. Includes ale and stout. Does not include beverages with alcohol percentage less than 1.15.
#'
#' @format BEER is a univariate time series of length 476, start January 1956, end Aug 1995, frequency = 12
#' \describe{
#' \item{BEER}{Monthly production of beer in Australia}
#' }
#' @source R package tsdl <https://github.com/FinYang/tsdl>
#' @examples
#' data(BEER)
#' ## maybe tsp(BEER) ; plot(BEER)
"BEER"
#' Weekly number of births in New York
#'
#' @format BLACKOUT is a univariate time series of length 313, 1961 -- 1966
#' \describe{
#' \item{BLACKOUT}{Weekly numbers of births in New York}
#' }
#' @source Izenman, A. J., and Zabell, S. L. (1981) <https://www.sciencedirect.com/science/article/abs/pii/
#' 0049089X81900181>
#' @examples
#' data(BLACKOUT)
#' ## maybe tsp(BLACKOUT) ; plot(BLACKOUT)
"BLACKOUT"
#' U.S. annual coffee consumption
#'
#' @format COFFEE is a univariate time series of length 61; start 1910, frequency = 1
#' \describe{
#' \item{COFFEE}{annual coffee-consumption USA, logarithmic transformed}
#' }
#' @source R package tsdl <https://github.com/FinYang/tsdl>
#' @examples
#' data(COFFEE)
#' ## maybe tsp(COFFEE) ; plot(COFFEE)
"COFFEE"
#' Market value of DAX
#'
#' @format DAX is a multivariate time series of length 12180 and 4 variables
#' \describe{
#' \item{DAY}{Day of the week}
#' \item{MONTH}{Month}
#' \item{Year}{Year}
#' \item{DAX30}{Market value}
#' }
#' @examples
#' data(DAX)
#' ## maybe tsp(DAX) ; plot(DAX)
"DAX"
#' Incidences of insulin-dependent diabetes mellitus
#'
#' @format DIABETES is a univariate time series of length 72, start January 1979, frequency = 12
#' \describe{
#' \item{DIABETES}{Incidences of insulin-dependent diabetes mellitus}
#' }
#' @source Waldhoer, T., Schober, E. and Tuomilehto, J. (1997) <https://www.sciencedirect.com/science/ \cr article/abs/pii/S0895435696003344>
#' @examples
#' data(DIABETES)
#' ## maybe tsp(DIABETES) ; plot(DIABETES)
"DIABETES"
#' Running yield of public bonds in Austria and Germany
#'
#' @format DOMINANCE is a bivariate time series of length 167:
#' \describe{
#' \item{X}{Interest rate Germany}
#' \item{Y}{Interest rate Austria}
#' }
#' @source Jaenicke, J. and Neck, R. (1996) <https://doi.org/10.17713/ajs.v25i2.555>
#' @examples
#' data(DOMINANCE)
#' ## maybe tsp(DOMINANCE) ; plot(DOMINANCE)
"DOMINANCE"
#' ENGINES is an alias for MACHINES
#'
#' @format ENGINES is a univariate time series of length 188, start January 1972 frequency = 12
#' \describe{
#' \item{ENGINES}{Incoming orders for engines}
#' }
#' @examples
#' data(ENGINES)
#' ## maybe tsp(ENGINES) ; plot(ENGINES)
"ENGINES"
#' Portfolio-Insurance-Strategies
#'
#' @format FINANCE is a multivariate time series of length 7529:
#' \describe{
#' \item{CPPI}{first Portfolio-Insurance-Strategy}
#' \item{TIPP}{second Portfolio-Insurance-Strategy}
#' \item{StopLoss}{third Portfolio-Insurance-Strategy}
#' \item{SyntheticPut}{fourth Portfolio-Insurance-Strategy}
#' \item{CASH}{money market investment}
#' }
#' @source Dichtl, H. and Drobetz, W. (2011) <doi:10.1016/j.jbankfin.2010.11.012>
#' @examples
#' data(FINANCE)
#' ## maybe tsp(FINANCE) ; plot(FINANCE)
"FINANCE"
#' Germany's gross domestic product adjusted for price changes
#'
#' @format GDP is a univariate time series of length 159, start January 1970, frequency = 4
#' \describe{
#' \item{GDP}{Gross domestic product adjusted for price changes}
#' }
#' @source <https://www-genesis.destatis.de/genesis//online?operation=table&code=81000-0002&levelindex \cr =0&levelid=1583750132341>
#' @examples
#' data(GDP)
#' ## maybe tsp(GDP) ; plot(GDP)
"GDP"
#' Germany's gross domestic product, values of Laspeyres index to base 2000
#'
#' @format GDPORIG is a univariate time series of length 159, start January 1970, frequency = 4
#' \describe{
#' \item{GDPORIG}{gross domestic product, values of Laspeyres index to the base 2000}
#' }
#' @source <https://www-genesis.destatis.de/genesis//online?operation=table&code=81000-0002&levelindex \cr =0&levelid=1583750132341>
#' @examples
#' data(GDPORIG)
#' ## maybe tsp(GDPORIG) ; plot(GDPORIG)
"GDPORIG"
#' Cardiac frequency of a patient
#'
#' @format HEARTBEAT is a univariate time series of length 30:
#' \describe{
#' \item{HEARTBEAT}{cardiac frequency of a patient}
