149,963 research outputs found
Modeling Binary Time Series Using Gaussian Processes with Application to Predicting Sleep States
Motivated by the problem of predicting sleep states, we develop a mixed
effects model for binary time series with a stochastic component represented by
a Gaussian process. The fixed component captures the effects of covariates on
the binary-valued response. The Gaussian process captures the residual
variations in the binary response that are not explained by covariates and past
realizations. We develop a frequentist modeling framework that provides
efficient inference and more accurate predictions. Results demonstrate the
advantages of improved prediction rates over existing approaches such as
logistic regression, generalized additive mixed model, models for ordinal data,
gradient boosting, decision tree and random forest. Using our proposed model,
we show that previous sleep state and heart rates are significant predictors
for future sleep states. Simulation studies also show that our proposed method
is promising and robust. To handle computational complexity, we utilize Laplace
approximation, golden section search and successive parabolic interpolation.
With this paper, we also submit an R-package (HIBITS) that implements the
proposed procedure.Comment: Journal of Classification (2018
Precursors of extreme increments
We investigate precursors and predictability of extreme increments in a time
series. The events we are focusing on consist in large increments within
successive time steps. We are especially interested in understanding how the
quality of the predictions depends on the strategy to choose precursors, on the
size of the event and on the correlation strength. We study the prediction of
extreme increments analytically in an AR(1) process, and numerically in wind
speed recordings and long-range correlated ARMA data. We evaluate the success
of predictions via receiver operator characteristics (ROC-curves). Furthermore,
we observe an increase of the quality of predictions with increasing event size
and with decreasing correlation in all examples. Both effects can be understood
by using the likelihood ratio as a summary index for smooth ROC-curves
Performance Following: Real-Time Prediction of Musical Sequences Without a Score
(c)2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works
- …