Machine learning techniques can overcome many of the shortcomings in conventional forecasting, researchers at the Federal Reserve Bank of Kansas City say in a new working paper.

Thomas Cook and Aaron Hall use a neural network – a form of “deep learning” – to generate a forecast model while remaining “agnostic” to functional form. They test four different architectures and find the encoder-decoder form, originally designed for language modelling, works best.

Each of the four methods produce