#' }
#' @examples
#' data(HEARTBEAT)
#' ## maybe tsp(HEARTBEAT) ; plot(HEARTBEAT)
"HEARTBEAT"
#' HSV's position in the first German soccer league
#'
#' @format HSV is a univariate time series of length 47:
#' \describe{
#' \item{HSV}{HSV's position in the first German soccer league}
#' }
#' @source <https://www.transfermarkt.de/hamburger-sv/platzierungen/verein/41>
#' @examples
#' data(HSV)
#' ## maybe tsp(HSV) ; plot(HSV)
"HSV"
#' IBM's stock price
#'
#' @format IBM is a univariate time series of length 369, start 17 May 1961
#' \describe{
#' \item{IBM}{IBM's daily stock price}
#' }
#' @source Box, G. E. P. and Jenkins, G. M. (1970, ISBN: 978-0816210947) "Time series analysis: forecasting and control"
#' @examples
#' data(IBM)
#' ## maybe tsp(IBM) ; plot(IBM)
"IBM"
#' Temperature and consumption of ice cream
#'
#' @format ICECREAM is a bivariate time series of length 160:
#' \describe{
#' \item{ICE}{consumption of ice cream}
#' \item{TEMP}{Temperature in Fahrenheit degrees}
#' }
#' @source Hand, D. J., et al. (1994, ISBN: 9780412399206) "A Handbook of Small Data Sets"
#' @examples
#' data(ICECREAM)
#' ## maybe tsp(ICECREAM) ; plot(ICECREAM)
"ICECREAM"
#' Income orders of a company
#'
#' @format INORDER is a univariate time series of length 237, start January 1968, frequency =12
#' \describe{
#' \item{INORDER}{Income orders of a company}
#' }
#' @examples
#' data(INORDER)
#' ## maybe tsp(INORDER) ; plot(INORDER)
"INORDER"
#' Daily subsoil water level and precipitation at pilot well Lith
#'
#' @format LITH is a bivariate time series of length 1347:
#' \describe{
#' \item{N}{precipitation amount}
#' \item{G}{water level}
#' }
#' @examples
#' data(LITH)
#' ## maybe tsp(LITH) ; plot(LITH)
"LITH"
#' Level of Luteinzing hormone of a cow
#'
#' @format LUHORMONE is a bivariate time series of length 29:
#' \describe{
#' \item{T}{Time in minutes}
#' \item{X}{Level of the Luteinzing-hormone}
#' }
"LUHORMONE"
#' Annual lynx trappings in a region of North-West Canada. Taken from Andrews and Herzberg (1985).
#'
#' @format LYNX is a univariate time series of length 114; start 1821 frequency = 1
#' \describe{
#' \item{LYNX}{annual lynx trappings in a region of North-west Canada}
#' }
#' @source Andrews, D. F. and Herzberg, A. M. (1985) "Data" <https://www.springer.com/gp/book/9781461295631>
#' @examples
#' data(LYNX)
#' ## maybe tsp(LYNX) ; plot(LYNX)
"LYNX"
#' Size of populations of lynxes and snow hares
#'
#' @format LYNXHARE is a simulated bivariate time series from a VAR[1]-model of length 100:
#' \describe{
#' \item{X}{Number of lynxes}
#' \item{Y}{Number of snow hares}
#' }
#' @examples
#' data(LYNXHARE)
"LYNXHARE"
#' Subsoil water level and precipitation at pilot well L921
#'
#' @format L921 is a trivariate time series of length 335:
#' \describe{
#' \item{T}{Day}
#' \item{Y}{Water level}
#' \item{Z}{Supplemented water level}
#' }
#' @examples
#' data(L921)
#' ## maybe tsp(L921) ; plot(L921)
"L921"
#' Number of incoming orders for machines
#'
#' @format MACHINES is a univariate time series of length 188, start January 1972 frequency = 12
#' \describe{
#' \item{MACHINES}{Incoming orders for machines}
#' }
#' @examples
#' data(MACHINES)
#' ## maybe tsp(MACHINES) ; plot(MACHINES)
"MACHINES"
#' Atmospheric CO2 concentrations (ppmv) derived from in situ air samples collected at Mauna Loa Observatory, Hawaii
#'
#' @format MAUNALOA is a univariate time series of length 735; start March 1958, frequency = 12
#' \describe{
#' \item{MAUNALOA}{CO2-concentration at Mauna Loa}
#' }
#' @source Keeling, C. D. , Piper, S. C., Bacastow, R. B., Wahlen, M. , Whorf, T. P., Heimann, M., and Meijer, H. A. (2001) <https://library.ucsd.edu/dc/object/bb3859642r>
#' @examples
#' data(MAUNALOA)
#' ## maybe tsp(MAUNALOA) ; plot(MAUNALOA)
"MAUNALOA"
#' Stock market price of MDAX
#'
#' @format MDAX is a multivariate time series of length 6181 and 4 variables
#' \describe{
#' \item{DAY}{Day of the week}
#' \item{MONTH}{Month}
#' \item{YEAR}{Year}
#' \item{MDAX}{Opening stock market price}
#' }
#' @source <https://www.onvista.de/index/MDAX-Index-323547>
#' @examples
#' data(MDAX)
#' ## maybe tsp(MDAX) ; plot(MDAX[,3])
"MDAX"
#' Melanoma incidence in Connecticut
#'
