图 书 馆
通过学习全局特征改进共享单车系统的需求预测Improving Demand Prediction in Bike Sharing System by Learning Global Features |
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| 课程网址: | https://videolectures.net/videos/kdd2016_zeng_global_features |
| 主讲教师: | Ming Zeng |
| 开课单位: | KDD 2016研讨会 |
| 开课时间: | 2016-10-12 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | A bike sharing system deploys bicycles at many open docking stations and makes them available to the public for shared use. These bikes can be checked-in and checked-out at any of the docking stations. Predicting daily visits is important for service providers to optimize bike allocation and station maintenance. In this paper, we formulate this prediction problem as a regression task. Through data analysis, we develop several features that are very helpful in predictions. Moreover, we demonstrate that there are significant differences among the patterns of visits at different stations. To improve prediction accuracy, we propose station-centric augmented with global feature transformation. The gradient boosting decision tree (GBDT) and neural network (NN) techniques are leveraged to extract global features. The experimental results demonstrate that the proposed model offers better prediction performance compared to two baseline approaches. |
| 关 键 词: | 全局特征; 共享单车; 需求预测; 决策树 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2025-01-08：liyq |
| 最后编审: | 2025-01-08：liyq |
| 阅读次数: | 10 |