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所在平台: Udemy |
课程主页: https://www.udemy.com/course/time-series-analysis-regression-forecasting-with-python/
课程评论：没有评论
课程名称：使用Python进行时间序列分析与回归预测 课程概述： 预测是现代数据科学的重要组成部分，驱动着金融、零售、医疗等多个领域的决策。本课程是您掌握时间序列分析和基于回归的预测的逐步指南。无论您是初学者数据科学家还是希望增加预测分析技能的分析师，本课程涵盖了从基本符号到高级模型（如ARIMA和SARIMA）的所有内容。您将学习如何准备机器学习的数据、可视化趋势以及如何像专业人士一样验证模型。通过在Python中的动手编码、真实案例和专家指导，您将获得建立和部署实际能产生影响的预测模型的信心。 课程内容简介： 第一部分：时间序列分析基础 开始学习时间序列数据的基础知识，了解其独特性及其在数据科学中的重要性。设置Anaconda和Jupyter环境后，深入数据加载、预处理和特征工程。您将学习如何可视化时间相关模式、应用变换以及使用基本统计技术如移动平均和指数平滑。在这一部分结束时，您将为构建时间敏感模型做好准备。 第二部分：时间序列预测模型 在这一部分中，您将从理论转向实践，学习关键的时间序列预测模型。从简单模型开始，逐步深入到自回归（AR）、移动平均（MA）和ARIMA。学习如何正确拆分时间序列数据，使用前向验证验证预测，并通过自相关函数（ACF）和偏自相关函数（PACF）图解读相关性。最后，学习先进的季节性模型SARIMA，并通过动手编码在Python中应用这些知识。 第三部分：线性回归的数据预处理 在构建回归模型之前，您需要清洗和有意义的数据。本部分教授高质量回归建模所需的基本预处理步骤。您将学习探索性数据分析、异常值检测、缺失值填补、季节性处理和相关性分析。还将学习变量变换、创建虚拟变量，并为建模准备数据集。每个概念都通过Python演示进行巩固。 第四部分：构建和评估回归模型 最后，通过在Python中构建回归模型来将数据变为生动的洞察。您将理解如何应用普通最小二乘（OLS）方法、解读系数，并使用R平方、F统计量等评估模型性能。您将构建简单和多元线性回归模型，包括处理分类变量。这一部分确保您不仅在模型创建上充满信心，而且能够有效地解释结果。 课程总结： 恭喜您！您现在已经掌握了使用Python进行时间序列预测和回归建模的核心技能。从构建干净的数据集到可视化趋势和预测未来值，您已准备好应对各个领域的真实预测挑战。您将数据转化为洞察的能力将使您在数据专业人士中脱颖而出。
Course Introduction:Forecasting is at the heart of modern data science, powering decision-making across finance, retail, healthcare, and beyond. This comprehensive course is your step-by-step guide to mastering time-series analysis and regression-based forecasting using Python. Whether you're a budding data scientist or an analyst aiming to add predictive analytics to your skillset, this course covers everything from basic notations to advanced models like ARIMA and SARIMA. You'll also learn how to prepare data for machine learning, visualize trends, and validate models like a pro.Through hands-on coding in Python, real-world use cases, and expert-led instruction, you'll gain the confidence to build and deploy forecasting models that actually drive impact.Section 1: Foundations of Time-Series Analysis in PythonStart your journey by understanding the fundamentals of time-series data-what makes it unique and why it matters in data science. You'll set up your environment with Anaconda and Jupyter, then dive into data loading, preprocessing, and feature engineering. You'll also learn how to visualize time-dependent patterns, apply transformations, and use basic statistical techniques like moving averages and exponential smoothing. By the end of this section, you'll be well-prepared for building time-aware models.Section 2: Time-Series Forecasting ModelsIn this section, you'll move from theory to practice with key time-series forecasting models. Starting with naive models, you'll progress to Auto-Regression (AR), Moving Average (MA), and ARIMA. Learn how to split time-series data properly, validate predictions using walk-forward validation, and interpret autocorrelation using ACF and PACF plots. You'll wrap up with SARIMA, an advanced seasonal model, and apply it all in Python through hands-on coding.Section 3: Data Preprocessing for Linear RegressionBefore building regression models, you need clean and meaningful data. This section teaches you the essential preprocessing steps required for high-quality regression modeling. You'll work through exploratory data analysis, outlier detection, missing value imputation, seasonality handling, and correlation analysis. You'll also transform variables, create dummy variables, and prepare your dataset for modeling. Each concept is reinforced with Python demos to solidify your understanding.Section 4: Building & Evaluating Regression ModelsFinally, bring your data to life by building regression models in Python. You'll understand how to apply the Ordinary Least Squares (OLS) method, interpret coefficients, and evaluate model performance using R-Squared, F-statistics, and more. You'll build both simple and multiple linear regression models, including handling categorical variables. This section ensures you're confident not only in model creation but also in explaining the results effectively.Course Conclusion:Congratulations! You've now developed the core skills required to perform both time-series forecasting and regression modeling using Python. From building clean datasets to visualizing trends and predicting future values, you're ready to tackle real-world forecasting challenges in any domain. Your ability to transform data into insight will set you apart as a capable and confident data professional.
