Statistical Models for Unequally Spaced Time Series

Irregularly observed time series and their analysis are fundamental for any application in which data are collected in a distributed or asynchronous manor. We propose a theoretical framework for analyzing both stationary and non-stationary irregularly spaced time series. Our models can be viewed as extensions of the well known auto-regression (AR) model. We provide experiments suggesting that, in practice, the proposed approach performs well in computing the basic statistics and doing prediction. We also develop a resampling strategy that uses the proposed models to reduce irregular time series to regular time series. This enables us to take advantage of the vast number of approaches developed for analyzing regular time series.