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所在平台: Udemy |
课程主页: https://www.udemy.com/course/python-for-time-series-data-analysis/
课程评论：没有评论
**课程名称：** Python for Time Series Data Analysis **课程概述：** 本课程是学习使用Python进行时间序列数据分析的绝佳在线资源。您将学习如何利用Python进行时间序列数据预测，以预测未来的数据点。 课程内容包括： * **基础操作：** 使用NumPy和Pandas库进行数据处理和操作。 * **Pandas进阶：** 学习Pandas的可视化功能，以及如何处理带时间戳的数据。 * **统计建模：** 掌握statsmodels库强大的时间序列分析工具，包括误差-趋势-季节性分解和Holt-Winters方法。 * **预测模型：** 学习创建自相关 (ACF) 和偏自相关 (PACF) 图，并结合ARIMA模型（包括季节性ARIMA和SARIMAX模型，可加入外生变量）进行预测。 * **深度学习：** 学习使用循环神经网络 (RNN) 等前沿深度学习技术进行未来数据预测。 * **Prophet库：** 学习Facebook开发的Prophet库，一种简单易用且功能强大的时间序列预测Python库。 **课程目标：** 掌握使用Python处理时间序列数据并进行未来预测的各项技能。 **立即加入，开启您的时间序列预测之旅！**
Welcome to the best online resource for learning how to use the Python programming Language for Time Series Analysis!This course will teach you everything you need to know to use Python for forecasting time series data to predict new future data points.We'll start off with the basics by teaching you how to work with and manipulate data using the NumPy and Pandas libraries with Python. Then we'll dive deeper into working with Pandas by learning about visualizations with the Pandas library and how to work with time stamped data with Pandas and Python.Then we'll begin to learn about the statsmodels library and its powerful built in Time Series Analysis Tools. Including learning about Error-Trend-Seasonality decomposition and basic Holt-Winters methods.Afterwards we'll get to the heart of the course, covering general forecasting models. We'll talk about creating AutoCorrelation and Partial AutoCorrelation charts and using them in conjunction with powerful ARIMA based models, including Seasonal ARIMA models and SARIMAX to include Exogenous data points.Afterwards we'll learn about state of the art Deep Learning techniques with Recurrent Neural Networks that use deep learning to forecast future data points.This course even covers Facebook's Prophet library, a simple to use, yet powerful Python library developed to forecast into the future with time series data.So what are you waiting for! Learn how to work with your time series data and forecast the future!We'll see you inside the course!