#' @format MELANOM is a multivariate time series of length 45 and 3 variables
#' \describe{
#' \item{POP}{Population}
#' \item{RATE}{Incidence}
#' \item{SUN}{Sunspots}
#' }
#' @source Andrews, D. F. and Herzberg, A. M. (1985) "Data" <https://www.springer.com/gp/book/9781461295631>
#' @examples
#' data(MELANOM)
#' ## maybe tsp(MELANOM) ; plot(MELANOM[,-1])
"MELANOM"
#' Annual trade of muskrat pelts
#'
#' @format MUSKRAT is a univariate time series of length 62; start 1848, frequency = 1
#' \describe{
#' \item{MUSKRAT}{annual trade of muskrat pelts}
#' }
#' @source <https://archive.uea.ac.uk/~gj/book/data/mink.dat>
#' @examples
#' data(MUSKRAT)
#' ## maybe tsp(MUSKRAT) ; plot(MUSKRAT)
"MUSKRAT"
#' Daily values of the Japanese stock market index Nikkei 225 between 02.02.2000 and 20.10.2020
#'
#' @format NIKKEI is a univariate time series of length 5057
#' \describe{
#' \item{NIKKEI}{Daily values of Nikkei}
#' }
#' @source Heber, G., Lunde, A., Shephard, N. and Sheppard, K. (2009) "Oxford-Man Institute's realized library, version 0.3",
#' Oxford-Man Institute, University of Oxford, Oxford <https://realized.oxford-man.ox.ac.uk/data>
#' @examples
#' data(NIKKEI)
#' ## maybe plot(NIKKEI)
"NIKKEI"
#' Amount of an Oxygen isotope
#'
#' @format OXYGEN is a matrix with 164 rows and 2 columns
#' \describe{
#' \item{T}{Time}
#' \item{D}{DELTA18O}
#' }
#' @source Belecher, J., Hampton, J. S., and Tunnicliffe Wilson, T. (1994, ISSN: 1369-7412) "Parameterization of Continuous Time Autoregressive Models for Irregularly Sampled Time Series Data"
#' @examples
#' data(OXYGEN)
#' ## maybe plot(OXYGEN[,1],OXYGEN[,2],type="l"); rug(OXYGEN[,1])
"OXYGEN"
#' Two measurements at a paper machine
#'
#' @format PAPER is a bivariate time series of length 160
#' \describe{
#' \item{H}{High}
#' \item{W}{Weight}
#' }
#' @source Janacek, G. J. & Swift, L. (1993, ISBN: 978-0139184598) "Time Series: Forecasting, Simulation, Applications"
#' @examples
#' data(PAPER)
#' ## maybe tsp(PAPER) ; plot(PAPER)
"PAPER"
#' Monthly prices for pigs
#'
#' @format PIGPRICE is a univariate time series of length 240; start January 1894, frequency =12
#' \describe{
#' \item{PIGPRICE}{Monthly prices for pigs}
#' }
#' @source Hanau, A. (1928) "Die Prognose der Schweinepreise"
#' @examples
#' data(PIGPRICE)
#' ## maybe tsp(PIGPRICE) ; plot(PIGPRICE)
"PIGPRICE"
#' Peak power demand in Berlin
#'
#' @format PPDEMAND is a univariate time series of length 37; start 1955, frequency = 1
#' \describe{
#' \item{PPDEMAND}{annual peak power demand in Berlin, Megawatt}
#' }
#' @source Fiedler, H. (1979) "Verschiedene Verfahren zur Prognose des des Stromspitzenbedarfs in Berlin (West)"
#' @examples
#' data(PPDEMAND)
#' ## maybe tsp(PPDEMAND) ; plot(PPDEMAND)
"PPDEMAND"
#' Production index of manufacturing industries
#'
#' @format PRODINDEX is a univariate time series of length 119:
#' \describe{
#' \item{PRODINDEX}{Production index of manufacturing industries}
#' }
#' @source Statistisches Bundesamt (2009) <https://www-genesis.destatis.de/genesis/online>
#' @examples
#' data(PRODINDEX)
#' ## maybe tsp(PRODINDEX) ; plot(PRODINDEX)
"PRODINDEX"
#' Annual amount of rainfall in Los Angeles
#'
#' @format RAINFALL is a univariate time series of length 119; start 1878, frequency = 1
#' \describe{
#' \item{RAINFALL}{Amount of rainfall in Los Angeles}
#' }
#' @source LA Times (January 28. 1997)
#' @examples
#' data(RAINFALL)
#' ## maybe tsp(RAINFALL) ; plot(RAINFALL)
"RAINFALL"
#' Monthly sales of Australian red wine (1000 l)
#'
#' @format REDWINE is a univariate time series of length 187; start January 1980, frequency =12
#' \describe{
#' \item{REDWINE}{Monthly sales of Australian red wine }
#' }
#' @source R package tsdl <https://github.com/FinYang/tsdl>
#' @examples
#' data(REDWINE)
#' ## maybe tsp(REDWINE) ; plot(REDWINE)
"REDWINE"
#' CO2-Concentration obtained in Schauinsland, Germany
#'
#' @format SCHAUINSLAND is a univariate time series of length 72:
#' \describe{
#' \item{SCHAUINSLAND}{CO2-Concentration obtained in Schauinsland}
#' }
#' @source <http://cdiac.ornl.gov/trends/co2/uba/uba-sc.html>
#' @examples
#' data(SCHAUINSLAND)
#' ## maybe tsp(SCHAUINSLAND) ; plot(SCHAUINSLAND)
"SCHAUINSLAND"
#' Annual logging of spruce wood.
