I have a dataset containing the prevalence rate of Malaria in Botswana, starting in 1990 and ending in 2014. My task is to verify whether these data can be used in order to make predictions on the future Malaria prevalance rate. I know that 24 data points is probably not enough, but I decided to give it a try.
#plot data Mal.TS <- ts(Mal$Value, start=1990, end=2014, freq=1) plot.ts(Mal.TS)
Now it would have been nice if there was a completely increasing/decreasing trend, but unfortunately the trend was first increasing and later decreasing.
#Test for stationarity adf.test(Mal.TS)
Because of the first increasing and later decreasing trend and the absence of variance as there is only one data point per year in a limited dataset, the Dickey-Fuller test suggests that the data are stationary.
#Test AFC and PACF acf(Mal.TS) pacf(Mal.TS)
#Do arima fit <- arima(Mal.TS, order=c(2,0,1))
#Predict pred <- predict(fit, n.ahead = 5) ts.plot(Mal.TS,pred$pred, lty = c(1,3))
I know this is a poor analysis because of the lack of datapoints. However, I would like some suggestions on how to interpret the results that I found and reasons why this cannot work. The plot shows that the Malaria Prevalence in Botswana has been decreasing over the last 10 years, so one would expect the data to suggest that in the future the prevalence will keep decreasing. Yet, the model predicts the prevalance to increase. Why is this?
One way to possibly address this contra-intuitive result is by adding exogeneous data using the
xreg argument. If I could include time series data on GDP, Health care expenditures,... which probably correlate with the Malaria prevalence data and thus also show a decreasing trend, I might be able to predict the future prevalence to be decreasing. Is this correct?
If the task of predicting the future malaria prevalence rate using the 24 earlier data points is not possible, could you give a clear reasoning why this is the case?
So in short I have following questions:
- How come the arima model predicts the prevalance rate to increase in the future
- Can I cause the predictions to be decreasing using exogeneous data like GDP and health care expenditures?
- If the analysis is really hopeless, could you give a clear reasoning why this is the case?