awslabs/gluonts

`lagged_sequence_values` helper does not work well with multivariate data

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#2,708 opened on Mar 6, 2023

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 (7 comments) (0 reactions) (0 assignees)Python (753 forks)batch import
enhancementgood first issuemultivariate

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Description

one issue i found with the lagged_sequence_values helper is that with multivariate inputs this helper doesn't return the stacked tensors... e.g.

# prior_input.shape torch.Size([1, 167, 137])
# input.shape torch.Size([1, 24, 137])

lags = lagged_sequence_values([0, 167], prior_input, input, dim=1)
# lags.shape torch.Size([1, 24, 137, 2])

where i would have expected torch.Size([1, 24, 137*2])

Originally posted by @kashif in https://github.com/awslabs/gluonts/issues/2618#issuecomment-1455833300

Contributor guide

Research direction
Inspect the `lagged sequence values` function in the gluonts source code to understand how it currently stacks tensors and modify it to flatten the last two dimensions when input is multivariate.
Tech stack
pythonpytorch
Domain
datamachine learning
Issue type
Bug
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-3 hours
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Active
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Clear
Prerequisites
PythonPyTorch
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
70