A two-step state space time series modeling method

Generalized state - space models for modeling nonstationary EEG time - series

Contemporary neuroscientific research has access to various techniques for recording time-resolved data relating to human brain activity: electroencephalography (EEG) and magnetoencephalography (MEG) record the electromagnetic fields generated by the brain, while other techniques, such as near-infrared spectroscopy (NIRS) and functional magnetic resonance imaging (fMRI) are sensitive to the loc...

State-Space Time Series Modeling of Structural Breaks

A State-space Method for Language Modeling

In this paper, a new state-space method for language modeling is presented. The complexity of the model is controlled by choosing the dimension of the state instead of the smoothing and back-off methods common in n-gram modeling. The model complexity also controls the generalization ability of the model, allowing the model to handle similar words in similar manner. We compare the state-space mo...

Time Series Analysis by State Space Methods

The purpose of this document is to summarize Part I of the book “Time Series Analysis by State Space Methods ”, written by James Durbin and Siem Jan Koopman.

The forecasting of menstruation based on a state‐space modeling of basal body temperature time series

Women's basal body temperature (BBT) shows a periodic pattern that associates with menstrual cycle. Although this fact suggests a possibility that daily BBT time series can be useful for estimating the underlying phase state as well as for predicting the length of current menstrual cycle, little attention has been paid to model BBT time series. In this study, we propose a state-space model that...