DeepTime: Using Deep Time-Index Meta-Learning to Improve Non-Stationary Time-Series Forecasting

TL;DR: The performance of existing time-series forecasting methods can degrade due to non-stationarity, where the statistical distribution of time-series data changes over time. Our new DeepTime method overcomes non-stationarity issues by leveraging a “forecasting as meta-learning” framework on deep time-index models. DeepTime achieves competitive accuracy on the long-sequence time-series

13 Oct 2022 • #DeepTime

ETSformer: Exponential Smoothing Transformers for Time-Series Forecasting

TL;DR: We developed a new time-series forecasting model called ETSformer that leverages the power of two frameworks. By combining the classical intuition of seasonal-trend decomposition and exponential smoothing with modern transformers – as well as introducing novel exponential smoothing and frequency attention mechanisms – ETSformer achieves state-of-the-art performance. Background Before diving

23 Aug 2022 • #ETSformer