I have a series of measurements taken of water pollution on a roughly weekly basis at different locations. In total there is a measurement every 3 days at a different location. I have series of regular environmental measurements (wind, tides, precipitation) taken daily at the same locations with no missing data. I know that the environmental information is a strong predictor of the water pollution level, and I want to forecast the water pollution.

This difference in timescale of the measurements seems to be problematic. If I use the union of the two sets of measurements approximate 6/7 of the measurements of the dependent variable is missing per day and per location. It is known that there can be a lag between the effect from the environmental measurements effect the pollution measurements, how can I quantify this though given the amount of missing data?

As mentioned, I want to use the environmental measurements to build a predictive model, but I am not sure how many days of environmental measurements I should use or how to select that in an appropriate way e.g. interpolating the missing data, and looking at the ACF as per this question: Use ACF and PACF for irregular time series?.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.