[FIX] Numerical stability scaling for timeseries forecasting tasks

[dengdifan]

Types of changes

• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)

Note that a Pull Request should only contain one of refactoring, new features or documentation changes.
Please separate these changes and send us individual PRs for each.
For more information on how to create a good pull request, please refer to The anatomy of a perfect pull request.

Checklist:

• My code follows the code style of this project.
• My change requires a change to the documentation.
• I have updated the documentation accordingly.

• Have you checked to ensure there aren't other open Pull Requests for the same update/change?
• Have you added an explanation of what your changes do and why you'd like us to include them?
• Have you written new tests for your core changes, as applicable?
• Have you successfully ran tests with your changes locally?

Description

The current scaler implementation might scale to a very high value, as described in #462. This PR aims to restrict the range of the scaled values.

Motivation and Context

How has this been tested?

[codecov]

Codecov Report

Merging #467 (b20f26d) into development (faa1efd) will increase coverage by 0.14%.
The diff coverage is 100.00%.

@@               Coverage Diff               @@
##           development     #467      +/-   ##
===============================================
+ Coverage        85.50%   85.64%   +0.14%     
===============================================
  Files              231      231              
  Lines            16311    16314       +3     
  Branches          3012     3012              
===============================================
+ Hits             13946    13972      +26     
+ Misses            1527     1507      -20     
+ Partials           838      835       -3     

Impacted Files	Coverage Δ
autoPyTorch/constants.py	100.00% <100.00%> (ø)
...cessing/time_series_preprocessing/scaling/utils.py	92.07% <100.00%> (+0.07%)	⬆️
...mponents/setup/forecasting_target_scaling/utils.py	92.63% <100.00%> (+2.20%)	⬆️
...ipeline/components/setup/network_backbone/utils.py	87.31% <0.00%> (-1.50%)	⬇️
...orch/pipeline/components/training/metrics/utils.py	88.00% <0.00%> (+2.00%)	⬆️
...omponents/training/data_loader/base_data_loader.py	94.66% <0.00%> (+2.66%)	⬆️
...ine/components/training/trainer/StandardTrainer.py	100.00% <0.00%> (+3.70%)	⬆️
...nts/setup/network_backbone/ConvNetImageBackbone.py	100.00% <0.00%> (+3.84%)	⬆️
...nts/setup/early_preprocessor/EarlyPreprocessing.py	91.42% <0.00%> (+5.71%)	⬆️
... and 5 more

Help us with your feedback. Take ten seconds to tell us how you rate us. Have a feature suggestion? Share it here.