Time Series Forecasting

Forecasting comparison of different neural architectures on the Multiscale Lorenz-96 system

Optimizing Sequence Models for Dynamical Systems

We systematically ablate core mechanisms of Transformers and RNNs, finding that attention-augmented Recurrent Highway Networks outperform standard Transformers on forecasting high-dimensional chaotic systems.

Time Series Forecasting

LSTNet architecture diagram showing convolutional, recurrent, recurrent-skip, and autoregressive components

LSTNet: Long- and Short-Term Time Series Network

LSTNet is a deep learning framework for multivariate time series forecasting that uses convolutional layers for local dependencies, a recurrent-skip component for periodic long-term patterns, and an autoregressive component for scale robustness.