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所在平台: Udemy |
课程主页: https://www.udemy.com/course/time-series-analysis-and-forecasting-plus-eda-using-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预测 课程概述： 在这门全面的时间序列分析与预测课程中，您将学习自信分析时间序列数据并做出准确预测所需的一切。课程通过理论与实际示例相结合的方式，时长为10-11小时，让您在时间序列概念上打下坚实基础，并获得各种模型和技术的实践经验。此外，课程也包含探索性数据分析，尽管这些概念可能不完全适用于时间序列分析与预测，但在数据领域中却是必不可少的。 课程内容包括： 1. **理解时间序列**：探索时间序列分析的基本概念，包括时间序列的不同组成部分，如趋势、季节性和噪声。 2. **分解技术**：学习如何将时间序列数据分解为个别组成部分，以更好地理解其潜在模式和趋势。 3. **自回归（AR）模型**：深入了解自回归模型，发掘观察值与若干滞后观察值之间的关系。 4. **移动平均（MA）模型**：探索移动平均模型，理解它们如何有效地平滑噪声并揭示时间序列数据中的隐含模式。 5. **ARIMA模型**：掌握广泛使用的ARIMA模型，结合自回归和移动平均的概念，以应对时间序列数据中的趋势和季节性。 6. **Facebook Prophet**：获得使用Facebook Prophet这一强大的开源时间序列预测工具的实践经验，学习如何利用其能力进行准确预测。 7. **真实世界项目**：将您的知识和技能应用于三个真实世界项目，解决各种时间序列分析与预测问题，积累宝贵的经验和信心。 此外，课程还涵盖以下主题： - **数据预处理与清洗**：学习如何对时间序列数据进行预处理和清洗，以确保数据质量和适合分析，包括处理缺失值、处理异常值以及进行数据转换。 - **多元预测**：探索涉及多个变量的时间序列数据预测技术，学习如何处理和分析包含多个时间序列的数据集，理解多元预测所面临的复杂性和挑战。 通过本课程，您将对时间序列分析与预测有扎实的理解，并具备应用不同模型与技术解决现实问题的能力。立即加入我们，掌握时间序列数据的力量，以做出明智的预测并推动业务决策。今天就报名，开启您的时间序列专家之旅！
In this comprehensive Time Series Analysis and Forecasting course, you'll learn everything you need to confidently analyze time series data and make accurate predictions. Through a combination of theory and practical examples, in just 10-11 hours, you'll develop a strong foundation in time series concepts and gain hands-on experience with various models and techniques.This course also includes Exploratory Data Analysis which might not be 100% applicable for Time Series Analysis & Forecasting, but these concepts are very much needed in the Data space!!This course includes:Understanding Time Series: Explore the fundamental concepts of time series analysis, including the different components of time series, such as trend, seasonality, and noise.Decomposition Techniques: Learn how to decompose time series data into its individual components to better understand its underlying patterns and trends.Autoregressive (AR) Models: Dive into autoregressive models and discover how they capture the relationship between an observation and a certain number of lagged observations.Moving Average (MA) Models: Explore moving average models and understand how they can effectively smooth out noise and reveal hidden patterns in time series data.ARIMA Models: Master the widely used ARIMA models, which combine the concepts of autoregressive and moving average models to handle both trend and seasonality in time series data.Facebook Prophet: Get hands-on experience with Facebook Prophet, a powerful open-source time series forecasting tool, and learn how to leverage its capabilities to make accurate predictions.Real-World Projects: Apply your knowledge and skills to three real-world projects, where you'll tackle various time series analysis and forecasting problems, gaining valuable experience and confidence along the way.In addition to the objectives mentioned earlier, our course also covers the following topics:Preprocessing and Data Cleaning: Students will learn how to preprocess and clean time series data to ensure its quality and suitability for analysis. This includes handling missing values, dealing with outliers, and performing data transformations.Multivariate Forecasting: The course explores techniques for forecasting time series data that involve multiple variables. Students will learn how to handle and analyze datasets with multiple time series and understand the complexities and challenges associated with multivariate forecasting.By the end of this course, you'll have a solid understanding of time series analysis and forecasting, as well as the ability to apply different models and techniques to solve real-world problems. Join us now and unlock the power of time series data to make informed predictions and drive business decisions. Enroll today and start your journey toward becoming a time series expert!
