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

翻译：本文描述了一种从含误差的大型开放混沌系统测量数据中，以合理的伪未来历史线程集合形式表示数据的方法。该方法能够提供渐近一致的预测分布，支持变量选择，易于更新以改进预测，并允许沿未来历史路径检查条件情景以用于规划。通过预测跨越4个季节的9年气候变量（包括两个地点的当地温度和降雨量测量值，提前最多4个季节进行预测），对学习能力和变量选择进行了测试。虽然主要关注降水预测，但所有测量值均展现出一定的可预测性，即使是明显仅向其他变量输入信息的太阳黑子测量值也是如此。这表明学习挂毯不仅为预测开放混沌系统提供了方法，还可利用此类系统测量其耦合系统的动力学特性。