#'
#' @format SPRUCE is a univariate time series of length 42:
#' \describe{
#' \item{SPRUCE}{Annual logging of spruce wood}
#' }
#' @examples
#' data(SPRUCE)
#' ## maybe tsp(SPRUCE) ; plot(SPRUCE)
"SPRUCE"
#' Monthly community taxes in Germany (billions EURO)
#'
#' @format TAXES is a univariate time series of length 246; start January 1999, frequency = 12
#' \describe{
#' \item{TAXES}{monthly community taxes in Germany}
#' }
#' @source <https://www-genesis.destatis.de/genesis/online?operation=previous&levelindex=1&step=1&titel= \cr Tabellenaufbau&levelid=1583748637039>
#' @examples
#' data(TAXES)
#' ## maybe tsp(TAXES) ; plot(TAXES)
"TAXES"
#' Mean thickness of annual tree rings
#'
#' @format TREERING is a multivariate time series of length 66 with 3 variables:
#' \describe{
#' \item{THICK}{mean thickness of annual tree rings}
#' \item{TEMP}{mean temperature of the year}
#' \item{RAIN}{amount of rain of the year}
#' }
#' @source <https://ltrr.arizona.edu/>
#' @examples
#' data(TREERING)
#' ## maybe tsp(TREERING) ; plot(TREERING)
"TREERING"
#' Measurements of physiological tremor
#'
#' @format TREMOR is a univariate time series of length 400.
#' \describe{
#' \item{TREMOR}{Tremor}
#' }
#' @examples
#' data(TREMOR)
#' ## maybe tsp(TREMOR) ; plot(TREMOR)
"TREMOR"
#' Monthly sales of a company
#'
#' @format SALES is a univariate time series of length 77:
#' \describe{
#' \item{y}{monthly sales of a company}
#' }
#' @source Newton, H. J. (1988, ISBN: 978-0534091989): "TIMESLAB: A time series analysis laboraty"
#' @examples
#' data(SALES)
#' ## maybe tsp(SALES) ; plot(SALES)
"SALES"
#' Population of USA
#'
#' @format USAPOP is a univariate time series of length 39; start 1630, frequency = 0.1
#' \describe{
#' \item{USAPOP}{Population of USA}
#' }
#' @source <https://www.worldometers.info/world-population/us-population/>
#' @examples
#' data(USAPOP)
#' ## maybe tsp(USAPOP) ; plot(USAPOP)
"USAPOP"
#' Concentration of growth hormone of a bull
#'
#' @format WHORMONE is a univariate time series of length 97:
#' \describe{
#' \item{WHORMONE}{Concentration of growth hormone of a bull}
#' }
#' @source Newton, H. J. (1988, ISBN: 978-0534091989): "TIMESLAB: A time series analysis laboraty"
#' @examples
#' data(WHORMONE)
#' ## maybe tsp(WHORMONE) ; plot(WHORMONE)
"WHORMONE"
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#' Monthly numbers of road traffic accidents with personal injury in BRD
#'
#' @format ACCIDENT is a univariate time series of length 528, start January 1974, frequency = 12
#' \describe{
#' \item{ACCIDENT}{Monthly numbers of road traffic accidents with personal injury}
#' }
#' @source < https://www-genesis.destatis.de/genesis//online?operation=table&code=46241-0002& \cr
#' levelindex=0&levelid=1583749114977>
#' @examples
#' data(ACCIDENT)
#' ## maybe tsp(ACCIDENT) ; plot(ACCIDENT)
"ACCIDENT"
#' Alcohol Demand, UK, 1870-1938.
