Predictive modeling: least squares method for compression of time-series data

Saraswathi Mukherjee; Justin Zobel

doi:10.1117/12.290354

6 October 1997 Predictive modeling: least squares method for compression of time-series data

Saraswathi Mukherjee, Justin Zobel

Proceedings Volume 3229, Multimedia Storage and Archiving Systems II; (1997) https://doi.org/10.1117/12.290354
Event: Voice, Video, and Data Communications, 1997, Dallas, TX, United States

Abstract

Time-series data form a major class of numerical data that is stored in statistical databases. In an earlier paper, we instantiated a framework in an effort to automate the process of compression, by designing comparative predictive models for data sources which are time- dependent. In this paper, we include one more model for compression of time-series data, into this framework. This model uses the method of least squares and the parameters in this model are optimized by an off-line process using this method; it allows the data to be efficiently encoded using a combination of Golomb and gamma coding techniques. We achieve enhanced compression performance as compared tour previous models and performs better than existing compression techniques as well. We apply the model to real work data sources such as astrophysical, geographical and business data sources.

Citation Download Citation

Saraswathi Mukherjee and Justin Zobel "Predictive modeling: least squares method for compression of time-series data", Proc. SPIE 3229, Multimedia Storage and Archiving Systems II, (6 October 1997); https://doi.org/10.1117/12.290354

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