add MC dropout to model uncertainty and add confidence level

[shane-huang]

Monte Carlo Dropout is a simple way of adding uncertainty for modeling extreme/unexpected events and for anomaly detection.

• Easy to implement.
• Also provides a way for NN's to calculate confidence level and empirical coverage metrics as statistical methods.

Refer to paper: https://arxiv.org/abs/1709.01907

[shanyu-sys]

use Monte Carlo dropout with tsp.fit(mc=True) add a new interface predict_with_uncertainty for pipeline:

y_pred, y_pred_uncertainty = pipeline.predict_with_uncertainty(input_df, n_iter=100)

y_pred is a dataframe with datetime and value column. value column contains the mean value of the prediction results for n_iter times. y_pred_uncertainty is a numpy array with a shape of [num samples, future_seq_len]