Search Results for author: Yoh-ichi Mototake

Found 5 papers, 1 papers with code

Autoregressive with Slack Time Series Model for Forecasting a Partially-Observed Dynamical Time Series

1 code implementation28 Jun 2023 Akifumi Okuno, Yuya Morishita, Yoh-ichi Mototake

This study delves into the domain of dynamical systems, specifically the forecasting of dynamical time series defined through an evolution function.

Time Series

Signal identification without signal formulation

no code implementations13 Apr 2023 Yoh-ichi Mototake, Y-h. Taguchi

We propose that the correlation length of a dynamical system and the number of samples are crucial for the practical definition of noise variables among the signal variables generated by such a system.

Interpretable Conservation Law Estimation by Deriving the Symmetries of Dynamics from Trained Deep Neural Networks

no code implementations31 Dec 2019 Yoh-ichi Mototake

We propose a novel framework that can infer the hidden conservation laws of a complex system from deep neural networks (DNNs) that have been trained with physical data of the system.

Time Series Time Series Analysis

Bayesian Spectral Deconvolution Based on Poisson Distribution: Bayesian Measurement and Virtual Measurement Analytics (VMA)

no code implementations11 Dec 2018 Kenji Nagata, Yoh-ichi Mototake, Rei Muraoka, Takehiko Sasaki, Masato Okada

Since the measurement time is strongly related to the signal-to-noise ratio for the Poisson noise model, Bayesian measurement with Poisson noise model enables us to clarify the relationship between the measurement time and the limit of estimation.

Cannot find the paper you are looking for? You can Submit a new open access paper.