Hands-On Machine Learning for Algorithmic Trading

How to forecast macro fundamentals

We will build a SARIMAX model for monthly data on an industrial production time series for the 1988-2017 period. As illustrated in the first section on analytical tools, the data has been log-transformed, and we are using seasonal (lag-12) differences. We estimate the model for a range of both ordinary and conventional AR and MA parameters using a rolling window of 10 years of training data, and evaluate the RMSE of the 1-step-ahead forecast, as shown in the following simplified code (see GitHub for details):

for p1 in range(4):                # AR order    for q1 in range(4):            # MA order        for p2 in range(3):        # seasonal AR order            for q2 in range(3):    # seasonal MA order                y_pred = []                for i, T in enumerate(range(train_size, len ...