Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. It only takes a minute to sign up.
Sign up to join this community
I am trying to model a time series that contains a sequence of zeros. I tried fitting an ARIMA model using auto.arima function from the forecast package in R but the MAPE is reported as infinity (probably due to division by zero). Moreover, the auto.arima fits an ARIMA(0,1,0) model over the data.
Can you suggest any types of models that may be appropriate for such data?
A common approach to handle many zeroes in a time series is to use a Croston Model. To implement this model with your time series there are two R packages forecast and tsintermittent. The tsintermittent package optimizes the Croston model $\alpha$ parameter whereas the forecast package produces a forecast for a given $\alpha$ value.
This is not the only approach to forecasting a time series w/ a sequence of zeroes but is a common approach. Another approach would be to fit a zero-inflated or hurdle model.
However, as mentioned in the comments of the post by the OP, the measurements could be continuous on the positive half-line. With non-negative measurements and a zero point mass the most likely approach would be a Tweedie regression. Here is a good post from SO with example codes: https://stackoverflow.com/questions/21807118/r-codes-for-tweedie-compound-poisson-gamma
