clarifying parameters on time series

[gvashishtha]

We are excited to review your PR.

Some context:

Also note that, here we are talking about three different parameters:

1. seriesLength is the length of the whole timeseries.
2. trainSize is the number of points from the beginning of the series that are used for training.
3. windowSize is the size of rolling window during the training of SSA.

The first parameter is dictated by the series and even though it’s not used in SSA, it’s not meaningful for the user to “set” it arbitrarily. The third parameter is recommended to be at least twice as the biggest seasonality in the data that the user care about. The second parameter must be more twice the third parameter.

So to summarize we should have:

1. 2*Seasonality length < windowSize
2. 2*windowSize < trainSize
3. trainSize < seriesLength

The first constraint is recommended while the other two are required.

So we can do the best job, please check:

• There's a descriptive title that will make sense to other developers some time from now.
• There's associated issues. All PR's should have issue(s) associated - unless a trivial self-evident change such as fixing a typo. You can use the format Fixes #nnnn in your description to cause GitHub to automatically close the issue(s) when your PR is merged.
• Your change description explains what the change does, why you chose your approach, and anything else that reviewers should know.
• You have included any necessary tests in the same PR.

[mstfbl]

Added a few nit changes and build-breaking-fix changes such as the trailing whitespaces that causes builds to fail. Once these are fixed, this PR is good to