Alternative approach to TS scikit models

[Yard1]

Describe the changes you've made

Alternate, simpler approach to sklearn models for time series.

Type of change

Please delete options that are not relevant.

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Code style update (formatting, local variables)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• This change requires a documentation update

How Has This Been Tested?

Please describe the tests that you ran to verify your changes. Provide instructions so we can reproduce.

Describe if there is any unusual behaviour of your code(Write NA if there isn't)

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Checklist:

• My code follows the style guidelines of this project.
• I have performed a self-review of my own code.
• I have commented my code, particularly in hard-to-understand areas.
• I have made corresponding changes to the documentation.
• My changes generate no new warnings.
• I have added tests that prove my fix is effective or that my feature works.
• New and existing unit tests pass locally with my changes.
• Any dependent changes have been merged and published in downstream modules.

Screenshots

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[Yard1]

@ngupta23 as we have talked on Slack, this is how I'd have done it. This approach is easier to maintain as it doesn't require separate classes for every different sklearn model. I also included a few bug fixes, and suffixed self.forecaster with an underscore to follow convention.