###
###
###  Time Series Data List
###
###      - Copy and paste below code in R to load the data
###
###############################################

library(forecast)
source('https://nmimoto.github.io/R/TS-00.txt')




#
#  Financial / Economic
#
#==============================================


#[SPY]-----------------------------------------------------------------------
#  Daily Price of S&P500 ETF (SPY) from Jan 02 2000 to Dec 31 2014

  D <- read.csv("https://nmimoto.github.io/datasets/spy.csv", header=T)
  head(D)
  X <- ts(D[,8], start=1)
  plot(X)


#[dowj]-----------------------------------------------------------------------
#   The Dow Jones Utilities Index, Aug. 28–Dec. 18, 1972; 
#   DOWJ.TSM from Brockwell and Davis (2002)

    D  <- read.csv("https://nmimoto.github.io/datasets/dowj.csv")
    D1 <- ts(D, start=c(1,1), freq=1)

    plot(D1, type='o')


#[djao2]-----------------------------------------------------------------------
#  Closing values of the DowJones Index of stocks on the New York Stock Exchange 
#  and the closing values of the Australian All Ordinaries Index of Share Prices, 
#  recorded at the termination of trading.  251 successive trading days  Sep 10, 1993 to Aug 26, 1994
#
#
#  (Because of the time difference between Sydney and New York, 
#  the markets do not close simultaneously in both places; however, 
#  in Sydney the closing price of the Dow Jones index for the previous
#  day is known before the opening of the market on any trading day.)
#
#                                    djao2.tsm From Brockwell and Davis (2002) 

    D  <- read.table("https://nmimoto.github.io/datasets/djao2.csv", header=T)
    A <- ts(D$DJ, start=c(1,1), freq=1)
    B <- ts(D$AO, start=c(1,1), freq=1)

    layout(matrix(1:2, 2, 1))
    plot(A, type='o')
    plot(B, type='o')
    layout(1,1,1)


#[SP100]-----------------------------------------------------------------------
#  SP100.TSM from Brockwell and Davis (2002)

    D  <- read.csv("https://nmimoto.github.io/datasets/sp100.csv")
    D1 <- ts(D, start=c(1,1), freq=1)
    plot(D1, type='o')


#[Trade 4x46]-----------------------------------------------------------------------
#  US Trade in Goods and Services Balance of Payments (BOP) Basis. 
#  Value in millions of 2000 dollars 1960-2005
#  From Time Series Data Library

    D <- read.csv("https://nmimoto.github.io/datasets/trade.csv")
    D1 <- ts(D[,2], start=1960, freq=1)
    D2 <- ts(D[,3], start=1960, freq=1)
    plot(D1, type='o')


#[interest**]-----------------------------------------------------------------------
#  Monthly interest rates Government Bond Yield 2-year securities,
#  Reserve Bank of Australia. Jan 1969 – Sep 1994

    D <- read.csv("https://nmimoto.github.io/datasets/interest.csv", header=T)
    D1 <- ts(D[,5], start=1960, freq=1)
    plot(D1, type='o')


#[acci]-----------------------------------------------------------------------
#  Accidental death in US
#  deaths.TSM from Brockwell and Davis (2002)

    D  <- read.csv("https://nmimoto.github.io/datasets/acci.csv")
    D1 <- ts(D, start=c(1,1), freq=12)
    plot(D1, type='o')


#[copper]---------------------------------------------------------------------
#  Annual Copper prices, 1800-1997

    D  <- read.csv("https://nmimoto.github.io/datasets/copper.csv")
    D1 <- ts(D[,2], start=c(1800,1), freq=1)
    plot(D1, type='o')


#[concentration]-----------------------------------------------------------------------
#  Chemical concentration readings

    D  <- read.csv("https://nmimoto.github.io/datasets/concentration.csv")
    D1 <- ts(D[,2], start=c(1,1), freq=1)
    plot(D1, type='o')


#[unemployment]-----------------------------------------------------------------------
#  Annual unemployment, U.S., 1890 to 1970

    D  <- read.csv("https://nmimoto.github.io/datasets/unemployment.csv")
    D1 <- ts(D[,2], start=c(1890,1), freq=1)
    plot(D1, type='o')


#[water]-----------------------------------------------------------------------
#  Baltmore city annual water use, liters per capita per day, 1885-1968

    D  <- read.csv("https://nmimoto.github.io/datasets/water.csv")
    D1 <- ts(D[,2], start=c(1885,1), freq=1)
    plot(D1, type='o')







