Irregular time series data is becoming increasingly prevalent with the growth of multi-sensor
systems as well as the continued use of unstructured manual data recording mechanisms …

An overview of the recent developments of time-series neural network modeling is
presented along with its use in model predictive control (MPC). A tutorial on the construction …

Models using structured state space sequence (S4) layers have achieved state-of-the-art
performance on long-range sequence modeling tasks. An S4 layer combines linear state …

Recent progress in neural forecasting accelerated improvements in the performance of large-
scale forecasting systems. Yet, long-horizon forecasting remains a very difficult task. Two …

The conjoining of dynamical systems and deep learning has become a topic of great
interest. In particular, neural differential equations (NDEs) demonstrate that neural networks …

A central goal of sequence modeling is designing a single principled model that can
address sequence data across a range of modalities and tasks, particularly on long-range …

Recurrent neural networks (RNNs), temporal convolutions, and neural differential equations
(NDEs) are popular families of deep learning models for time-series data, each with unique …

The imputation of missing values in time series has many applications in healthcare and
finance. While autoregressive models are natural candidates for time series imputation …

Modeling continuous-time dynamics on irregular time series is critical to account for data
evolution and correlations that occur continuously. Traditional methods including recurrent …

A central problem in learning from sequential data is representing cumulative history in an
incremental fashion as more data is processed. We introduce a general framework (HiPPO) …