Add a TimeSeriesAnomalyDetector

[ManuelAlvarezC]

There will be a single class in the estimators module. It will contain all the logic required to fit, score, tune and obtain predictions from a pipeline, it will contain the following methods:

• __init__: The class constructor. It will receive the pipeline in the MLBlocks format, along with the cross-validation configuration and persistence options.

• tune: Will use BTB and the cross-validation and scorer from the constructor to find the best hyperparameters for the given template.

• fit: Will fit a pipeline using the hyperparameters found by the tune method or the ones given as an argument to the init method.

• predict: Will use the pipeline to make predictions. It will raise an exception if the pipeline has not been fitted.

[ManuelAlvarezC]

Also, a TimeSeriesLoader class to load the data in a format ready to be used by the TimeSeriesAnomalyDetector. The class constructor will have the following arguments:

• dataset_path: The path to the dataset. It will be an CSV file without index nor headers.
• predictions : Number of steps ahead to predict.
• samples: Number of steps composing each training sample.

And will have a single method load(self, target=True) that will load and format the data and return, for a CSV of size (n_rows, n_features) the following items:

• X: numpy.ndarray a 3-d array of shape (training_samples, samples, n_features )
• y: np.ndarray a 2-d array of shape (training_samples, predictions)