Update 'Baseline Results' for 'write-up.docx'

[jeff1evesque]

We need to update our "Baseline Results" for our write-up.docx.

[jeff1evesque]

We need to update our visualizations to correspond to our earlier classifiers having a potentially reduced/filtered dataset.

[jeff1evesque]

We need to update our README.md, and discuss the grid search implementation for our ARIMA model.

[jeff1evesque]

Currently, our grid-search ARIMA model is generating an SVD error after a few models have been successfully trained:

    raise LinAlgError("SVD did not converge")
numpy.linalg.LinAlgError: SVD did not converge

Therefore, we may consider taking a log transform of the data before train. This means we'll need a function that can back-transform the data, or prediction to the expected scale.

[jeff1evesque]

We should discuss the use of the grid search, as well as the implemented log-transform to better ensure convergence. Specifically, the log transform was utilized to decrease variability in the data, and potentially assisting for stationarity. Additionally, a small added factor 0.01 was added to each time series value (each value range(0, 1)), prior to modeling to eliminate the possibility of log(0), which breaks the corresponding modeling.

[jeff1evesque]

We need to mention the implementation of our dynamic auto_scale for the ts_index arima model.

[jeff1evesque]

We could mention that the efforts integrated into the ARIMA model to better ensure stationarity, could be applied on the time series data prior to our peak detection methodology.

[jeff1evesque]

729f6c1, 541fb53: even though we implemented our grid-search from our model/timeseries.py on our training data, then performed a rolling prediction on the remaining test dataset:

        # induce stationarity
        result = model.grid_search(auto_scale=auto_scale)

Later datasets (i.e. bats--bats_mmt) failed to converge:

[...INITIAL-TRACE-OMITTED...]
    raise LinAlgError("SVD did not converge")
numpy.linalg.LinAlgError: SVD did not converge

Therefore, we'll implement our custom model.grid_search() iteratively for each rolling time-series prediction. This will be computatively expensive and done within our algorithm/arima.py. While a more dynamic solution will require more thought, it is likely not worth the extra effort.

[jeff1evesque]

Furthermore, we'll need to update our write-up.docx to discuss that a rolling grid-search implementation was performed for the arima modeling.

[jeff1evesque]

78e11aa: we should also state if our arima model does not converge (even with the rolling grid-search), we will catch the error, then throw out the current modeling attempt.

[jeff1evesque]

ab18722: the rolling_grid_search is computatively too expensive. Rather, we will implement the catch_grid_search only when an exception is raised during model.fit. If the successive grid-search fails, then the modeling attempt will finally be caught, and thrown out.