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Found 26 papers, 2 papers with code
Variational Neural Stochastic Differential Equations with Change Points
We develop two methodologies for modeling and estimating change points in time-series data with distribution shifts.
A Language Model-Guided Framework for Mining Time Series with Distributional Shifts
Effective utilization of time series data is often constrained by the scarcity of data quantity that reflects complex dynamics, especially under the condition of distributional shifts.
Evaluating Large Language Models on Time Series Feature Understanding: A Comprehensive Taxonomy and Benchmark
Large Language Models (LLMs) offer the potential for automatic time series analysis and reporting, which is a critical task across many domains, spanning healthcare, finance, climate, energy, and many more.
LLM-driven Imitation of Subrational Behavior : Illusion or Reality?
We make an assumption that LLMs can be used as implicit computational models of humans, and propose a framework to use synthetic demonstrations derived from LLMs to model subrational behaviors that are characteristic of humans (e. g., myopic behavior or preference for risk aversion).
Synthetic Data Applications in Finance
Synthetic data has made tremendous strides in various commercial settings including finance, healthcare, and virtual reality.
Neural Stochastic Differential Equations with Change Points: A Generative Adversarial Approach
Given a time series dataset, the proposed method jointly learns the unknown change points and the parameters of distinct neural SDE models corresponding to each change point.
Augment on Manifold: Mixup Regularization with UMAP
Evaluations across diverse regression tasks show that UMAP Mixup is competitive with or outperforms other Mixup variants, show promise for its potential as an effective tool for enhancing the generalization performance of deep learning models.
Multi-Modal Financial Time-Series Retrieval Through Latent Space Projections
Moreover, the required storage, computational time, and retrieval complexity to search in the time-series space are often non-trivial.
INTAGS: Interactive Agent-Guided Simulation
To adapt to any environment with interactive sequential decision making agents, INTAGS formulates the simulator as a stochastic policy in reinforcement learning.
LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study
The recent advancements in Deep Learning (DL) research have notably influenced the finance sector.
MADS: Modulated Auto-Decoding SIREN for time series imputation
Time series imputation remains a significant challenge across many fields due to the potentially significant variability in the type of data being modelled.
Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness
Limit order books are a fundamental and widespread market mechanism.
Deep Gaussian Mixture Ensembles
This work introduces a novel probabilistic deep learning technique called deep Gaussian mixture ensembles (DGMEs), which enables accurate quantification of both epistemic and aleatoric uncertainty.
K-SHAP: Policy Clustering Algorithm for Anonymous Multi-Agent State-Action Pairs
Finally, by clustering the explanations we show that we are able to identify different agent policies and group observations accordingly.
DSLOB: A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shift
Using the proposed synthetic dataset, we provide a holistic analysis on the forecasting performance of three different state-of-the-art forecasting methods.
Learning to simulate realistic limit order book markets from data as a World Agent
Our approach proposes to learn a unique "world" agent from historical data.
StyleTime: Style Transfer for Synthetic Time Series Generation
In this work, a novel formulation of time series style transfer is proposed for the purpose of synthetic data generation and enhancement.
HyperTime: Implicit Neural Representation for Time Series
We show how these networks can be leveraged for the imputation of time series, with applications on both univariate and multivariate data.
Efficient Calibration of Multi-Agent Simulation Models from Output Series with Bayesian Optimization
Multi-agent simulation is commonly used across multiple disciplines, specifically in artificial intelligence in recent years, which creates an environment for downstream machine learning or reinforcement learning tasks.
ABIDES-Gym: Gym Environments for Multi-Agent Discrete Event Simulation and Application to Financial Markets
We introduce a general technique to wrap a DEMAS simulator into the Gym framework.
Towards Realistic Market Simulations: a Generative Adversarial Networks Approach
Unfortunately, this approach does not capture the market response to the experimental agents' actions.
Learning who is in the market from time series: market participant discovery through adversarial calibration of multi-agent simulators
In electronic trading markets often only the price or volume time series, that result from interaction of multiple market participants, are directly observable.
Calibrating Over-Parametrized Simulation Models: A Framework via Eligibility Set
Stochastic simulation aims to compute output performance for complex models that lack analytical tractability.
SIM-GAN: Adversarial Calibration of Multi-Agent Market Simulators.
In this paper, we present SIM-GAN -- a multi-agent simulator calibration method that allows to tune simulator parameters and to support more accurate evaluations of candidate trading algorithm.
Get Real: Realism Metrics for Robust Limit Order Book Market Simulations
Machine learning (especially reinforcement learning) methods for trading are increasingly reliant on simulation for agent training and testing.
Risk-Sensitive Compact Decision Trees for Autonomous Execution in Presence of Simulated Market Response
We demonstrate an application of risk-sensitive reinforcement learning to optimizing execution in limit order book markets.
