Multi-dimensional sparse time series: feature extraction

Details Multi-dimensional sparse time series: feature extraction Multi-dimensional sparse time series: feature extraction

2008-03-04

arxiv.org/abs/0803.0405v1

Computer Science

Multimedia

Keywords: multimedia mining, trend, entropy, Zipf law

Scientific paper

We show an analysis of multi-dimensional time series via entropy and statistical linguistic techniques. We define three markers encoding the behavior of the series, after it has been translated into a multi-dimensional symbolic sequence. The leading component and the trend of the series with respect to a mobile window analysis result from the entropy analysis and label the dynamical evolution of the series. The diversification formalizes the differentiation in the use of recurrent patterns, from a Zipf law point of view. These markers are the starting point of further analysis such as classification or clustering of large database of multi-dimensional time series, prediction of future behavior and attribution of new data. We also present an application to economic data. We deal with measurements of money investments of some business companies in advertising market for different media sources.

Affiliated with

Also associated with

No associations

Rating

Multi-dimensional sparse time series: feature extraction does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Multi-dimensional sparse time series: feature extraction, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Multi-dimensional sparse time series: feature extraction will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-406051