#
#  Social 
#
#==============================================


#[polio]-----------------------------------------------------------------------
#  Newly recorded cases of poliomyelitis in the U.S. for the years 1970–1983
#  Example 8.8.3 in Brockwell and Davis (2002) 

    D  <- read.csv("https://nmimoto.github.io/datasets/polio.csv")
    D1 <- ts(D, start=c(1,1), freq=1)
    plot(D1, type='o')







#
#  Industrial
#
#==============================================


#[iron]-----------------------------------------------------------------------
#  Monthly basic iron production in Australia: thousand tonnes. Jan 1956 – Aug 1995
    
    D <- read.csv("https://nmimoto.github.io/datasets/iron.csv", header=T)
    D1 <- ts(D[,2], start=c(1956,1), freq=12)
    plot(D1, type='o')


#[Steel]-----------------------------------------------------------------------
#  Monthly production of raw steel in Australia: thousand tonnes. Jan 1956 to Nov 1993
#  From Time Series Data Library

    D <- read.csv("https://nmimoto.github.io/datasets/steel.csv")
    D1 <- ts(D[,2], start=c(1956,1), freq=12)
    plot(D1, type='o')


#[repair]-----------------------------------------------------------------------
#  Monthly demand repair parts large/heavy equip. Iowa 1972 – 1979

    D <- read.csv("https://nmimoto.github.io/datasets/repair.csv", header=T)
    D1 <- ts(D[,2], start=c(1972,1), freq=12)
    plot(D1, type='o')


#[Gas]-----------------------------------------------------------------------
#  Monthly production of Gas in Australia: 
#  million megajoules. Includes natural gas from July 1989. Jan 1956 - Aug 1995
#  From Time Series Data Library

    D <- read.csv("https://nmimoto.github.io/datasets/Gas.csv")
    D1 <- ts(D[,2], start=c(1956, 1), freq=12)
    plot(D1, type='o')


#[methane]-----------------------------------------------------------------------
#  Methane input into gas furnace: cu. ft/min.
#  Sampling interval 9 seconds. Carbon dioxide output from gas furnace: 
#  percent of output gas. Sampling interval 9 seconds

    D <- read.csv("https://nmimoto.github.io/datasets/methane.csv", header=T)
    CO2 <- ts(D[,2], start=c(1, 1), freq=1)
    Met <- ts(D[,3], start=c(1, 1), freq=1)
    plot(Met, type='o')


#[gas2]-----------------------------------------------------------------------
#  Montly av. residential gas usage Iowa (cubic feet)*100 ’71 – ’79

    D <- read.csv("https://nmimoto.github.io/datasets/gas2.csv", header=T)
    D1 <- ts(D[,2], start=c(1, 1), freq=12)
    plot(D1, type='o')


#[sheep]-----------------------------------------------------------------------
#  Annual sheep population (1000s) in England & Wales 1867 – 1939

    D <- read.csv("https://nmimoto.github.io/datasets/sheep.csv", header=T)
    head(D)
    sheep <- ts(D[,2], start=c(1867))
    plot(sheep)


#[lynx]-----------------------------------------------------------------------
#  Annual Canadian Lynx Trappings   1821 - 1934
#  lynx.TSM from Brockwell and Davis (2002)

    D  <- read.csv("https://nmimoto.github.io/datasets/lynx.csv")
    D1 <- ts(D, start=c(1821), freq=1)
    plot(D1, type='o')


#[barley]-----------------------------------------------------------------------
#  Annual barley yields per acre in England & Wales 1884 – 1939

    D <- read.csv("https://nmimoto.github.io/datasets/barley.csv", header=T)
    head(D)
    barley <- ts(D[,2], start=c(1884))
    plot(barley)


#[milk]-----------------------------------------------------------------------
#  Monthly milk production: pounds per cow. Jan 62 – Dec 75

    D <- read.csv("https://nmimoto.github.io/datasets/milk.csv", header=T)
    D1 <- ts(D[,2], start=c(1962,1), freq=12)
    plot(D1, type='o')


#[Pigs]-----------------------------------------------------------------------
#  Monthly total number of pigs slaughtered in Victoria. Jan 1980 – August 1995
#  From Time Series Data Library

    D  <- read.csv("https://nmimoto.github.io/datasets/pigs.csv")
    D1 <- ts(D[,2], start=c(1980, 8), freq=12)
    plot(D1, type='o')






