N-BEATS — The First Interpretable Deep Learning Model That Worked for Time Series Forecasting
An easy-to-understand deep dive into how N-BEATS works and how you can use it.
Time series forecasting has been the only area in which Deep Learning and Transformers did not outperform other models.
Looking at the Makridakis M-competition, the winning solutions always relied on statistical models. Until the M4 competition, winning solutions were either pure statistical or a hybrid of ML and statistical models. Pure ML approaches barely surpassed the competition baseline.
This changed with a paper published by Oreshkin, et al. in 2020. The authors published N-BEATS, a promising pure Deep Learning approach. The model beat the winning solution of the M4 competition. It was the first pure Deep Learning approach that outperformed well-established statistical approaches.
N-BEATS stands for Neural Basis Expansion Analysis for Interpretable Time Series.
In this article, I will go through the architecture behind N-BEATS. But do not be afraid, the deep dive will be easy-to-understand. I also show you how we can make the deep learning approach interpretable. However, it is not enough to only understand how N-BEATS works. Thus, I will show you how…
