Forecasting Hospital Bed Demand Using Daily Observations

Question

Basically, my task for the next 3 months is to forecast bed demand and a couple of other variables in a hospital's emergency department. The data is 5 years worth of daily observations of these variables. The data is complete with no missing values.

The goal is to improve the prediction accuracy of the current tool, which is an Excel workbook.

I have not taken any time series or optimization courses in college thus far- so imagine my horror when I realised I had no clue on how to approach this project and that I would be working entirely alone. I was told no one in the department has any experience and no one would be able to help me. I'm using RStudio, but I'm not very proficient since it was self-taught.

From trying out the questions asked on here as well as YouTube tutorials to learn the appropriate syntax and functions, what I have managed to find out is: 1) My data is a time series and I should apply forecasting models to predict future values based on the historical data I have.

2) Daily observations of a long time series has weekly and annual seasonality, so I should define the data as a multi-seasonal time series.

I first tried defining my data as ts(), then msts(). One of the answers here mentioned zoo() would be more appropriate for daily obervations, so I tried that too. The forecasting models I've tried are snaive, ets, auto.arima and TBATS.

I would like to present the plots of the values/forecasts based on day-of-the-week other than all 365 days of the year, which is the only output I could plot. I tried using frequency = 365 and 7, and start = c(2014, 1) and end= c(2018, 365), but I haven't had any luck.

I would really appreciate any advice and help I could get from anyone. Thank you!

Answer 1

Without looking at your data, have you tried to get started with some basic ARIMA modeling and seeing what results you get from that? It’s a fairly friendly way to get started with time series forecasting, depending on your data. I was forecasting by the hour, but the frequency can be adjusted to whatever you need to forecast in. As you have mentioned, you are looking ot change the frequency. Sometimes it’s easier to see a pattern at larger time intervals, and can aggregate your data at larger time intervals.

For example, this converts daily observations to monthly.

library(xts)
dates <- seq(as.Date('2012-01-01'),as.Date('2019-03-31'),by='days')
beds$date.formatted <- dates
beds.xts <- xts(x=beds$neds.count,as.POSIXct(paste(beds$date.formatted)))
end.month <- endpoints(beds.xts,'months')
beds.month <- period.apply(beds.xts,end.month,sum)
beds.monthly.df <- data.frame(date=index(beds.month),coredata(beds.month))
colnames(beds.monthly.df) <- c('Date','Sessions')
beds.monthly <- ts(sessions.monthly.df$Sessions,start=c(2012,1),end=c(2019,3),frequency=12)
plot(beds.monthly)

I’m not sure if that would answer your question, but as you mentioned you are self-taught and stating out, I can share a script with you to help you go get started with an example, and maybe this would help you? It goes through the whole process of checking you have read your data in as a time series, what is time series data, how to check for non-stationary data and seasonality trends, plots that are useful for this, modeling, prediction, plotting actual vs predicted, accuracy, and further issues with the data that could be hindering your model. The video tutorial series are scripted in Python, but you can follow the end-to-end process of forecasting in ARIMA using the equivalent R script for this tutorial: https://code.datasciencedojo.com/rebeccam/tutorials/blob/master/Time%20Series/r_time_series_example.R

https://tutorials.datasciencedojo.com/time-series-python-reading-data/