Walk Forward Model Validation

I would love to see the implementation of walk-forward testing.

A rolling-forecast scenario also called walk-forward model validation is the recommended method (gold standard) for constructing machine learning models dedicated to time-series data. Walk-forward validation provides the most realistic evaluation of machine learning models since k-fold cross validation does not work for time series data.

Each time step of the test dataset is walked one at a time. A model is used to make a forecast for the time step, then the actual expected value from the test set is taken and made available to the model for the forecast on the next time step.

For more info, see code examples ([1](https://machinelearningmastery.com/backtest-machine-learning-models-time-series-forecasting/), [2](https://machinelearningmastery.com/simple-time-series-forecasting-models/)) or [research paper](https://www.researchgate.net/publication/282252518_Time_Series_Forecasting_with_Volume_Weighted_Support_Vector_Machines).

No repository file, test, or entry point is named. Read the existing model-validation flow, then compare it with the linked walk-forward examples and research paper. Done means each test timestep is forecast before its actual value is made available for the next forecast.

Written by the indexing model from the issue text.