- 具备Python编程的中级知识
- 熟悉基本统计和数据分析技术
受众
- 数据分析师
- 数据科学家
- 处理时间序列数据的专业人员
本课程提供时间序列分析的入门介绍，并在Google Colab中进行实操练习。参与者将探索ARIMA模型、Prophet以及其他时间序列预测技术，以分析和预测时序数据。课程重点在于这些技术在基于云端环境（如Google Colab）中的实际应用。
本课程为讲师指导的培训（线上或线下），针对希望使用Google Colab将时间序列预测技术应用于实际数据的中级数据专业人士。
在培训结束时，参与者将能够：
- 了解时间序列分析的基本概念。
- 使用Google Colab处理时间序列数据。
- 应用ARIMA模型预测数据趋势。
- 利用Facebook的Prophet库进行灵活的预测。
- 可视化时间序列数据及预测结果。
课程形式
- 互动式讲授与讨论。
- 大量练习与实践。
- 在实时实验环境中进行实操。
课程定制选项
- 如需定制本课程，请联系我们进行安排。
时间序列分析简介
- 时间序列数据概述
- 时间序列的组成部分：趋势、季节性、噪声
- 在Google Colab中设置时间序列分析
时间序列的探索性Data Analysis分析
- 可视化时间序列数据
- 分解时间序列的组成部分
- 检测季节性和趋势
时间序列的ARIMA模型Forecasting
- 理解ARIMA（自回归积分滑动平均模型）
- 选择ARIMA模型的参数
- 在Python中实现ARIMA模型
Prophet时间序列分析简介Forecasting
- Prophet时间序列预测概述
- 在Google Colab中实现Prophet模型
- 处理预测中的节假日和特殊事件
高级Forecasting技术
- 处理时间序列中的缺失数据
- 多变量时间序列预测
- 使用外部回归变量定制预测
评估和微调预测模型
- 时间序列预测的性能指标
- 微调ARIMA和Prophet模型
- 交叉验证和回测
时间序列分析的实际应用
- 时间序列预测的案例研究
- 使用真实数据集进行实践练习
- 在Python中继续时间序列分析的下一步
总结与下一步
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