#'
#' @format ALCINCOME is a threevariate time series of length 69 and 3 variables; start 1870, frequency = 1
#' \describe{
#' \item{Y}{log consumption per head}
#' \item{Z}{log real income per head}
#' \item{X}{log real price}
#' }
#' @examples
#' data(ALCINCOME)
#' ## maybe tsp(ALCINCOME) ; plot(ALCINCOME)
#' @source Durbin & Watson (1951) <https://doi.org/10.1093/biomet/38.1-2.159>
"ALCINCOME"
#' Monthly beer production in Australia: megalitres. Includes ale and stout. Does not include beverages with alcohol percentage less than 1.15.
#'
#' @format BEER is a univariate time series of length 476, start January 1956, end Aug 1995, frequency = 12
#' \describe{
#' \item{BEER}{Monthly production of beer in Australia}
#' }
#' @source R package tsdl <https://github.com/FinYang/tsdl>
#' @examples
#' data(BEER)
#' ## maybe tsp(BEER) ; plot(BEER)
"BEER"
#' Weekly number of births in New York
#'
#' @format BLACKOUT is a univariate time series of length 313, 1961 -- 1966
#' \describe{
#' \item{BLACKOUT}{Weekly numbers of births in New York}
#' }
#' @source Izenman, A. J., and Zabell, S. L. (1981) <https://www.sciencedirect.com/science/article/abs/pii/
#' 0049089X81900181>
#' @examples
#' data(BLACKOUT)
#' ## maybe tsp(BLACKOUT) ; plot(BLACKOUT)
"BLACKOUT"
#' U.S. annual coffee consumption
#'
#' @format COFFEE is a univariate time series of length 61; start 1910, frequency = 1
#' \describe{
#' \item{COFFEE}{annual coffee-consumption USA, logarithmic transformed}
#' }
#' @source R package tsdl <https://github.com/FinYang/tsdl>
#' @examples
#' data(COFFEE)
#' ## maybe tsp(COFFEE) ; plot(COFFEE)
"COFFEE"
#' Market value of DAX
#'
#' @format DAX is a multivariate time series of length 12180 and 4 variables
#' \describe{
#' \item{DAY}{Day of the week}
#' \item{MONTH}{Month}
#' \item{Year}{Year}
#' \item{DAX30}{Market value}
#' }
#' @examples
#' data(DAX)
#' ## maybe tsp(DAX) ; plot(DAX)
"DAX"
#' Incidences of insulin-dependent diabetes mellitus
#'
#' @format DIABETES is a univariate time series of length 72, start January 1979, frequency = 12
#' \describe{
#' \item{DIABETES}{Incidences of insulin-dependent diabetes mellitus}
#' }
#' @source Waldhoer, T., Schober, E. and Tuomilehto, J. (1997) <https://www.sciencedirect.com/science/ \cr article/abs/pii/S0895435696003344>
#' @examples
#' data(DIABETES)
#' ## maybe tsp(DIABETES) ; plot(DIABETES)
"DIABETES"
#' Running yield of public bonds in Austria and Germany
#'
#' @format DOMINANCE is a bivariate time series of length 167:
#' \describe{
#' \item{X}{Interest rate Germany}
#' \item{Y}{Interest rate Austria}
#' }
#' @source Jaenicke, J. and Neck, R. (1996) <https://doi.org/10.17713/ajs.v25i2.555>
#' @examples
#' data(DOMINANCE)
#' ## maybe tsp(DOMINANCE) ; plot(DOMINANCE)
"DOMINANCE"
#' ENGINES is an alias for MACHINES
#'
#' @format ENGINES is a univariate time series of length 188, start January 1972 frequency = 12
#' \describe{
#' \item{ENGINES}{Incoming orders for engines}
#' }
#' @examples
#' data(ENGINES)
#' ## maybe tsp(ENGINES) ; plot(ENGINES)
"ENGINES"
#' Portfolio-Insurance-Strategies
#'
#' @format FINANCE is a multivariate time series of length 7529:
#' \describe{
#' \item{CPPI}{first Portfolio-Insurance-Strategy}
#' \item{TIPP}{second Portfolio-Insurance-Strategy}
#' \item{StopLoss}{third Portfolio-Insurance-Strategy}
#' \item{SyntheticPut}{fourth Portfolio-Insurance-Strategy}
