**Continuing from Part 3…**

# Generating a time series data of daily mean of Atm Prs

**library(tseries)**

**library(forecast)**

**library(TTR)**

**Atm.Prs.TS <- ts(new.weather.df$mean, frequency=1)**

**plot.ts(Atm.Prs.TS)**

# This plot looks better than earlier one

#save this data in a csv format in current working directory for later use

**write.table(Atm.Prs.TS, file=”atmprsts.csv”, sep=”,”)**

#storing values as a daily time series

** Atm.Prs.TS1 <- ts(new.weather.df$mean, frequency=365)**

#decomposing the above time series

**Atm.Prs.TS1decomp<- decompose(Atm.Prs.TS1)**

#looking at the seasonal values

**Atm.Prs.TS1decomp$seasonal**

#ploting decomposed series

**plot(Atm.Prs.TS1decomp)**

#The decomposition plot above shows the original time series in the top plot,

# the estimated trend component in the second plot from top,

# the estimated seasonal component in the third plot from top,

# and the estimated irregular component in the bottom plot.

**atmprsseasonaladj<- Atm.Prs.TS1 – Atm.Prs.TS1decomp$seasonal**

** plot(atmprsseasonaladj)**

#forecasting using HoltWinters’ Method

**atmprsforecast1<- HoltWinters(Atm.Prs.TS1, beta=FALSE, gamma=FALSE)**

#we can view the results:

**atmprsforecast1**

#The forecasts made by the function HoltWinters() are stored in a list variable called “fitted”.

# we can view these values:

**atmprsforecast1$fitted**

#Similarly, we can develop forecasting using HoltWinters() function for seasonally adjusted series as well…

**atmprsforecast2 <- HoltWinters(atmprsseasonaladj, beta=FALSE, gamma=FALSE)**

** atmprsforecast2**

** atmprsforecast2$fitted**

# We also can plot the original time series vs. the forecast series:

**plot(atmprsforecast1)**

** #and seasonally adjusted series**

** plot(atmprsforecast2)**

# the sum of squared errors can be viewed by typing:

atmprsforecast1$SSE

atmprsforecast2$SSE

#by default HoltWinters() function gives a forecast for the time period of the series, for forecasting in future,

#we can use forecast.HoltWinters of “forecast” package

**library(forecast)**

#forecasting for 100 periods in future… you can use any number you want

**atmprsforecast3<- forecast.HoltWinters(atmprsforecast1, h=100)**

#viewing the forecated values:

**atmprsforecast3**

#we can plot the foracastes by typing plot.forecast() function:

**plot.forecast(atmprsforecast3)**

# The forecast.HoltWinters() function forecasts with 80% and 95% prediction interval, and is shown in blue and gray shade

# for each data point in the series, the errors in forecast are calculated as the observed values minus predicted values

Garima Jain

said:Do i know the location of data which we used here M4_not_cleande

LikeLike