- 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
本課程提供時間序列分析的入門介紹，並在Google Colab中進行實操練習。參與者將探索ARIMA模型、Prophet以及其他時間序列預測技術，以分析和預測時序數據。課程重點在於這些技術在基於雲端環境（如Google Colab）中的實際應用。
本課程為講師指導的培訓（線上或線下），針對希望使用Google Colab將時間序列預測技術應用於實際數據的中級數據專業人士。
在培訓結束時，參與者將能夠：
- 了解時間序列分析的基本概念。
- 使用Google Colab處理時間序列數據。
- 應用ARIMA模型預測數據趨勢。
- 利用Facebook的Prophet庫進行靈活的預測。
- 可視化時間序列數據及預測結果。
課程形式
- 互動式講授與討論。
- 大量練習與實踐。
- 在實時實驗環境中進行實操。
課程定制選項
- 如需定制本課程，請聯繫我們進行安排。
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
United Arab Emirates - Time Series Analysis with Google Colab
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