Machine learning model outperforms professional forecasters
The model may uncover new relationships in data that were previously overlooked, researcher says
Machine learning methods can significantly outperform professional forecasters, says a senior data scientist at the Federal Reserve Bank of Kansas City.
In the paper, Aaron Hall compares the performance of standard consensus and statistical forecasts with machine learning methods for predicting the monthly US unemployment rate. He tests the consensus blue-chip forecast, a simple statistical model, a random walk, and a machine learning model based on big data from the St Louis Fed’s Fred-MD
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