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A method of representing measured data from large open chaotic systemssubject to error as collections of threads of plausible pseudo future historiesis described in this paper which provides asymptotically consistent predictivedistributions, allows variable selection, is easily updated to improve thepredictions and which allows examination of conditional scenarios along thefuture history for planning purposes. The method is tested for learning andvariable selection by examining its behavior in predicting 9 years across 4seasons of climate variables, including local temperature and rainfallmeasurements at two locations, predicting up to 4 seasons ahead. Although themain focus is on predicting precipitation, any of the measurements show somecapability of being predicted, even the sunspot measurement that is clearlyjust inputting information into the other variables. This suggests thatlearning tapestries provide ways to not only predict open chaotic systems, butto use such systems to measure the dynamics of the systems they are coupled to.

Learning tapestries, a statistical learning substrate for open chaotic systems measured with error