Learning While Tracking: A Practical System Based on Variational Gaussian Process State-Space Model and Smartphone Sensory Data

In this thesis, we propose a novel nonparametric modeling framework for financial time series data analysis, and we apply the framework to the problem of time varying volatility modeling. Existing parametric models have a rigid transition function form and they often have over-fitting problems when model parameters are estimated using maximum likelihood methods. These drawbacks effect the models' forecast performance. To solve this problem, we take Bayesian nonparametric modeling approach. By adding Gaussian process prior to the hidden state transition process, we extend the standard state-space model to a Gaussian process state-space model. We introduce our Gaussian process regression stochastic volatility (GPRSV) model. Instead of using maximum likelihood methods, we use Monte Carlo inference algorithms. Both online particle filter and offline particle Markov chain Monte Carlo methods are studied to learn the proposed model. We demonstrate our model and inference methods with both simulated and empirical financial data.

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EndoSensorFusion: Particle Filtering-Based Multi-Sensory Data Fusion with Switching State-Space Model for Endoscopic Capsule Robots

2018 IEEE International Conference on Robotics and Automation (ICRA) ◽

10.1109/icra.2018.8460472 ◽

2018 ◽

Cited By ~ 3

Author(s):

Mehmet Turan ◽

Yasin Almalioglu ◽

Hunter Gilbert ◽

Helder Araujo ◽

Taylan Cemgil ◽

...

Keyword(s):

Data Fusion ◽

State Space ◽

Particle Filtering ◽

State Space Model ◽

Space Model ◽

Sensory Data ◽

Endoscopic Capsule ◽

Endoscopic Capsule Robots ◽

Switching State

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A Gaussian process state-space model for atmospheric CO2 and sea surface temperature index reconstruction from boron isotope and planktonic δ18O proxies

Proceedings of the 10th International Conference on Climate Informatics ◽

10.1145/3429309.3429316 ◽

2020 ◽

Author(s):

Taehee Lee ◽

Charles E. Lawrence

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Gaussian Process ◽

State Space ◽

State Space Model ◽

Sea Surface ◽

Atmospheric Co2 ◽

Boron Isotope ◽

Temperature Index ◽

Space Model

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Financial time series volatility analysis using Gaussian process state-space models

10.32920/ryerson.14655975.v1 ◽

2021 ◽

Author(s):

Jianan Han

Keyword(s):

Time Series ◽

Gaussian Process ◽

State Space ◽

Financial Time Series ◽

State Space Model ◽

Likelihood Methods ◽

Nonparametric Modeling ◽

Space Model ◽

Financial Time ◽

Maximum Likelihood Methods

In this thesis, we propose a novel nonparametric modeling framework for financial time series data analysis, and we apply the framework to the problem of time varying volatility modeling. Existing parametric models have a rigid transition function form and they often have over-fitting problems when model parameters are estimated using maximum likelihood methods. These drawbacks effect the models' forecast performance. To solve this problem, we take Bayesian nonparametric modeling approach. By adding Gaussian process prior to the hidden state transition process, we extend the standard state-space model to a Gaussian process state-space model. We introduce our Gaussian process regression stochastic volatility (GPRSV) model. Instead of using maximum likelihood methods, we use Monte Carlo inference algorithms. Both online particle filter and offline particle Markov chain Monte Carlo methods are studied to learn the proposed model. We demonstrate our model and inference methods with both simulated and empirical financial data.

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Gaussian Process-Integrated State Space Model for Continuous Joint Angle Prediction from EMG and Interactive Force in a Human-Exoskeleton System

Applied Sciences ◽

10.3390/app9081711 ◽

2019 ◽

Vol 9 (8) ◽

pp. 1711 ◽

Cited By ~ 5

Author(s):

Yan Zeng ◽

Jiantao Yang ◽

Yuehong Yin

Keyword(s):

Gaussian Process ◽

State Space ◽

Muscle Activation ◽

Joint Angle ◽

State Space Model ◽

Measurement Model ◽

The State ◽

State Model ◽

Space Model ◽

Emg Signal

As one of the most direct indicators of the transparency between a human and an exoskeleton, interactive force has rarely been fused with electromyography (EMG) in the control of human-exoskeleton systems, the performances of which are largely determined by the accuracy of the continuous joint angle prediction. To achieve intuitive and naturalistic human intent learning, a state space model (SSM) for continuous angle prediction of knee joint is developed. When the influence of the interactive force is often ignored in the existing models of human-exoskeleton systems, interactive force is applied as the measurement model output of the proposed SSM, and the EMG signal is used as the state model input signal to indicate muscle activation. The forward dynamics of joint motion and the human-machine interaction mechanism, i.e., the biomechanical interpretations of the interactive force generation mechanism, are derived as the bases for the state model and measurement model based on Hill’s muscle model and semiphenomenological (SP) muscular model, respectively. Gaussian process (GP)-based nonlinear autoregressive with the exogenous inputs (NARX) model and back-propagation neural network (BPNN) are applied to provide better adaptivity for the SSM in practical applications. Corresponding experimental results demonstrate the validity and superiority of the method.

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