Thanks all I have edited the questions based on your replies
I have a univariate time series with high frequency (the data is generated minutely) with 10 days of data so far.
The data is coming from sensors located on different parking sites that count vacancy availability, my objective is to do a forecast for each parking site for the next 24 hours.
> str(stations)
'data.frame': 76234 obs. of 3 variables:
$ siteid : Factor w/ 5 levels "15031","15032",..: 3 4 1 5 2 3 4 1 5 2 ...
$ Count : int 13 16 30 2 9 13 16 30 2 9 ...
$ LastUpdate: POSIXct, format: "2015-08-27 06:31:00" "2015-08-27 06:31:00" "2015-08-27 06:31:00" "2015-08-27 06:31:00" ...
> head(stations)
siteid Count LastUpdate
1 15033 13 2015-08-27 06:31:00
2 15034 16 2015-08-27 06:31:00
3 15031 30 2015-08-27 06:31:00
4 15035 2 2015-08-27 06:31:00
5 15032 9 2015-08-27 06:31:00
6 15033 13 2015-08-27 06:32:00
I have read through forums that arima models are not suited for high frequency data, when I decompose the ts that initiate with a frequency of 24*60.
> s15033<- stations[stations$siteid == 15033,]
> s15033.ts <- ts(s15033$Count, start = c(2015,239), frequency = 1440)
Thank you for the reply
Here is an updated hourly sample of the dataset as requested
What would be the right approach and recommended method to use for such high frequency?
Edit
Thank you so much for your input, I had to digest all of the information you provided.
In my case, I have no choice but to use R so as that incoporate the model into the workflow of my application.
I understand that I will be better off, forecast at the hour level and use external regressors to help with the shifts and sudden spike that may appear in the time series.
As follow up, I have a couple of questions and apologies if the questions may sound a bit rudimentary as I am still learning about forecasting and R
- How can I implement in R the hybrid causal/ARIMA/pulse you came up with in Autobox
- I have about 1000 parking site, would I need a model for each parking site
- Would machine learning model help with predicting at lower granularity