Dimensionality Reduction and Filtering on Time Series Sensor Streams

Abstract

This chapter surveys fundamental tools for dimensionality reduction and filtering of time series streams, illustrating what it takes to apply them efficiently and effectively to numerous problems. In particular, we show how least-squares based techniques (auto-regression and principal component analysis) can be successfully used to discover correlations both across streams, as well as across time. We also broadly overview work in the area of pattern discovery on time series streams, with applications in pattern discovery, dimensionality reduction, compression.