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I have a dataset of patients where, for each patient, a measurement is taken 3 times per day. For example, patient 1 has recordings at 7.30 am, 12.30 pm and 8.30 pm. Patient 1 has a collection of 30 days with such data recorded thrice a day.(altogether 3 recordings per day * 30 days = 90 data points).
Is this type of data suitable for time-series forecasting?
Thanks in advance for the help.
Welcome to the site!
Forecasting can be done using any length of time series. For example, if I have a set of data {1, 10, 19, 28}, then I can be pretty sure that the next value in the set is going to be 37 (because there is a strong pattern here: 10=1+9, 19=10+9, etc.).
So if you have a strong signal, then even if you don't have a very long sequence, you can get a pretty accurate forecast.
The question becomes: what type of time series forecasting model should you use?
I would avoid any type of neural network here (the Data Science forum often talks about neural networks of some type). Your data are not rich enough to support estimation of the hundreds (to millions!) of parameters that such models require.
Instead, I would try something fairly simple to start, like a moving average or perhaps an ARIMA-type model.
Remember, each patient is independent (hopefully!) but each measurement per patient is dependent (i.e. if you have patient 1 at time 1, patient 2 at time 2, and patient 3 at time 3, each patient time measurement is dependent on the patient). So if you want to forecast "the value at 7:30 AM regardless of the patient", that's different from forecasting "patient 1's value at 10 pm".
Hope that at least gives you a starting point!
Sure! A time-series is just a set of data points indexed temporally, and that's exactly what you have for each patient.
You can certainly build a forecaster using this data. You may run into a couple wrinkles since your data is not spaced evenly, but that's not an insurmountable barrier.
If you want to quickly prototype a forecasting model, FB Prophet is incredibly easy to use and often works well out-of-the-box.
Since the recording of data is same at regular time intervals for every patient, you are getting a good daily time series data.
And given your question which says you are recording a single measurement daily that means you have a univariate time series analysis problem, as the forecasting is based on just the past values of series data not any other variables.
Hence the data is definitely helpful.
Just keep in mind to not forecast for longer intervals, since the cycle of data is daily, you can forecast may be upto next three days (may be try to start with just one day. You can try ARIMA for this.
