成都理工大学毕业设计（论文）测井时间序列的支持向量机回归预测摘要统计学习理论是针对小样本情况下的机器学习理论，其核心思想是通过控制学习机器的复杂度实现对学习机器推广能力的控制。支持向量机能够尽量提高学习机的推广能力，即使由有限数据集得到的判别函数对独立的测试集仍能够得到较小的误差。因此，本文把支持向量机用于测井时间序列的回归预测。首先，介绍了时间序列和支持向量机的基础理论。其次，详细介绍了支持向量机的回归原理和算法。最后，本文根据石油地质勘探的实际问题，将支持向量机运用测井曲线预测储层参数——孔隙度。结果表明，该方法预测精度高，方法稳定有效。支持向量机较好的解决了小样本测井勘探的实际问题。关键词：支持向量机；时间序列；回归预测I成都理工大学毕业设计（论文）LoggingtimeseriessupportvectormachineregressionAbstract:Statisticaltheoryisacaseofmachinelearningtheorywhichisbasedonsmallsample.It’scoreideaisthemachinebycontrollingthecomplexityoflearningtoachievethepromotionoftheabilityoflearningmachinecontrol.Supportvectormachinetomaximizethegeneralizationabilityoflearningmachine,evenifalimiteddatasetobtainedfromthediscriminantfunctionontheindependenttestsetwillbesmallerstillerror.Therefore,thesupportvectormachineisusdtologgingtimeseriesregression.Firstofall,thisarticleintroducesthetheoryofthetime-seriesandthebasisofsupportvectormachine.Second,itintroducesdetailedinformationonthereturnofsupportvectormachinetheoryandalgorithm.Finally,thisarticleinaccordancewiththeactualgeologicalexplorationofoilwillbetheuseofsupportvectormachinepredictionofreservoirparameterslogging-porosity.Theresultsshowthathighpredictionaccuracyofthemethod,astableandefficientmethod.Supportvectormachinetoresolvebetterthesmallsampleofthepracticalproblemsloggingexploration.Keywords：supportvectormachines；timeseries；regressionII成都理工大学毕业设计（论文）目录第1章前言............................................................................................................11.1选题意义.............................................................................................................11.2研究现状.............................................................................................................11.3论文内容.............................................................................................................2第2章测井时间序列................................................................................................32.1时间序列概述.....................................................................................................32.2时间序列的预测方法.........................................................................................42.2.1时间序列线性预测方法...............................................................................42.2.2时间序列的非线性预测方法.......................................................................52.2.3自回归移动平均(ARMA)模型....................................................................62.2.4季节型模型.................................................................................................10第3章支持向量机的原理和方法..........................................................................113.1SVM的基本思想..............................................................................................113.1.1最优分类面.................................................................................................113.1.2广义的最优...
