BFAST in R

# use R software

library(zoo)
library(xts)
library(forecast)
library(seas)
library(bfast)
library(devtools)
install_github('loicdtx/bfastSpatial')
library(bfastSpatial)  # added 5/2/2019
library(lubridate)
library(ggplot2)

# load data
data(tura)

# get the image dates
dates <- getZ(tura)

selected_pixel <- 90

# number of valid values in TS
nbr.NA <- sum(!is.na( !as.vector(tura[selected_pixel])))

# create a regular time series object by combining data and date information
(s <- bfastts(as.vector(tura[selected_pixel]), dates, type = c("irregular")))

## Time Series:
## Start = c(1984, 157) 
## End = c(2013, 101) 
## Frequency = 365 
# First day of the Time Series
(first.day <- head(date_decimal(as.numeric(time(s))),1))
## [1] "1984-06-05 10:15:27 UTC"

# Last day of the Time Series
(last.day <- tail(date_decimal(as.numeric(time(s))),1))
## [1] "2013-04-10 23:59:59 UTC"
s.d.linear <- round(na.approx(s),0) 

# added 3/3/2019
# convert the time series to a data.frame 
time.series.to.dataframe <- function(time_series, source) {
  
  s.df           <- data.frame(as.numeric(time_series))
  colnames(s.df) <- "NDVI"
  s.df$Time      <- as.Date(date_decimal(as.numeric(time(time_series))))
  s.df$Type      <- source
  return(s.df)
  
}
############### Daily Timeseries################
# create a data.frame
s.d.linear.df <- time.series.to.dataframe(s.d.linear, "Linear Interpolation")

######################Decomposition##################
s.d.periodic <- round(na.interp(s),0)

################aggregation###############
aggregate.daily.to.weekly <- function(daily.ts) {
    
  dates      <- as.Date(date_decimal(as.numeric(time(daily.ts))))
    
  xts.daily  <- xts(daily.ts, order.by = dates)
  
  xts.weekly <- round(xts::apply.weekly(xts.daily, median),0)  # xts
  
  start(xts.weekly)
  ts.weekly <- ts(data = xts.weekly, 
               # define the start and end (Year, Week)    
               start = c(as.numeric(format(start(xts.weekly), "%Y")),
                         as.numeric(format(start(xts.weekly), "%W"))), 
               end   = c(as.numeric(format(end(xts.weekly), "%Y")), 
                         as.numeric(format(end(xts.weekly), "%W"))), 
               frequency = 52)
   
  return(ts.weekly)
}

s.w.linear   <- aggregate.daily.to.weekly(s.d.linear)

start(s.w.linear)
## [1] 1984   41

end(s.w.linear)
## [1] 2013   14
s.w.periodic   <- aggregate.daily.to.weekly(s.d.periodic)


#################withBFAST###########################
fit <- bfast(s.f.periodic.window, h = 10/length(s.f.periodic.window), 
             season = "harmonic", breaks = 2, max.iter = 2)

plot(fit)