摘要翻译：
消费者是习惯的生物，通常是周期性的，与工作、购物和其他时间表联系在一起。我们分析了这家全球最大的共享单车公司一个月的数据，以得出需求行为周期，最初使用了动物跟踪模型，该模型显示大客户符合Ornstein-Uhlenbeck模型，需求峰值的周期为7、12、24小时和7天。自行车需求的Lorenz曲线表明，大多数消费者的使用是不频繁的，从时间序列模型中得出的需求周期会强烈地过度拟合数据，从而产生不可靠的模型。通过对共享单车合同时空张量的阈值小波分析，可以将数据压缩成56系数的模型，几乎没有信息损失，这表明共享单车的需求行为异常强烈和有规律。对预测需求的改进可以通过调整我们的模型从空气质量和天气信息以及不经常骑手的需求中过滤出来的“噪音”来实现。
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英文标题：
《Prediction of Shared Bicycle Demand with Wavelet Thresholding》
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作者：
J. Christopher Westland (University of Illinois), Jian Mou (XiDian
  University), Dafei Yin (Mobike)
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最新提交年份：
2018
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  Consumers are creatures of habit, often periodic, tied to work, shopping and other schedules. We analyzed one month of data from the world's largest bike-sharing company to elicit demand behavioral cycles, initially using models from animal tracking that showed large customers fit an Ornstein-Uhlenbeck model with demand peaks at periodicities of 7, 12, 24 hour and 7-days. Lorenz curves of bicycle demand showed that the majority of customer usage was infrequent, and demand cycles from time-series models would strongly overfit the data yielding unreliable models. Analysis of thresholded wavelets for the space-time tensor of bike-sharing contracts was able to compress the data into a 56-coefficient model with little loss of information, suggesting that bike-sharing demand behavior is exceptionally strong and regular. Improvements to predicted demand could be made by adjusting for 'noise' filtered by our model from air quality and weather information and demand from infrequent riders.
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PDF链接：
https://arxiv.org/pdf/1802.02683