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Variational Neural Stochastic Differential Equations with Change Points

Autor: El-Laham, Yousef, Sun, Zhongchang, Zhu, Haibei, Balch, Tucker, Vyetrenko, Svitlana

In this work, we explore modeling change points in time-series data using neural stochastic differential equations (neural SDEs). We propose a novel model formulation and training procedure based on the variational autoencoder (VAE) framework for mod

Externí odkaz: http://arxiv.org/abs/2411.00635

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A Language Model-Guided Framework for Mining Time Series with Distributional Shifts

Autor: Zhu, Haibei, El-Laham, Yousef, Fons, Elizabeth, Vyetrenko, Svitlana

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. Existing datasets may not encompass the full range of statistical

Externí odkaz: http://arxiv.org/abs/2406.05249

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Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection

Autor: Li, Zhenglin, Zhu, Haibei, Liu, Houze, Song, Jintong, Cheng, Qishuo

Publikováno v: International Journal of Computer Science and Information Technology, 2024, 2(1), 1-9

This study conducts a thorough examination of malware detection using machine learning techniques, focusing on the evaluation of various classification models using the Mal-API-2019 dataset. The aim is to advance cybersecurity capabilities by identif

Externí odkaz: http://arxiv.org/abs/2403.02232

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Synthetic Data Applications in Finance

Synthetic data has made tremendous strides in various commercial settings including finance, healthcare, and virtual reality. We present a broad overview of prototypical applications of synthetic data in the financial sector and in particular provide

Externí odkaz: http://arxiv.org/abs/2401.00081

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Once Burned, Twice Shy? The Effect of Stock Market Bubbles on Traders that Learn by Experience

Autor: Zhu, Haibei, Vyetrenko, Svitlana, Grundl, Serafin, Byrd, David, Dwarakanath, Kshama, Balch, Tucker

We study how experience with asset price bubbles changes the trading strategies of reinforcement learning (RL) traders and ask whether the change in trading strategies helps to prevent future bubbles. We train the RL traders in a multi-agent market s

Externí odkaz: http://arxiv.org/abs/2312.17472

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A Model-Based Synthetic Stock Price Time Series Generation Framework

Autor: Zhu, Haibei, Vyetrenko, Svitlana, Balch, Tucker

The Ornstein-Uhlenbeck (OU) process, a mean-reverting stochastic process, has been widely applied as a time series model in various domains. This paper describes the design and implementation of a model-based synthetic time series model based on a mu

Externí odkaz: http://arxiv.org/abs/2311.02209

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Improving Driver Situation Awareness Prediction using Human Visual Sensory and Memory Mechanism

Autor: Zhu, Haibei, Misu, Teruhisa, Martin, Sujitha, Wu, Xingwei, Akash, Kumar

Situation awareness (SA) is generally considered as the perception, understanding, and projection of objects' properties and positions. We believe if the system can sense drivers' SA, it can appropriately provide warnings for objects that drivers are

Externí odkaz: http://arxiv.org/abs/2111.00087

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