论文标题
Python的基本时间序列预测课程
A basic time series forecasting course with Python
论文作者
论文摘要
本文的目的是提供一组基于Python的工具，以使用时间序列数据集来开发预测。该材料基于作者为期四周的课程，该课程已教授了有关运营研究，管理科学，分析和统计学一年的MSC计划的学生七年。但是，它很容易适应其他各种受众，包括执行管理或某些本科课程。使用此材料不需要python的特殊知识。然而，我们假设对标准统计预测方法（例如指数平滑，自动回收的集成移动平均平均值（ARIMA）和基于回归的技术）的熟悉程度很高，这是提供这样的课程所必需的。对于有兴趣教授这样的课程或对相关方法和工具的数学背景的人，可以提供对基于此材料的相关数据，代码和讲义的访问（请参阅Github.com/abzemkoho/forecasting）。
The aim of this paper is to present a set of Python-based tools to develop forecasts using time series data sets. The material is based on a four week course that the author has taught for seven years to students on operations research, management science, analytics, and statistics one-year MSc programmes. However, it can easily be adapted to various other audiences, including executive management or some undergraduate programmes. No particular knowledge of Python is required to use this material. Nevertheless, we assume a good level of familiarity with standard statistical forecasting methods such as exponential smoothing, AutoRegressive Integrated Moving Average (ARIMA), and regression-based techniques, which is required to deliver such a course. Access to relevant data, codes, and lecture notes, which serve as based for this material are made available (see github.com/abzemkoho/forecasting) for anyone interested in teaching such a course or developing some familiarity with the mathematical background of relevant methods and tools.
