学习用Python进行时间序列预测，涵盖预处理、可视化、分解及ARIMA、SARIMA、Prophet等模型。基于能源与经济真实数据集，掌握模型选择与交叉验证技巧。
原始标题：Python for Time Series Forecasting (2025)
Released 07/2025
With Jesus Lopez
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Skill level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 4h 19m 11s | Size: 750 MB
Master time series forecasting in Python using real datasets, with hands-on skills in preprocessing, visualization, decomposition, model selection, and diagnostics.
Course details
Learn practical time series forecasting with Python using real-world datasets from energy (EIA – U.S. Energy Information Administration) and economics (FRED – Federal Reserve Economic Data).
Build skills step by step, from loading and preprocessing time series data to decomposing trends and seasonality, visualizing patterns with Plotly, and applying forecasting models like ARIMA, SARIMA, exponential smoothing, and Prophet. Learn to evaluate model performance using error metrics and cross-validation techniques like walk-forward validation.
The course emphasizes hands-on exercises in a GitHub Codespaces environment, so you can immediately apply what you learn to your own datasets. Whether you’re working with sales, energy, or financial data, you’ll gain the skills to generate accurate, interpretable forecasts that drive real-world decisions.
百度网盘下载：
