Predictability of Mobile Phone Associations

Bjørn Sand Jensen, Jan Larsen, Lars Kai Hansen, Jakob Eg Larsen, Kristian Jensen

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    Abstract

    Prediction and understanding of human behavior is of high importance in many modern applications and research areas ranging from context-aware services, wireless resource allocation to social sciences. In this study we collect a novel dataset using standard mobile phones and analyze how the predictability of mobile sensors, acting as proxies for humans, change with time scale and sensor type such as GSM and WLAN. Applying recent information theoretic methods, it is demonstrated that an upper bound on predictability is relatively high for all sensors given the complete history (typically above 90%). The relation between time scale and the predictability bound is examined for GSM and WLAN sensors, and both are found to have predictable and non-trivial behavior even on quite short time scales. The analysis provides valuable insight into aspects such as time scale and spatial quantization, state representation, and general behavior. This is of vital interest in the development of context-aware services which rely on forecasting based on mobile phone sensors.
    Original languageEnglish
    Title of host publication21st European Conference on Machine Learning : Mining Ubiquitous and Social Environments Workshop
    Publication date2010
    Publication statusPublished - 2010
    Event21st European Conference on Machine Learning : Mining Ubiquitous and Social Environments Workshop - Barcelona, Spain
    Duration: 1 Jan 2010 → …

    Conference

    Conference21st European Conference on Machine Learning : Mining Ubiquitous and Social Environments Workshop
    CityBarcelona, Spain
    Period01/01/2010 → …

    Cite this

    Jensen, B. S., Larsen, J., Hansen, L. K., Larsen, J. E., & Jensen, K. (2010). Predictability of Mobile Phone Associations. In 21st European Conference on Machine Learning: Mining Ubiquitous and Social Environments Workshop