Modern Strategies for Time Series Regression

Regression Quantiles for Time Series

In this article we study nonparametric estimation of regression quantiles by inverting a weighted Nadaraya-Watson estimator (WNW) of conditional distribution function, which was rst used by Hall, Woll and Yao (1999). First, under some regularity conditions, we establish the asymptotic normality and weak consistency of the WNW conditional distribution estimator for-mixing time series at both bou...

Algorithms for Segmenting Time Series

As with most computer science problems, representation of the data is the key to ecient and eective solutions. Piecewise linear representation has been used for the representation of the data. This representation has been used by various researchers to support clustering, classication, indexing and association rule mining of time series data. A variety of algorithms have been proposed to obtain...

Regression Models for Time Series Analysis

Nonparametric regression for locally stationary time series

In this paper, we study nonparametric models allowing for locally stationary regressors and a regression function that changes smoothly over time. These models are a natural extension of time series models with time-varying coefficients. We introduce a kernel-based method to estimate the time-varying regression function and provide asymptotic theory for our estimates. Moreover, we show that the...

Simple Geodesic Regression for Image Time-Series

Geodesic regression generalizes linear regression to general Riemannian manifolds. Applied to images, it allows for a compact approximation of an image time-series through an initial image and an initial momentum. Geodesic regression requires the definition of a squared residual (squared distance) between the regression geodesic and the measurement images. In principle, this squared distance sh...