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
Creating a time seriesThe ts() function will convert a numeric vector into an R time series object. The format is ts(vector, start=, end=, frequency=) where start and end are the times of the first and last observation and frequency is the number of observations per unit time (1=annual, 4=quartly, 12=monthly, etc.).
Time series in R is defined as a series of values, each associated with the timestamp also measured over regular intervals (monthly, daily) like weather forecasting and sales analysis. The R stores the time series data in the time-series object and is created using the ts() function as a base distribution.
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.
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))
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