sktime / sktime/pytorch-forecasting
Meaning of min/max_encoder_length and min/max_prediction_length in TimeSeriesDataSet
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Description
What is the meaning of having a range for encoder_length and prediction length in TimeSeriesDataSet?
Of course trying multiple value for encoder and prediction length might have an impact in the forecasting performance/business needs. But why do we set a range in the TimeSeriesDataSet instead of single values?
What does a model do with a range since I would assume that one model needs a single value to define the model architecture. A range of values does make sense for hyper-parameter optimization, is this the intention?
Can someone please explain what exactly the range for min/max encoder_length and prediction_length is used for and how to properly set it?
Thanks in advance
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Research direction
Start with the TimeSeriesDataSet entry point and trace how min/max_encoder_length and min/max_prediction_length are used. Document why ranges are accepted, how they affect dataset samples and model behavior, and how users should choose them; the issue does not name a specific file or test to run.
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Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100