starting a daily time series in R

I have a daily time series about number of visitors on the web site. my series start from 01/06/2014 until today 14/10/2015 so I wish to predict number of visitor for in the future. How can I read my series with R? I'm thinking:

series <- ts(visitors, frequency=365, start=c(2014, 6)) 

if yes,and after runing my time series model arimadata=auto.arima() I want to predict visitor's number for the next 6o days, how can i do this?

h=..?
forecast(arimadata,h=..), 

the value of h shoud be what ? thanks in advance for your help

The ts specification is wrong; if you are setting this up as daily observations, then you need to specify what day of the year 2014 is June 1st and specify this in start:

## Create a daily Date object - helps my work on dates
inds <- seq(as.Date("2014-06-01"), as.Date("2015-10-14"), by = "day")

## Create a time series object
set.seed(25)
myts <- ts(rnorm(length(inds)),     # random data
           start = c(2014, as.numeric(format(inds[1], "%j"))),
           frequency = 365)

Note that I specify start as c(2014, as.numeric(format(inds[1], "%j"))). All the complicated bit is doing is working out what day of the year June 1st is:

> as.numeric(format(inds[1], "%j"))
[1] 152

Once you have this, you're effectively there:

## use auto.arima to choose ARIMA terms
fit <- auto.arima(myts)
## forecast for next 60 time points
fore <- forecast(fit, h = 60)
## plot it
plot(fore)

That seems suitable given the random data I supplied...

You'll need to select appropriate arguments for auto.arima() as suits your data.

Note that the x-axis labels refer to 0.5 (half) of a year.

Doing this via zoo

This might be easier to do via a zoo object created using the zoo package:

## create the zoo object as before
set.seed(25)
myzoo <- zoo(rnorm(length(inds)), inds)

Note you now don't need to specify any start or frequency info; just use inds computed earlier from the daily Date object.

Proceed as before

## use auto.arima to choose ARIMA terms
fit <- auto.arima(myts)
## forecast for next 60 time points
fore <- forecast(fit, h = 60)

The plot though will cause an issue as the x-axis is in days since the epoch (1970-01-01), so we need to suppress the auto plotting of this axis and then draw our own. This is easy as we have inds

## plot it
plot(fore, xaxt = "n")    # no x-axis 
Axis(inds, side = 1)

This only produces a couple of labeled ticks; if you want more control, tell R where you want the ticks and labels:

## plot it
plot(fore, xaxt = "n")    # no x-axis 
Axis(inds, side = 1,
     at = seq(inds[1], tail(inds, 1) + 60, by = "3 months"),
     format = "%b %Y")

Here we plot every 3 months.

Time Series Object does not work well with creating daily time series. I will suggest you use the zoo library.

library(zoo)
zoo(visitors, seq(from = as.Date("2014-06-01"), to = as.Date("2015-10-14"), by = 1))

Here's how I created a time series when I was given some daily observations with quite a few observations missing. @gavin-simpson gave quite a big help. Hopefully this saves someone some grief.

The original data looked something like this:

library(lubridate)
set.seed(42)
minday = as.Date("2001-01-01")
maxday = as.Date("2005-12-31")
dates <- seq(minday, maxday, "days")
dates <- dates[sample(1:length(dates),length(dates)/4)] # create some holes
df <- data.frame(date=sort(dates), val=sin(seq(from=0, to=2*pi, length=length(dates))))

To create a time-series with this data I created a 'dummy' dataframe with one row per date and merged that with the existing dataframe:

df <- merge(df, data.frame(date=seq(minday, maxday, "days")), all=T)

This dataframe can be cast into a timeseries. Missing dates are NA.

nts <- ts(df$val, frequency=365, start=c(year(minday), as.numeric(format(minday, "%j"))))
plot(nts)

series <- ts(visitors, frequency=365, start=c(2014, 152)) 

152 number is 01-06-2014 as it start from 152 number because of frequency=365 To forecast for 60 days, h=60.

forecast(arimadata , h=60)