Learning NumPy Array

Using more robust statistics

We can make our code from the The time-dependent temperature mean adjusted autoregressive model section more robust by doing the following:

• Computing the median instead of the mean

  avgs[i-1] = np.median(temp[indices])

• Ignoring the outliers with a masked array

  temp[:cutoff] = ma.masked_array(temp[:cutoff], temp[:cutoff] < (q1 - 1.5 * irq))

We get slightly different output with the modified code, with about 70 percent of the values predicted having an absolute error of less than 2 degrees Celsius:

AR params [ 0.95095073 -0.17373633]
% delta less than 2 70.8567244325