Neural Basis Expansion Analysis for Interpretable Time Series Forecasting (N-BEATS)

The first model that used some components from DL (we can’t call it DL because it is essentially a mix of DL and classical statistics) and made a splash in the field was a model that won the M4 competition (univariate) in 2018. This was a model by Slawek Smyl from Uber (at the time) and was a Frankenstein-style mix of exponential smoothing and an RNN, dubbed ES-RNN (Further reading has links to a newer and faster implementation of the model that uses GPU acceleration). This led to Makridakis et al. putting forward an argument that “hybrid approaches and combinations of methods are the way forward.” The creators of the N-BEATS model aspired to challenge this conclusion by designing a pure DL architecture for time series forecasting. They succeeded in this when they created a model that beat all other methods in the M4 competition (although they didn’t publish it in time to...