15.6k citations · 17.7k across the 43 of their papers we have counts for
45 papers
Tailored Forecasting from Short Time Series via Meta-learning
Declan A. Norton, Edward Ott, Andrew Pomerance +2
Machine learning models can effectively forecast dynamical systems from time-series data, but they typically require large amounts of past data, making forecasting particularly cha…
Stabilizing Machine Learning Prediction of Dynamics: Noise and Noise-inspired Regularization
Alexander Wikner, Joseph Harvey, Michelle Girvan +4
Recent work has shown that machine learning (ML) models can be trained to accurately forecast the dynamics of unknown chaotic dynamical systems. Short-term predictions of the state…
A Meta-learning Approach to Reservoir Computing: Time Series Prediction with Limited Data
Daniel Canaday, Andrew Pomerance, Michelle Girvan
Recent research has established the effectiveness of machine learning for data-driven prediction of the future evolution of unknown dynamical systems, including chaotic systems. Ho…
Parallel Machine Learning for Forecasting the Dynamics of Complex Networks
Keshav Srinivasan, Nolan Coble, Joy Hamlin +3
Forecasting the dynamics of large complex networks from previous time-series data is important in a wide range of contexts. Here we present a machine learning scheme for this task…
Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components
Alexander Wikner, Jaideep Pathak, Brian R. Hunt +3
We consider the problem of data-assisted forecasting of chaotic dynamical systems when the available data is in the form of noisy partial measurements of the past and present state…
Hybrid Backpropagation Parallel Reservoir Networks
Matthew Evanusa, Snehesh Shrestha, Michelle Girvan +2
In many real-world applications, fully-differentiable RNNs such as LSTMs and GRUs have been widely deployed to solve time series learning tasks. These networks train via Backpropag…