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Akademický článek
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Forecasting irregular time series presents significant challenges due to two key issues: the vulnerability of models to mean regression, driven by the noisy and complex nature of the data, and the limitations of traditional error-based evaluation met
Externí odkaz:
http://arxiv.org/abs/2411.19341
Autor:
Park, Jihyun, Sarantsev, Andrey
We study a multivariate autoregressive stochastic volatility model for the first 3 principal components (level, slope, curvature) of 10 series of zero-coupon Treasury bond rates with maturities from 1 to 10 years. We fit this model using monthly data
Externí odkaz:
http://arxiv.org/abs/2411.03699
Autor:
Park, Jihyun, Sarantsev, Andrey
Classic stochastic volatility models assume volatility is unobservable. We use the Volatility Index: S&P 500 VIX to observe it, to easier fit the model. We apply it to corporate bonds. We fit autoregression for corporate rates and for risk spreads be
Externí odkaz:
http://arxiv.org/abs/2410.22498
Autor:
Park, Jihyun, Sarantsev, Andrey
We model time series of VIX (monthly average) and monthly stock index returns. We use log-Heston model: logarithm of VIX is modeled as an autoregression of order 1. Our main insight is that normalizing monthly stock index returns (dividing them by VI
Externí odkaz:
http://arxiv.org/abs/2410.22471
Autor:
Lou, Renze, Xu, Hanzi, Wang, Sijia, Du, Jiangshu, Kamoi, Ryo, Lu, Xiaoxin, Xie, Jian, Sun, Yuxuan, Zhang, Yusen, Ahn, Jihyun Janice, Fang, Hongchao, Zou, Zhuoyang, Ma, Wenchao, Li, Xi, Zhang, Kai, Xia, Congying, Huang, Lifu, Yin, Wenpeng
Numerous studies have assessed the proficiency of AI systems, particularly large language models (LLMs), in facilitating everyday tasks such as email writing, question answering, and creative content generation. However, researchers face unique chall
Externí odkaz:
http://arxiv.org/abs/2410.22394
The advent of the Attention mechanism and Transformer architecture enables contextually natural text generation and compresses the burden of processing entire source information into singular vectors. Based on these two main ideas, model sizes gradua
Externí odkaz:
http://arxiv.org/abs/2410.11381
Dialogue State Tracking (DST) is a key part of task-oriented dialogue systems, identifying important information in conversations. However, its accuracy drops significantly in spoken dialogue environments due to named entity errors from Automatic Spe
Externí odkaz:
http://arxiv.org/abs/2409.06263
Autor:
Lee, Jihyun, Lee, Gary Geunbae
Traditional dialogue state tracking approaches heavily rely on extensive training data and handcrafted features, limiting their scalability and adaptability to new domains. In this paper, we propose a novel method that leverages inference and in-cont
Externí odkaz:
http://arxiv.org/abs/2409.06243