#
#
########## Simple Time Series examples ##########
#
#
# This script assumes you have worked through all the previous notes from 
# the web page and you have downloaded, installed, and updated all available
# R packages. 

# Load the following libraries if you have not already.

library(Rcmdr)

# In Rcmdr:
# Load the 'epack' plugin for Rcmdr by clicking on 'Tools' in Rcmdr, then 'Load Rcmdr plug-in(s)' and then 
# select 'RcmdrPlugin.epack'.
# Rcmdr will need to close and reopen before the Plug-in will be available. Once Rcmdr closes and re-opens, 
# all code/syntax below should work when typed/pasted into the console. 

################

# BP Stock time series; series data are 'by-month'.
# The BP oil spill disaster began with the explosion of the Deepwater Horizon oil rig on April 20th, 2010.
# The following (4) scripts create 'time series' (ts) data vectors for the stock price at "open" and "close" 
# for the dates specified. The data is downloaded from Yahoo -- which cataloges stock prices. 

# In Console:

bp_open <- histprice2(inst1="BP",quot1="Open",start1="1998-01-01",
  end1="2010-12-1")

bp_close <- histprice2(inst1="BP",quot1="Close",start1="1998-01-01",
  end1="2010-12-1")

bp2_open <- histprice2(inst1="BP",quot1="Open",start1="2010-01-1",
  end1="2010-12-1")

bp2_close <- histprice2(inst1="BP",quot1="Close",start1="2010-01-1",
  end1="2010-12-1")


cor(bp_open, bp_close)
cor(bp2_open, bp2_close)

bp_total <- data.frame(bp_open, bp_close)
rm(bp_open, bp_close)
summary(bp_total)

bp_spill <- data.frame(bp2_open, bp2_close)
rm(bp2_open, bp2_close)
summary(bp_spill)

bp_total$ts      # Open Price (ts)
bp_total$ts.1    # Close Price (ts.1)

bp_total

plot(bp_total$ts, bp_total$ts.1)

plot.ts(bp_total$ts)

plot.ts(bp_total$ts.1)

plot.ts(bp_total)

plot.ts(bp_spill)

# Fit an autoregresssive model to time series data (ts).

ar(bp_total$ts)
ar(bp_total$ts.1)

# Fit an ARMA model to a univariate time series by conditional least squares. 
# For exact maximum likelihood estimation see 'arima0'.

arma(bp_total$ts)
arma(bp_total$ts.1)

library(tseries)

# KPSS test for stationarity.

kpss.test(bp_total$ts)
kpss.test(bp_total$ts.1)

kpss.test(bp_spill$ts)
kpss.test(bp_spill$ts.1)

# Using Rcmdr 'epack' plugin. 

ArimaModel.1 <- Arima(bp_total$ts,order=c(0,0,0),
		include.mean=1,
  seasonal=list(order=c(0,0,0),period=12))
ArimaModel.1

ArimaModel.2 <- Arima(bp_total$ts.1,order=c(0,0,0),
		include.mean=1,
  seasonal=list(order=c(0,0,0),period=12))
ArimaModel.2

# Forcasting the next three values of BP stock 'open price' = ArimaModel 1 and BP stock 'close price' = ArimaModel 2.

fore001 <- predar3(ArimaModel.1,fore1=3)
fore001

fore002 <- predar3(ArimaModel.2,fore1=3)
fore002

# Checking BP stock 'open price' and its ability to forecast accurately.

accur001<- fore1('ts',bp_total$ts, bp_total)

# Filtering with Exponential smoothing. 

HoltWintersModel.4 <- HoltWinters(bp_total$ts,
	   gamma=0,beta=0)
HoltWintersModel.4

# Filtering a non-seasonal model. 

HoltWintersModel.7 <- HoltWinters(bp_total$ts,
	   gamma=0)
HoltWintersModel.7

# Filering a seasonal model (additive). 

HoltWintersModel.8 <- HoltWinters(bp_total$ts,
	   seasonal='add')
HoltWintersModel.8

# Spectural density plot.

spectrum(bp_total$ts)

spectrum(bp_total$ts.1)

# Autocorrelation plot. 

acf(bp_total$ts)

acf(bp_total$ts.1)







# END; Sep. 2010