SAXO: An optimized data-driven symbolic representation of time series
Alexis Bondu, Marc Boullé, B. Grossin · 2013
In France, the currently emerging “smart grid” and more particularly the 35 millions of “smart meters” will produce a large amount of daily updated metering data. The main french provider of electricity (EDF) is interested by compact and generic representations of time series which allow to accelerate the processing of data. This article proposes a new data-driven symbolic representation of time series named SAXO, where each symbol represents a typical distribution of data points. Furthermore, the time dimension is optimally discretized into intervals by using a parameter free Bayesian coclustering approach (MODL). SAXO is favorably compared with the SAX representation by evaluating a classifier trained from recoded datasets. Our experiments highlight a significant gap in performance between both approaches.