Closed iamchrisearle closed 3 months ago
@iamchrisearle here's a detail on the three options:
Note: whatever layout you use for your input, series will be predicted independently of each other (the model is univariate; the batch dimension only makes processing happen in parallel).
For your example: my understanding is that each raster is a different point in time (a different day), so you have 9 time series of length 3 in your toy example. In this case you should do something like
>>> np.stack([raster1, raster2, raster3], axis=-1).reshape((-1, 3))
array([[0, 1, 2],
[2, 4, 5],
[5, 7, 8],
[7, 5, 6],
[8, 2, 3],
[1, 3, 7],
[3, 8, 1],
[6, 6, 4],
[4, 0, 9]])
which will have the required 2D layout (first dimension is batch, second dimension is time).
From the docs:
Where could additional details on the interpretation and limitations of the inputs be found? Tangentially, replying to @abdulfatir in #13
Suppose I want to model raster time-series for rainfall with this toy example:
Could these be modeled with the
... list of 1D tensors
from the docs, by flattening? If so, how can each 1D tensor in the list be interpreted as? Or is this not a valid use case? I have so far tried:Flattening the list of tensors into one 1D tensor
Or if each raster is flattened into a
list of 1D tensors
is this a more appropriate representation to model? Visually this looks incorrect to me.