Atypical information theory for real-valued data

Atypical sequences are subsequences of long sequences that deviates from the `normal´ data. In a previous paper we have developed an information theory approach to such sequences for discrete data. In the current paper we extend this principle to real-valued data, whereby it is possible to use signal processing tools to search for atypical data. The application of this principle is to extract a few interesting sets of information from `big data´ sets. We include a simple application to stock market data.