#' \item{CASH}{money market investment}
#' }
#' @source Dichtl, H. and Drobetz, W. (2011) <doi:10.1016/j.jbankfin.2010.11.012>
#' @examples
#' data(FINANCE)
#' ## maybe tsp(FINANCE) ; plot(FINANCE)
"FINANCE"
#' Germany's gross domestic product adjusted for price changes
#'
#' @format GDP is a univariate time series of length 159, start January 1970, frequency = 4
#' \describe{
#' \item{GDP}{Gross domestic product adjusted for price changes}
#' }
#' @source <https://www-genesis.destatis.de/genesis//online?operation=table&code=81000-0002&levelindex \cr =0&levelid=1583750132341>
#' @examples
#' data(GDP)
#' ## maybe tsp(GDP) ; plot(GDP)
"GDP"
#' Germany's gross domestic product, values of Laspeyres index to base 2000
#'
#' @format GDPORIG is a univariate time series of length 159, start January 1970, frequency = 4
#' \describe{
#' \item{GDPORIG}{gross domestic product, values of Laspeyres index to the base 2000}
#' }
#' @source <https://www-genesis.destatis.de/genesis//online?operation=table&code=81000-0002&levelindex \cr =0&levelid=1583750132341>
#' @examples
#' data(GDPORIG)
#' ## maybe tsp(GDPORIG) ; plot(GDPORIG)
"GDPORIG"
#' Cardiac frequency of a patient
#'
#' @format HEARTBEAT is a univariate time series of length 30:
#' \describe{
#' \item{HEARTBEAT}{cardiac frequency of a patient}
#' }
#' @examples
#' data(HEARTBEAT)
#' ## maybe tsp(HEARTBEAT) ; plot(HEARTBEAT)
"HEARTBEAT"
#' HSV's position in the first German soccer league
#'
#' @format HSV is a univariate time series of length 47:
#' \describe{
#' \item{HSV}{HSV's position in the first German soccer league}
#' }
#' @source <https://www.transfermarkt.de/hamburger-sv/platzierungen/verein/41>
#' @examples
#' data(HSV)
#' ## maybe tsp(HSV) ; plot(HSV)
"HSV"
#' IBM's stock price
#'
#' @format IBM is a univariate time series of length 369, start 17 May 1961
#' \describe{
#' \item{IBM}{IBM's daily stock price}
#' }
#' @source Box, G. E. P. and Jenkins, G. M. (1970, ISBN: 978-0816210947) "Time series analysis: forecasting and control"
#' @examples
#' data(IBM)
#' ## maybe tsp(IBM) ; plot(IBM)
"IBM"
#' Temperature and consumption of ice cream
#'
#' @format ICECREAM is a bivariate time series of length 160:
#' \describe{
#' \item{ICE}{consumption of ice cream}
#' \item{TEMP}{Temperature in Fahrenheit degrees}
#' }
#' @source Hand, D. J., et al. (1994, ISBN: 9780412399206) "A Handbook of Small Data Sets"
#' @examples
#' data(ICECREAM)
#' ## maybe tsp(ICECREAM) ; plot(ICECREAM)
"ICECREAM"
#' Income orders of a company
#'
#' @format INORDER is a univariate time series of length 237, start January 1968, frequency =12
#' \describe{
#' \item{INORDER}{Income orders of a company}
#' }
#' @examples
#' data(INORDER)
#' ## maybe tsp(INORDER) ; plot(INORDER)
"INORDER"
#' Daily subsoil water level and precipitation at pilot well Lith
#'
#' @format LITH is a bivariate time series of length 1347:
#' \describe{
#' \item{N}{precipitation amount}
#' \item{G}{water level}
#' }
#' @examples
#' data(LITH)
#' ## maybe tsp(LITH) ; plot(LITH)
"LITH"
#' Level of Luteinzing hormone of a cow
#'
#' @format LUHORMONE is a bivariate time series of length 29:
#' \describe{
#' \item{T}{Time in minutes}
#' \item{X}{Level of the Luteinzing-hormone}
#' }
"LUHORMONE"
#' Annual lynx trappings in a region of North-West Canada. Taken from Andrews and Herzberg (1985).
