Add new timeseries anomaly detection primitives

X-Sigma primitive:
This primitive computes the absolute error of a prediction and checks to see if its magnitude is larger than a factor of x-sigma where x is a positive integer.

Edit the timeseries_errors.py file so that it has methods from only the NASA paper. It should be given a different name like dynamic_error_thresholding.py

For the new primitive, I will call it sigma_error_thresholding.py

Methods needed for this primitive:

• To compute the error for this method, we could use the get_forecast_errors() method similarly implemented in the current timeseries_errors.py

• detect_sigma_outliers(errors, num_sigmas) returns a list of contiguous sequences of anomalies, where each an anomaly has errors[i] > x*sigma_error and sigma_error = np.std(errors).

Start by reading timeseries_errors.py and its get_forecast_errors() method, then compare the requested NASA-paper methods with the proposed sigma_error_thresholding.py name. Define detect_sigma_outliers(errors, num_sigmas) around the stated standard-deviation threshold and contiguous anomaly sequences; done means the new primitive is separated from the existing methods and its behavior is covered by the repository's relevant checks.