5 papers
Exponential mixing properties of nonlinear functional autoregressive models
Shuntarou Suzuki, Yoshikazu Terada
The importance of functional data analysis has increased substantially in recent years. In machine learning, nonlinear function regression based on deep neural networks is referred…
Neural Stochastic Processes for Satellite Precipitation Refinement
Shunya Nagashima, Takumi Bannai, Shuitsu Koyama +2
Accurate precipitation estimation is critical for flood forecasting, water resource management, and disaster preparedness. Satellite products provide global hourly coverage but con…
A Decomposition-based State Space Model for Multivariate Time-Series Forecasting
Shunya Nagashima, Shuntaro Suzuki, Shuitsu Koyama +1
Multivariate time series (MTS) forecasting is crucial for decision-making in domains such as weather, energy, and finance. It remains challenging because real-world sequences inter…
PENGUIN: General Vital Sign Reconstruction from PPG with Flow Matching State Space Model
Shuntaro Suzuki, Shuitsu Koyama, Shinnosuke Hirano +1
Photoplethysmography (PPG) plays a crucial role in continuous cardiovascular health monitoring as a non-invasive and cost-effective modality. However, PPG signals are susceptible t…
Adaptive Bayes estimator for stochastic differential equations with jumps under small noise asymptotics
Shuntaro Suzuki, Takaaki Wakamatsu, Yasutaka Shimizu
In this paper, we consider parameter estimation for stochastic differential equations driven by Wiener processes and compound Poisson processes. We assume unknown parameters corres…