#'
#' @format LYNX is a univariate time series of length 114; start 1821 frequency = 1
#' \describe{
#' \item{LYNX}{annual lynx trappings in a region of North-west Canada}
#' }
#' @source Andrews, D. F. and Herzberg, A. M. (1985) "Data" <https://www.springer.com/gp/book/9781461295631>
#' @examples
#' data(LYNX)
#' ## maybe tsp(LYNX) ; plot(LYNX)
"LYNX"
#' Size of populations of lynxes and snow hares
#'
#' @format LYNXHARE is a simulated bivariate time series from a VAR[1]-model of length 100:
#' \describe{
#' \item{X}{Number of lynxes}
#' \item{Y}{Number of snow hares}
#' }
#' @examples
#' data(LYNXHARE)
"LYNXHARE"
#' Subsoil water level and precipitation at pilot well L921
#'
#' @format L921 is a trivariate time series of length 335:
#' \describe{
#' \item{T}{Day}
#' \item{Y}{Water level}
#' \item{Z}{Supplemented water level}
#' }
#' @examples
#' data(L921)
#' ## maybe tsp(L921) ; plot(L921)
"L921"
#' Number of incoming orders for machines
#'
#' @format MACHINES is a univariate time series of length 188, start January 1972 frequency = 12
#' \describe{
#' \item{MACHINES}{Incoming orders for machines}
#' }
#' @examples
#' data(MACHINES)
#' ## maybe tsp(MACHINES) ; plot(MACHINES)
"MACHINES"
#' Atmospheric CO2 concentrations (ppmv) derived from in situ air samples collected at Mauna Loa Observatory, Hawaii
#'
#' @format MAUNALOA is a univariate time series of length 735; start March 1958, frequency = 12
#' \describe{
#' \item{MAUNALOA}{CO2-concentration at Mauna Loa}
#' }
#' @source Keeling, C. D. , Piper, S. C., Bacastow, R. B., Wahlen, M. , Whorf, T. P., Heimann, M., and Meijer, H. A. (2001) <https://library.ucsd.edu/dc/object/bb3859642r>
#' @examples
#' data(MAUNALOA)
#' ## maybe tsp(MAUNALOA) ; plot(MAUNALOA)
"MAUNALOA"
#' Stock market price of MDAX
#'
#' @format MDAX is a multivariate time series of length 6181 and 4 variables
#' \describe{
#' \item{DAY}{Day of the week}
#' \item{MONTH}{Month}
#' \item{YEAR}{Year}
#' \item{MDAX}{Opening stock market price}
#' }
#' @source <https://www.onvista.de/index/MDAX-Index-323547>
#' @examples
#' data(MDAX)
#' ## maybe tsp(MDAX) ; plot(MDAX[,3])
"MDAX"
#' Melanoma incidence in Connecticut
#'
#' @format MELANOM is a multivariate time series of length 45 and 3 variables
#' \describe{
#' \item{POP}{Population}
#' \item{RATE}{Incidence}
#' \item{SUN}{Sunspots}
#' }
#' @source Andrews, D. F. and Herzberg, A. M. (1985) "Data" <https://www.springer.com/gp/book/9781461295631>
#' @examples
#' data(MELANOM)
#' ## maybe tsp(MELANOM) ; plot(MELANOM[,-1])
"MELANOM"
#' Annual trade of muskrat pelts
#'
#' @format MUSKRAT is a univariate time series of length 62; start 1848, frequency = 1
#' \describe{
#' \item{MUSKRAT}{annual trade of muskrat pelts}
#' }
#' @source <https://archive.uea.ac.uk/~gj/book/data/mink.dat>
#' @examples
#' data(MUSKRAT)
#' ## maybe tsp(MUSKRAT) ; plot(MUSKRAT)
"MUSKRAT"
#' Daily values of the Japanese stock market index Nikkei 225 between 02.02.2000 and 20.10.2020
#'
#' @format NIKKEI is a univariate time series of length 5057
#' \describe{
#' \item{NIKKEI}{Daily values of Nikkei}
#' }
#' @source Heber, G., Lunde, A., Shephard, N. and Sheppard, K. (2009) "Oxford-Man Institute's realized library, version 0.3",
#' Oxford-Man Institute, University of Oxford, Oxford <https://realized.oxford-man.ox.ac.uk/data>
#' @examples
#' data(NIKKEI)
#' ## maybe plot(NIKKEI)
"NIKKEI"
#' Amount of an Oxygen isotope
#'
#' @format OXYGEN is a matrix with 164 rows and 2 columns
#' \describe{
#' \item{T}{Time}
#' \item{D}{DELTA18O}
#' }
#' @source Belecher, J., Hampton, J. S., and Tunnicliffe Wilson, T. (1994, ISSN: 1369-7412) "Parameterization of Continuous Time Autoregressive Models for Irregularly Sampled Time Series Data"
#' @examples
#' data(OXYGEN)
#' ## maybe plot(OXYGEN[,1],OXYGEN[,2],type="l"); rug(OXYGEN[,1])
"OXYGEN"
#' Two measurements at a paper machine
#'
#' @format PAPER is a bivariate time series of length 160
#' \describe{
#' \item{H}{High}
#' \item{W}{Weight}
#' }
#' @source Janacek, G. J. & Swift, L. (1993, ISBN: 978-0139184598) "Time Series: Forecasting, Simulation, Applications"
#' @examples
#' data(PAPER)
#' ## maybe tsp(PAPER) ; plot(PAPER)
"PAPER"
#' Monthly prices for pigs
#'
#' @format PIGPRICE is a univariate time series of length 240; start January 1894, frequency =12
#' \describe{
#' \item{PIGPRICE}{Monthly prices for pigs}
#' }
