Search Results for author: Ting Gao

Found 13 papers, 5 papers with code

Early Warning Prediction with Automatic Labeling in Epilepsy Patients

no code implementations9 Oct 2023 Peng Zhang, Ting Gao, Jin Guo, Jinqiao Duan, Sergey Nikolenko

Early warning for epilepsy patients is crucial for their safety and well-being, in particular to prevent or minimize the severity of seizures.

EEG Meta-Learning

Early warning indicators via latent stochastic dynamical systems

no code implementations7 Sep 2023 Lingyu Feng, Ting Gao, Wang Xiao, Jinqiao Duan

Detecting early warning indicators for abrupt dynamical transitions in complex systems or high-dimensional observation data is essential in many real-world applications, such as brain diseases, natural disasters, financial crises, and engineering reliability.

EEG Electroencephalogram (EEG) +1

Learning Stochastic Dynamical Systems as an Implicit Regularization with Graph Neural Networks

no code implementations12 Jul 2023 Jin Guo, Ting Gao, Yufu Lan, Peng Zhang, Sikun Yang, Jinqiao Duan

To that end, the observed randomness and spatial-correlations are captured by learning the drift and diffusion terms of the stochastic differential equation with a Gumble matrix embedding, respectively.

Time Series

Reservoir Computing with Error Correction: Long-term Behaviors of Stochastic Dynamical Systems

1 code implementation1 May 2023 Cheng Fang, Yubin Lu, Ting Gao, Jinqiao Duan

The prediction of stochastic dynamical systems and the capture of dynamical behaviors are profound problems.

Multi-task Meta Label Correction for Time Series Prediction

1 code implementation9 Mar 2023 Luxuan Yang, Ting Gao, Wei Wei, Min Dai, Cheng Fang, Jinqiao Duan

To address the above issues, we create a label correction method to time series data with meta-learning under a multi-task framework.

Contrastive Learning Data Visualization +5

Model Based Reinforcement Learning with Non-Gaussian Environment Dynamics and its Application to Portfolio Optimization

no code implementations23 Jan 2023 Huifang Huang, Ting Gao, Pengbo Li, Jin Guo, Peng Zhang, Nan Du

With the fast development of quantitative portfolio optimization in financial engineering, lots of AI-based algorithmic trading strategies have demonstrated promising results, among which reinforcement learning begins to manifest competitive advantages.

Algorithmic Trading Decision Making +5

GT-CausIn: a novel causal-based insight for traffic prediction

no code implementations12 Dec 2022 Ting Gao, Rodrigo Kappes Marques, Lei Yu

We then present a novel model named Graph Spatial-Temporal Network Based on Causal Insight (GT-CausIn), where prior learned causal information is integrated with graph diffusion layers and temporal convolutional network (TCN) layers.

Traffic Prediction

Stock Trading Optimization through Model-based Reinforcement Learning with Resistance Support Relative Strength

no code implementations30 May 2022 Huifang Huang, Ting Gao, Yi Gui, Jin Guo, Peng Zhang

Reinforcement learning (RL) is gaining attention by more and more researchers in quantitative finance as the agent-environment interaction framework is aligned with decision making process in many business problems.

Decision Making Model-based Reinforcement Learning +2

Learning effective dynamics from data-driven stochastic systems

no code implementations9 May 2022 Lingyu Feng, Ting Gao, Min Dai, Jinqiao Duan

Multiscale stochastic dynamical systems have been widely adopted to a variety of scientific and engineering problems due to their capability of depicting complex phenomena in many real world applications.

An Optimal Control Method to Compute the Most Likely Transition Path for Stochastic Dynamical Systems with Jumps

1 code implementation31 Mar 2022 Wei Wei, Ting Gao, Jinqiao Duan, Xiaoli Chen

One of the challenges to calculate the most likely transition path for stochastic dynamical systems under non-Gaussian L\'evy noise is that the associated rate function can not be explicitly expressed by paths.

An end-to-end deep learning approach for extracting stochastic dynamical systems with $α$-stable Lévy noise

1 code implementation31 Jan 2022 Cheng Fang, Yubin Lu, Ting Gao, Jinqiao Duan

Recently, extracting data-driven governing laws of dynamical systems through deep learning frameworks has gained a lot of attention in various fields.

Neural network stochastic differential equation models with applications to financial data forecasting

1 code implementation25 Nov 2021 Luxuan Yang, Ting Gao, Yubin Lu, Jinqiao Duan, Tao Liu

In this article, we employ a collection of stochastic differential equations with drift and diffusion coefficients approximated by neural networks to predict the trend of chaotic time series which has big jump properties.

Time Series Time Series Forecasting +1

Learning the temporal evolution of multivariate densities via normalizing flows

no code implementations29 Jul 2021 Yubin Lu, Romit Maulik, Ting Gao, Felix Dietrich, Ioannis G. Kevrekidis, Jinqiao Duan

Specifically, the learned map is a multivariate normalizing flow that deforms the support of the reference density to the support of each and every density snapshot in time.

Cannot find the paper you are looking for? You can Submit a new open access paper.