### Below needs review



#
#  Sales
#
#==============================================


#[LS2]-----------------------------------------------------------------------
#  Daily Sales data with leading indicator given by Box and Jenkins (1976), p. 537.
#  LS2.tsm from Brockwell and Davis (2002)

    D  <- read.table("https://nmimoto.github.io/datasets/ls2.csv", header=T)
    A <- ts(D$A, start=c(1,1), freq=1)
    B <- ts(D$B, start=c(1,1), freq=1)

    layout(matrix(1:2, 2, 1))
    plot(A, type='o')
    plot(B, type='o')
    

#[wine n=142]------------------------------------------------------------------
#  Monthly Wine sales in Australia wine.TSM from Brockwell and Davis (2002)

    D  <- read.csv("https://nmimoto.github.io/datasets/wine.csv")
    D1 <- ts(D, start=c(1980,1), freq=12)
    plot(D1, type='o')


#[engines n=190]---------------------------------------------------------------
#  Monthly Motor vehicles engines and parts/CPI, Canada, 1976-1991

    D <- read.csv("https://nmimoto.github.io/datasets/engines.csv", header=T)
    D1 <- ts(D[,2], start=c(1976,1), freq=12)
    plot(D1, type='o')


#[scott]-----------------------------------------------------------------------
#  Daily scott tissue sales 
  
    D <- read.csv("https://nmimoto.github.io/datasets/scott.csv", header=T)
    D1 <- ts(D, start=c(1,1), freq=7)
    plot(D1, type='o')


#[blenders n=60]---------------------------------------------------------------------
#  Lenex corporation: Monthl shipment of blenders Jan’67-Dec’71
  
    D <- read.csv("https://nmimoto.github.io/datasets/blenders.csv", header=T)
    head(D); dim(D)
    D1 <- ts(D[,2], start=c(1967,1), freq=12)
    plot(D1)


#[canop n=60]------------------------------------------------------------------------
#  Lenex corporation: shipment of can openers Jan’67-Dec’71

    D <- read.csv("https://nmimoto.github.io/datasets/canop.csv", header=T)
    head(D); dim(D)
    D1 <- ts(D[,2], start=c(1967,1), freq=12)
    plot(D1)


#[shampoo n=36]----------------------------------------------------------------------
#  Monthly Sales of shampoo over a three year period

    D  <- read.csv("https://nmimoto.github.io/datasets/shampoo.csv", header=T)
    D1 <- ts(D[,2], start=c(1,1), freq=12)
    plot(D1, type='o')


#[companyx n=77]---------------------------------------------------------------------
#  Sales of company X, Jan. 1965 to May 1971

    D <- read.csv("https://nmimoto.github.io/datasets/companyx.csv", header=T)
    D1 <- ts(D[,2], start=c(1965,1), freq=12)
    plot(D1, type='o')


#[radios n=144]----------------------------------------------------------------------
#  Lenex corporation: shipment of radios Jan’67-Dec’78 (12F5.0)

    D <- read.csv("https://nmimoto.github.io/datasets/radios.csv", header=T)
    head(D); dim(D)
    D1 <- ts(D[,2], start=c(1967,1), freq=12)
    plot(D1)


#[houses n=107]----------------------------------------------------------------------
#  Sales of new one-family houses, USA, from Jan 1987 through Nov 1995

    D <- read.csv("https://nmimoto.github.io/datasets/houses.csv", header=T)
    D1 <- ts(D[,2], start=c(1987,1), freq=12)
    plot(D1, type='o')


#[car n=108]-------------------------------------------------------------------------
#  Monthly car sales in Quebec 1960-1968

    D <- read.csv("https://nmimoto.github.io/datasets/car.csv", header=T)
    D1 <- ts(D[,2], start=c(1960,1), freq=12)
    plot(D1, type='o')


#[winnebago n=66]--------------------------------------------------------------------
#  Monthly unit sales, Winnebago Industries, Nov. 1966 – Feb. 1972

    D <- read.csv("https://nmimoto.github.io/datasets/winnebago.csv", header=T)
    D1 <- ts(D[,2], start=c(1966,1), freq=12)
    plot(D1, type='o')


#[advertising]-----------------------------------------------------------------------
#  Advertising and sales data: 36 consecutive monthly sales and advertising 
#  expenditures of a dietary weight control product

    D <- read.csv("https://nmimoto.github.io/datasets/advertising.csv", header=T)
    head(D); dim(D)
    ad   <- ts(D[,2], start=c(1), freq=1)
    sale <- ts(D[,3], start=c(1), freq=1)
    ts.plot(ad,sale, col=c("red","black"))


