Irregulary observed time series and their analysis are fundamental for any application in which data are collected a distributed or/and asyncronous manner. In this paper, we provide theory-sound, practical approaches to analyze such time series. We propose two models and their parameter estimation algorithms for both stationary and non-stationary iregular time series. Our models can be viewed as extensions of the well-known Auto-Regression (AR) model. We then 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. Our experiments with real and synthetic data demonstrate that our approach performs well in computing the basic statistics and doing prediction.
By: Emre Ergodan; Sheng Ma; Alina Beygelzimer; Irina Rish
Published in: RC23702 in 2005
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