Money in The Trees: Using Random Forests to Predict The Economy
Much like a dense woodland of decision trees, Random Forests employ an ensemble learning approach that combines the predictive power of numerous models to yield a robust and accurate forecast. This exploration seeks to demystify the algorithmic magic behind these forests, shedding light on how they dissect and interpret complex economic data such as the ISM PMI.
The ISM PMI
The ISM PMI, or Institute for Supply Management Purchasing Managers’ Index, is a widely watched economic indicator in the United States. It provides insight into the health of the manufacturing sector. The PMI is based on a survey of purchasing managers from various industries, asking about things like new orders, production, employment, supplier deliveries, and inventories.
The ISM PMI helps us understand how well the manufacturing businesses are doing. It’s like a report card that looks at different aspects of these businesses, such as how many new orders they’re getting, how much they’re producing, whether they’re hiring more people, and how quickly they’re getting supplies. If the PMI is above 50, it generally means the manufacturing sector is expanding, and if it’s below 50, it suggests a contraction.
In essence, it’s a useful tool for analysts, investors, and policymakers to gauge the overall economic health and trends in the manufacturing industry.
The following shows the monthly evolution of the ISM PMI.
In the next section, we will create a random forest forecasting algorithm in Python that will use the past changes in the value of the ISM PMI to forecast the next changes.
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