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所在平台: Udemy |
课程主页: https://www.udemy.com/course/time-series-analysis-and-forecasting-using-python/
课程评论：没有评论
课程名称：2023年使用Python进行时间序列分析与预测 课程概述：本课程专为需要预测在线用户、流量、贷款组合表现、房地产、用户消费习惯等数据的学员而设计。时间序列分析是处理有时间依赖性数据的有效方法。本课程是学习如何使用Python进行时间序列分析的绝佳在线资源。 课程内容包括： - 使用NumPy和Pandas库进行数据处理和操作的基础知识。 - 探索statsmodels库及其强大的时间序列分析工具，包括误差-趋势-季节性分解及基础的霍尔特-温特斯方法。 - 创建自相关和偏自相关图，并结合强大的基于ARIMA的模型，包括季节性ARIMA模型和SARIMAX，以纳入外生数据。 - 学习最先进的深度学习技术，如使用循环神经网络预测未来数据点。 本课程将教授学员当下最实用的技术，使其能够胜任量化金融分析师、数据分析师或数据科学家的职位。学员将掌握应用于实际的复杂时间序列分析技能。我们将利用最流行的Python编程语言，涵盖诸多广泛使用的模型，如： - 自回归模型（AR） - 滞后平均模型（MA） - 自回归移动平均模型（ARMA） - 自回归集成移动平均模型（ARIMA） - 季节性自回归集成移动平均模型（SARIMA） 为何等待？每一天都是错失的机会。点击“立即购买”按钮，开始今天就掌握时间序列分析！ 适合人群： - 渴望成为数据科学家的学员 - 编程初学者 - 对量化金融感兴趣的人 - 希望在金融领域专攻的程序员 - 需要更好地应用Python知识的金融专业毕业生和从业者
Is this one of your needs? Then course is for you Forecasting Online Users ?Forecasting Traffic ?Forecasting the expected performance of their loan portfolio?Forecasting real-estate properties?Forecasting User Spending Habits ?If there is some time dependency, then you know it - the answer is: time series analysis.Welcome to the best online resource for learning how to use the Python programming Language for Time Series Analysis!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 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.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.Then 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 will teach you the practical skills that would allow you to land a job as a quantitative finance analyst, a data analyst or a data scientist.In no time, you will acquire the fundamental skills that will enable you to perform complicated time series analysis directly applicable in practice. We take the most prominent tools and implement them through Python - the most popular programming language right now. With that in mind…With these tools we will master the most widely used models out there:· AR (autoregressive model)· MA (moving-average model)· ARMA (autoregressive-moving-average model)· ARIMA (autoregressive integrated moving average model). SARIMA (seasonal autoregressive integrated moving average model)Why wait? Every day is a missed opportunity.Click the "Buy Now" button and start mastering time series in Python today.Who this course is for:Aspiring data scientists.Programming beginners.People interested in quantitative finance.Programmers who want to specialize in finance.Finance graduates and professionals who need to better apply their knowledge in Python.