#[sales2]-----------------------------------------------------------------------
#  Annual domestic sales and advertising of Lydia E. Pinkham Medicine Company:
#  both in thousands of dollars. 1907-1960. (Pankratz	 1991)

    D  <- read.csv("https://nmimoto.github.io/datasets/sales2.csv")
    ad     <- ts(D[,2], start=c(1907,1), freq=1)
    sales  <- ts(D[,3], start=c(1907,1), freq=1)
    ts.plot(ad,sales, type='o', col=c("blue", "orange"))











#
#  Meteorological
#
#==============================================



#[lake]-----------------------------------------------------------------------
#  Level of Lake Huron 1875-1972.  
#  From Brockwell and Davis (2002) 
#
    D  <- read.csv("https://nmimoto.github.io/datasets/lake.csv")
    D1 <- ts(D, start=1875, freq=1)
    plot(D1, type='o')


#[FraizierMonthly]-----------------------------------------------------------------------
#  Monthly Discharge data of Fraizier river

    D  <- read.csv("https://nmimoto.github.io/datasets/frazier.csv")
    D1 <- ts(D, start=c(1,1), freq=1)
    plot(D1, type='o')


#[Boise]-----------------------------------------------------------------------
#  Boise River near Twin Springs, Idaho, Oct. 1912 ? Sep. 1960
#  From Time Series Data Library

    D <- read.csv("https://nmimoto.github.io/datasets/boise.csv")
    D1 <- ts(D, start=c(1,1), freq=1)
    plot(D1, type='o')


#[Snow 63]-----------------------------------------------------------------------
#  Annual snowfall in Buffalo, 1910-1972.
#  From Time Series Data Library

    D  <- read.csv("https://nmimoto.github.io/datasets/snow.csv")
    D1 <- ts(D[,2], start=c(1910,1), freq=1)
    plot(D1, type='o')


#[michigan]-----------------------------------------------------------------------
#  Annual precipitation (inches), Lake Michigan, 1900 to 1986

    D <- read.csv("https://nmimoto.github.io/datasets/michigan.csv", header=T)
    D1 <- ts(D, start=c(1,1), freq=1)
    plot(D1, type='o')


#[gtemp 130]-----------------------------------------------------------------------
#  Global temparature data from Shumway
#  package(astsa)

    D  <- read.csv("https://nmimoto.github.io/datasets/gtemp.csv")
    D1 <- ts(D, start=c(1880), freq=1)
    plot(D1, type='o')


#[Temp 3650]-----------------------------------------------------------------------
#  Daily maximum temperatures in Melbourne, Australia, 1981-1990
#  From Time Series Data Library

    D <- read.csv("https://nmimoto.github.io/datasets/temp.csv")
    D1 <- ts(D[,2], start=c(1,1), freq=7)
    plot(D1, type='o')


#[Temp2 1461]-----------------------------------------------------------------------
#  Mean daily temperature, Fisher River near Dallas, Jan 01, 1988 to Dec 31, 1991
#  From Time Series Data Library

    D <- read.csv("https://nmimoto.github.io/datasets/temp2.csv")
    D1 <- ts(D[,2], start=c(1,1), freq=1)
    plot(D1, type='o')


#[Rhine 150]-----------------------------------------------------------------------
#  Rhine River near basle, Switzerland, 1807 to 1957
#  From Time Series Data Library

    D  <- read.csv("https://nmimoto.github.io/datasets/rhine.csv")
    D1 <- ts(D[,2], start=c(1807,1), freq=1)
    plot(D1, type='o')


#[Erie 601]-----------------------------------------------------------------------
#  Monthly Lake Erie Levels 1921 – 1970,

    D <- read.csv("https://nmimoto.github.io/datasets/erie.csv", header=T)
    D1 <- ts(D[,2], start=c(1921,1), freq=12)
    plot(D1, type='o')


#[clear 600]-----------------------------------------------------------------------
#  Monthly riverflow in cms, Clear water River at kamiah, Idaho, 1911 - 1965

    D <- read.csv("https://nmimoto.github.io/datasets/clear.csv", header=T)
    D1 <- ts(D[,2], start=c(1911,1), freq=12)
    plot(D1, type='o')


#[mad]-----------------------------------------------------------------------
#  Monthly riverflow in cms, Mad River at springfield, OH., 1915 - 1960

