We present a novel variational framework for performing inference in (neural) stochastic
differential equations (SDEs) driven by Markov-approximate fractional Brownian motion …

Abstract Fractional Stochastic Differential Equations are becoming more popular in the
literature as they can model phenomena in financial data that typical Stochastic Differential …

Deep hedging is a deep-learning-based framework for derivative hedging in incomplete
markets. The advantage of deep hedging lies in its ability to handle various realistic market …

EEG source localization remains a challenging problem given the uncertain conductivity
values of the volume conductor models (VCMs). As uncertain conductivities vary across …

In this paper, we introduce a novel method for generating fractional time series through the
utilization of neural networks. Although Neural Stochastic Differential Equations (Neural …

In this paper, we propose the Continuous Time Fractional Topic Model (cFTM), a new
method for dynamic topic modeling. This approach incorporates fractional Brownian …

Neural stochastic differential equations (neural SDEs) are effective for modelling complex
dynamics in time series data, especially random behavior. We introduced JDFlow, a novel …

There are sparse opportunities for direct measurement of upper stratospheric winds, yet
improving their representation in subseasonal‐to‐seasonal prediction models can have …

抄録 株価や経済指標などの金融時系列は, 一般に長期記憶性や不確実性など,
単純なモデルでは再現の難しい特徴を持つことが観測されており, このことは金融市場の複雑性を …

抄録 人工市場シミュレーションによる人工データが金融機械学習モデルの学習に有効であるか検証
する. 金融市場の過去データは数に限りがあることから, 金融時系列データは機械学習モデルの学習 …