#' @source Hanau, A. (1928) "Die Prognose der Schweinepreise"
#' @examples
#' data(PIGPRICE)
#' ## maybe tsp(PIGPRICE) ; plot(PIGPRICE)
"PIGPRICE"
#' Peak power demand in Berlin
#'
#' @format PPDEMAND is a univariate time series of length 37; start 1955, frequency = 1
#' \describe{
#' \item{PPDEMAND}{annual peak power demand in Berlin, Megawatt}
#' }
#' @source Fiedler, H. (1979) "Verschiedene Verfahren zur Prognose des des Stromspitzenbedarfs in Berlin (West)"
#' @examples
#' data(PPDEMAND)
#' ## maybe tsp(PPDEMAND) ; plot(PPDEMAND)
"PPDEMAND"
#' Production index of manufacturing industries
#'
#' @format PRODINDEX is a univariate time series of length 119:
#' \describe{
#' \item{PRODINDEX}{Production index of manufacturing industries}
#' }
#' @source Statistisches Bundesamt (2009) <https://www-genesis.destatis.de/genesis/online>
#' @examples
#' data(PRODINDEX)
#' ## maybe tsp(PRODINDEX) ; plot(PRODINDEX)
"PRODINDEX"
#' Annual amount of rainfall in Los Angeles
#'
#' @format RAINFALL is a univariate time series of length 119; start 1878, frequency = 1
#' \describe{
#' \item{RAINFALL}{Amount of rainfall in Los Angeles}
#' }
#' @source LA Times (January 28. 1997)
#' @examples
#' data(RAINFALL)
#' ## maybe tsp(RAINFALL) ; plot(RAINFALL)
"RAINFALL"
#' Monthly sales of Australian red wine (1000 l)
#'
#' @format REDWINE is a univariate time series of length 187; start January 1980, frequency =12
#' \describe{
#' \item{REDWINE}{Monthly sales of Australian red wine }
#' }
#' @source R package tsdl <https://github.com/FinYang/tsdl>
#' @examples
#' data(REDWINE)
#' ## maybe tsp(REDWINE) ; plot(REDWINE)
"REDWINE"
#' CO2-Concentration obtained in Schauinsland, Germany
#'
#' @format SCHAUINSLAND is a univariate time series of length 72:
#' \describe{
#' \item{SCHAUINSLAND}{CO2-Concentration obtained in Schauinsland}
#' }
#' @source <http://cdiac.ornl.gov/trends/co2/uba/uba-sc.html>
#' @examples
#' data(SCHAUINSLAND)
#' ## maybe tsp(SCHAUINSLAND) ; plot(SCHAUINSLAND)
"SCHAUINSLAND"
#' Annual logging of spruce wood.
#'
#' @format SPRUCE is a univariate time series of length 42:
#' \describe{
#' \item{SPRUCE}{Annual logging of spruce wood}
#' }
#' @examples
#' data(SPRUCE)
#' ## maybe tsp(SPRUCE) ; plot(SPRUCE)
"SPRUCE"
#' Monthly community taxes in Germany (billions EURO)
#'
#' @format TAXES is a univariate time series of length 246; start January 1999, frequency = 12
#' \describe{
#' \item{TAXES}{monthly community taxes in Germany}
#' }
#' @source <https://www-genesis.destatis.de/genesis/online?operation=previous&levelindex=1&step=1&titel= \cr Tabellenaufbau&levelid=1583748637039>
#' @examples
#' data(TAXES)
#' ## maybe tsp(TAXES) ; plot(TAXES)
"TAXES"
#' Mean thickness of annual tree rings
#'
#' @format TREERING is a multivariate time series of length 66 with 3 variables:
#' \describe{
#' \item{THICK}{mean thickness of annual tree rings}
#' \item{TEMP}{mean temperature of the year}
#' \item{RAIN}{amount of rain of the year}
#' }
#' @source <https://ltrr.arizona.edu/>
#' @examples
#' data(TREERING)
#' ## maybe tsp(TREERING) ; plot(TREERING)
"TREERING"
#' Measurements of physiological tremor
#'
#' @format TREMOR is a univariate time series of length 400.
#' \describe{
#' \item{TREMOR}{Tremor}
#' }
#' @examples
#' data(TREMOR)
#' ## maybe tsp(TREMOR) ; plot(TREMOR)
"TREMOR"
#' Monthly sales of a company
#'
#' @format SALES is a univariate time series of length 77:
#' \describe{
#' \item{y}{monthly sales of a company}
#' }
#' @source Newton, H. J. (1988, ISBN: 978-0534091989): "TIMESLAB: A time series analysis laboraty"
#' @examples
#' data(SALES)
#' ## maybe tsp(SALES) ; plot(SALES)
"SALES"
#' Population of USA
#'
#' @format USAPOP is a univariate time series of length 39; start 1630, frequency = 0.1
#' \describe{
#' \item{USAPOP}{Population of USA}
#' }
#' @source <https://www.worldometers.info/world-population/us-population/>
#' @examples
#' data(USAPOP)
#' ## maybe tsp(USAPOP) ; plot(USAPOP)
"USAPOP"
#' Concentration of growth hormone of a bull
#'
#' @format WHORMONE is a univariate time series of length 97:
#' \describe{
#' \item{WHORMONE}{Concentration of growth hormone of a bull}
#' }
#' @source Newton, H. J. (1988, ISBN: 978-0534091989): "TIMESLAB: A time series analysis laboraty"
#' @examples
#' data(WHORMONE)
#' ## maybe tsp(WHORMONE) ; plot(WHORMONE)
"WHORMONE"
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