    D <- read.csv("https://nmimoto.github.io/datasets/mad.csv", header=T)
    D1 <- ts(D, start=c(1,1), freq=1)
    plot(D1, type='o')


#[feather]-----------------------------------------------------------------------
#  Monthly riverflow in cms, Feather River at oroville, California, Oct.1902 – Sep. 1977

    D <- read.csv("https://nmimoto.github.io/datasets/feather.csv", header=T)
    D1 <- ts(D, start=c(1,1), freq=1)
    plot(D1, type='o')


#[neches]-----------------------------------------------------------------------
#  Monthly riverflow in cms, Neches River at rockland, Texas, 1914 - 1960

    D <- read.csv("https://nmimoto.github.io/datasets/neches.csv", header=T)
    D1 <- ts(D, start=c(1,1), freq=1)
    plot(D1, type='o')


#[magne]-----------------------------------------------------------------------
#  Monthly riverflow in cms, N. Magnetawan River at burks falls, 1916 – Sep. 1977

    D <- read.csv("https://nmimoto.github.io/datasets/magne.csv", header=T)
    D1 <- ts(D, start=c(1,1), freq=1)
    plot(D1, type='o')


#[oldman]-----------------------------------------------------------------------
#  Mean daily flow, Oldman Rivernear Brocket, Jan 01, 1988 to Dec 31, 1991

    D <- read.csv("https://nmimoto.github.io/datasets/oldman.csv", header=T)
    D1 <- ts(D, start=c(1,1), freq=1)
    plot(D1, type='o')


#[jokulsa]-----------------------------------------------------------------------
#  Mean daily flow in cms, Jokulsa Eystri River, 1 Jan 1972 – 31 Dec 1974

    D <- read.csv("https://nmimoto.github.io/datasets/jokulsa.csv", header=T)
    D1 <- ts(D, start=c(1,1), freq=1)
    plot(D1, type='o')


#[buffalo]-----------------------------------------------------------------------
#  Annual snowfall in Buffalo, 1910-1972.

    D <- read.csv("https://nmimoto.github.io/datasets/buffalo.csv", header=T)
    head(D); dim(D)
    buff <- ts(D[,2], start=c(1910), freq=1)
    plot(buff)


#[chicago]-----------------------------------------------------------------------
#  Annual snowfall in Chicago, 1939 to 1978

    D <- read.csv("https://nmimoto.github.io/datasets/chicago.csv", header=T)
    head(D); dim(D)
    chi <- ts(D[,2], start=c(1939), freq=1)
    plot(chi)


#[ozone]-----------------------------------------------------------------------
#  Ozone, Arosa, 1932-1972

    D <- read.csv("https://nmimoto.github.io/datasets/ozone.csv", header=T)
    D1 <- ts(D[,2], start=c(1,1), freq=1)
    plot(D1, type='o')


#[winter]-----------------------------------------------------------------------
#  Winter negative temperature sum (in deg. C), 1781 – 1988

    D <- read.csv("https://nmimoto.github.io/datasets/winter.csv", header=T)
    D1 <- ts(D, start=c(1,1), freq=1)
    plot(D1, type='o')


#[volcanic]-----------------------------------------------------------------------
#  Volcanic dust veil index, northern hemisphere, 1500-1969

    D <- read.csv("https://nmimoto.github.io/datasets/volcanic.csv", header=T)
    D1 <- ts(D, start=c(1,1), freq=1)
    plot(D1, type='o')


#[sunspot]-----------------------------------------------------------------------
#  Annual sunspot relative number 1936-1972

    D  <- read.csv("https://nmimoto.github.io/datasets/sunspot.csv")
    D1 <- ts(D[,2], start=c(1936,1), freq=1)
    plot(D1, type='o')


#[sunspot2]-----------------------------------------------------------------------
#  Monthly sunspot number, Zurich, 1749-1983

    D  <- read.csv("https://nmimoto.github.io/datasets/sunspot2.csv")
    D1 <- ts(D[,2], start=c(1749,1), freq=12)
    plot(D1)


#[earthquakes]-----------------------------------------------------------------------
#  Number of earthquakes per year magnitude 7.0 or greater. 1900-1998
#  National Earthquake Information Center. 
#  Different lists will give different numbers depending on the formula 
#  used for calculating the magnitude

    D <- read.csv("https://nmimoto.github.io/datasets/earthquakes.csv", header=T)
    D1 <- ts(D[,2], start=c(1900,1), freq=1)
    plot(D1, type='o')