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- Have an image for each day (weeks, months, years). Within each image, there will be bands that represent weather
parameters - Image (raster data, each pixel has data) .TIFF files
- For time series prediction, think about each pixel. For each pixel, we will have T (number of previous timesteps we're using
to predict the next step) x N (number of weather parameters) data. Eg. 10 previous days, 50 parameters for each day -->
10x50 dataframe. - This is 2D data, and if you want to consider spatial information (surrounding points), you can add another dimension
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