Abstract
The experimental scientist need tools to quantify and classify collected data. This paper proposes to give meaning and measure to the intuitive concept of predictability. It is a global and time dependent real valued quantity that, we argue, indicates how hard it is to make a forecast for the next value on a time series. We start with a a definition of predictability for binary words and show properties about its growth and computational cost. Our measure evaluates in time On 3, what is an acceptable performance specially for supporting bounded time decisions. Then, we investigate application procedures illustrated with data achieved from iterations of the logistic map, economic simulations and the Portuguese GDP (Gross Domestic Product).
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Coelho, F., Coelho, H. (2006). Predictability for Autonomous Decision Support. In: Sichman, J.S., Antunes, L. (eds) Multi-Agent-Based Simulation VI. MABS 2005. Lecture Notes in Computer Science(), vol 3891. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11734680_7
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DOI: https://doi.org/10.1007/11734680_7
Publisher Name: Springer, Berlin, Heidelberg
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