I would like a guidance no how to plot daily data and use forecasting in R.
There are low purchase for Sunday and Saturday in this data. And there are certain weekdays that have no purchase at all. So its the obstacles for the analysis.
I have around 300 rows with various item name which the items are duplicated inside the column, but with different dates.
Example, I bought exactly 1 soap 3 times a week, at monday, wednesday and also sunday.
This is the example data table :
My trouble so far is that it took me a long time to forecast manually using other statistical software, so I try to learn R from the start and see how it could save the time. The table above have been put into R, the
First please consider adding a reproducible example for a more substantial answer. Look at the most upvoted question with tag R for a how-to.
EDIT: I think this is what you want before creating the ts:
data.agg <- aggregate(data$purchase, by = list(data$date, data$item), FUN = sum)
If your data is not yet of class 'ts' you can create a time-series object with the
ts() command. From the
ts(data = NA, start = 1, end = numeric(), frequency = 1, deltat = 1, ts.eps = getOption("ts.eps"), class = , names = ) as.ts(x, ...)
Generally you could use the HoltWinters function for exponential smoothing like so:
data.hw <- HotlWinters(data) data.predict <- predict(data.hw, n.ahead = x) # for x = units of time ahead you would like to predict
?HoltWinters for more info on the function
Reproducible Example for aggregate:
data <- data.frame(date = c(1, 2, 1, 2, 1, 1), item = c('b','b','a','a', 'a', 'a'), purchase = c(5,15, 23, 7, 12, 11)) data.agg <- aggregate(data$purchase, by = list(data$date, data$item), FUN = sum)
Reproducible Example for HoltWinters:
library(AER) data("UKNonDurables") nd <- window((log(UKNonDurables)), end = c(1970, 4)) tsp(nd) hw <- HoltWinters(nd) pred <- predict(hw, n.ahead = 35) pred plot(hw, pred, ylim = range(log(UKNonDurables))) lines(log(UKNonDurables))