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时间序列预测的常见方法及思考

2020-12-22 08:27:06 InfoQ

{"type":"doc","content":[{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"一、背景"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"随着大数据的发展,自然科学、社会科学、工业工程、金融科技等领域都积累了海量的数据,在这些海量的数据中,时间序列数据(按时间戳顺序依次到达的数据)是其中重要的组成部分。利用这些时间序列数据来预测其未来一段时间的状态有着广泛的应用场景,比如在金融领域被使用来做现金流量预测、股票价格预测,在零售行业被使用来做业务收入预测、库存消耗预测,在旅游行业被使用来预测旅游订单量、客服服务量等,在气象、人口密度预测等方面也被广泛使用来帮助决策者做出有数据支撑的重要决策。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"在携程也有一些时间序列预测相关的业务场景,比如下单量预测、话务量预测、客流量预测等,以下将介绍我们在处理时间序列预测相关问题使用的一些方法与思考。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"二、时间序列预测比较常见的工具方法"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"通常来说,时间序列预测工具方法可以归结为三大类:一类是基于业务场景理解的因子预测模型,一类是传统时间序列预测模型,比如均值回归、ARIMA、指数平滑预测法(比如Holt-Winters)等,还有一类是机器学习模型,比如支持向量机、树模型(比如GBM、QRF)、神经网络模型(比如RNN、CNN)等。"}]},{"type":"heading","attrs":{"align":null,"level":3},"content":[{"type":"text","text":"2.1 基于业务场景理解的因子预测模型"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}}]}

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