Forecast Hourly Partitioning Data and Plotting - r

Question

I am trying to forecast hourly sales based on past years of data, display the plot of the forecast with x axis SaleDateTime, and check the accuracy against a test set of dates. I keep running in to errors.
I tried using dput to generate a small sample of data but for some reason it still tries to output many more dates then I have in the subset sample data.
My data looks like this: SaleDateTime = "2015-01-02 23:00:00.000" and SaleCount = "1".
It looks like my main issue is with how I'm trying to partition the data into training and test sets.
Also I would like to x axis on the plot to have the form "2015-03-01 23:00:00". I'm pretty new to forecasting so all help is very much appreciated.
Code:
library("forecast")
library("zoo")
SampleData <- read.csv("SampleDataAll.csv")
Value<-SampleData[,c("SaleDateTime","SaleCount")]
rDateTime<-as.POSIXct(SampleData$SaleDateTime, format="%Y-%m-%d %H:%M:%S")
eventdata <- zoo(Value, order.by = rDateTime)
timeseries <- ts(eventdata$SaleCount, frequency=24)
##Partitioning data Training/Testing
ts1Train <- window(timeseries,start="2011-08-01 00:00:00", end="2014-08-01 00:00:00")
Error:
Error in window.default(x, ...) : 'start' cannot be after 'end'
In addition: Warning message:
In window.default(x, ...) : 'end' value not changed
ts1Test <- window(timeseries,start="2014-08-01 01:00:00", end="2015-08-01 00:00:00")
Error in window.default(x, ...) : 'start' cannot be after 'end'
In addition: Warning message:
In window.default(x, ...) : 'end' value not changed
fcast2<-forecast(ts1Train,h=8764)
Error:
Error in forecast(ts1Train, h = 8764) : object 'ts1Train' not found
plot(fcast2)
accuracy(fcast2,ts1Test)
Error:
Error in frequency(x) : object 'ts1Test' not found
UPDATE:
I made the changes below to how I partition the training and testing data as per the suggestion. Now I'm getting the error message below when I try to run accuracy on the ts1Test data.
New Code:
library("forecast")
library("zoo")
SampleData<-SampleData
Value<-SampleData[,c("SaleDateTime","SaleCount")]
rDateTime<-as.POSIXct(SampleData$SaleDateTime, format="%Y-%m-%d %H:%M:%S")
eventdata <- zoo(Value, order.by = rDateTime)
##Partitioning data Training/Testing
ts1SampleTrain<-eventdata[1:2000,]
ts1Train<-ts(ts1SampleTrain$SaleCount, frequency=24)
ts1SampleTest<-eventdata[2001:28567,]
ts1Test<-ts(ts1SampleTest$SaleCount, frequency=24)
#Training Model
fcast2<-forecast(ts1Train,h=8567)
plot(fcast2)
accuracy(fcast2,ts1Test)
New Error:
Error in -.default(xx, ff[1:n]) :
non-numeric argument to binary operator

Answer 1

You can try to split the function before converting the data to a time series function. For example:
train <- sampleData[1:100,] # choose the first 100 row as training set
test <- sampleData[101:200,] # choose the following 100 row as testing set
There are several issues in your code:
The start parameter in window function accept integer (as year) or vector (year and month). ?window will give more info.
The error from the first window function will not give expected input for the following code, especailly the forecast part.
forecast as the name tells, is a forecasting function. You need to build a time series model (for example, using arima function) on your training data.
I would suggest you read some time series tutorials, and here is the one:
https://a-little-book-of-r-for-time-series.readthedocs.org/en/latest/