Probabilistic forecasting for spatio-temporal raster data

Specific Domains Probabilistic programming
, , , ,
1

We have a time series gridded/ratser panel dataset (spatio-temporal). The dataset is in 3D, where each ((x, y, t), where x and y ranges from 1-25 while t ranges from 1-1800 though we’re trying to predict just the next time step) coordinate has a numeric value (such as the sea temperature at that location and at that specific point in time). So we can think of it as a matrix with a temporal component. The dataset is similar to this but with just one channel:

Dataset

We’re trying to predict/forecast the nth time step values for the whole region (i.e., all x, y coordinates in the dataset) given the values for the n-1 time steps and the uncertainty.

Can you all suggest any model/architecture/approach for the same? Is it possible to tackle this with multiple kernels with separate kernels for the spatial covariance and separate for the temporal covariance?

Thanks!

2

It really depends on the spatial-temporal resolution. You mentioned that you have temperature as the target variable, it is usually very smooth and seasonal. I had success with StateSpaceModels.jl, it is very robust and explainable, which is usually something you would like to have in climate/weather studies.

3

You might be interesting in trying TemporalGPs.jl, it is specifically designed to work on this kind of problem.