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How to scale spatiotemporal data
Say for a given location you have 20 weeks worth of weather and other temporal values. The dataset is compromised of several unique locations, all with measurements across the same 20 weeks. The data matrix has dimension [no locations x no weeks x no features]. For a given observation/location, each week is sequentially fed into a recurrent model.
How would you scale the data? WRT location and time, or across all weeks for a particular feature(ignoring location and time). Also, …!-->