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所在平台: Udemy |
课程主页: https://www.udemy.com/course/applied-time-series-analysis-and-forecasting-in-python/
课程评论：没有评论
课程名称：《Python中的应用时间序列分析与预测》 课程概述： 在这门课程中，我们将探讨商业银行如何预测其贷款组合的预期表现，以及投资经理如何评估股票投资组合的风险，还会介绍用于预测房地产的定量方法。时间序列分析是处理这些问题的关键。课程将教授你实用技能，使你具备在量化金融分析师、数据分析师或数据科学家等职位中工作的能力。 课程亮点： - 简明易懂 - 内容全面 - 实用性强 - 符合实际需求 - 兼具丰富的练习和资源 我们将使用当前最流行的编程语言Python，通过Python中最优秀的工具进行学习。课程内容将从基础的时间序列理论入手，使学员在后续模型的理解上打下坚实基础。我们将使用强大的pandas库以及其他基本库，如NumPy、matplotlib、StatsModels、finance、ARCH等。 课程将涵盖的主要模型包括： - 自回归模型（AR） - 移动平均模型（MA） - 自回归移动平均模型（ARMA） - 自回归积分移动平均模型（ARIMA） - 带外生变量的自回归积分移动平均模型（ARIMAX） - 季节性自回归移动平均模型（SARIMA） - 条件异方差模型（ARCH） - 广义条件异方差模型（GARCH） - 向量自回归移动平均模型（VARMA） 此外，课程还将介绍更复杂的统计模型和深度学习技术，涵盖ARIMA、SARIMA、SARIMAX的多重时间序列预测，以及使用Tensorflow进行深度学习的多种技术，包括线性模型、深度神经网络（DNN）、卷积神经网络（CNN）和长短期记忆网络（LSTM）等。 学员将通过超过五个完整的Python项目进行实践，所有源代码均可访问。课程还将提供大量额外材料，包括笔记本文件、课程笔记、测验问题及许多练习，确保你全面掌握时间序列分析的技能。
How does a commercial bank forecast the expected performance of its loan portfolio?Or how does an investment manager estimate a stock portfolio's risk?Which are the quantitative methods used to predict real-estate properties?If there is some time dependency, then you know it - the answer is time series analysis.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 have created a time series course that is not only timeless but also:· Easy to understand· Comprehensive· Practical· To the point· Packed with plenty of exercises and resourcesBut we know that may not be enough.We take the most prominent tools and implement them through Python - the most popular programming language right now. With that in mind…Welcome to Time Series Analysis in Python!The big question in taking an online course is what to expect. And we've made sure that you are provided with everything you need to become proficient in time series analysis.We start by exploring the fundamental time series theory to help you understand the modelling that comes afterwards.Then throughout the course, we will work with several Python libraries, providing you with complete training. We will use the powerful time series functionality built into pandas, as well as other fundamental libraries such as NumPy, matplotlib, StatsModels, finance, ARCH and prima.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)· ARIMAX (autoregressive integrated moving average model with exogenous variables). SARIA (seasonal autoregressive moving average model). SARIMA (seasonal autoregressive integrated moving average model). SARIMAX (seasonal autoregressive integrated moving average model with exogenous variables)· ARCH (autoregressive conditional heteroscedasticity model)· GARCH (generalized autoregressive conditional heteroscedasticity model). VARMA (vector autoregressive moving average model)We know that time series is one of those topics that always leaves some doubts.Until now.This course is exactly what you need to comprehend time series once and for all. Not only that, but you will also get a ton of additional materials - notebook files, course notes, quiz questions, and many, many exercises - everything is included.This is the only course that combines the latest statistical and deep learning techniques for time series analysis. First, the course covers the basic concepts of time series:stationarity and augmented Dicker-Fuller testseasonalitywhite noiserandom walkautoregressionmoving averageACF and PACF,Model selection with AIC (Akaike's Information Criterion)Then, we move on and apply more complex statistical models for time series forecasting:ARIMA (Autoregressive Integrated Moving Average model)SARIMA (Seasonal Autoregressive Integrated Moving Average model)SARIMAX (Seasonal Autoregressive Integrated Moving Average model with exogenous variables)We also cover multiple time series forecasting with:VAR (Vector Autoregression)VARMA (Vector Autoregressive Moving Average model)VARMAX (Vector Autoregressive Moving Average model with exogenous variable)Then, we move on to the deep learning section, where we will use Tensorflow to apply different deep learning techniques for times series analysis:Simple linear model (1-layer neural network)DNN (Deep Neural Network)CNN (Convolutional Neural Network)LSTM (Long Short-Term Memory)CNN + LSTM modelsResNet (Residual Networks)Autoregressive LSTMThroughout the course, you will complete more than 5 end-to-end projects in Python, with all source code available to you.
