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所在平台: Udemy |
课程主页: https://www.udemy.com/course/time-series-analysis-and-forecasting-in-r/
课程评论：没有评论
课程名称：R语言时间序列分析与预测导论 概述：了解现在 - 预测未来！时间序列分析与预测是统计编程的关键领域之一。它使您能够识别时间序列数据中的模式，构建模型，并根据这些模型进行预测。随着现代技术的发展，数据量持续增加，成功的企业深知，基于过去数据进行决策并为未来建模，可以带来巨大的差异。掌握时间序列分析与预测的知识和技能将使您成为公司或机构的宝贵资产，并助力职业发展！ 在本课程中，您将学习如何在R语言中处理日期和时间数据，包括时区、闰年和不同格式等内容，这些都使日期和时间的计算变得尤为复杂。您将学习R Base中的POSIXt类、chron包以及特别是lubridate包。此外，您还将学习如何可视化、清理和准备数据，数据预处理占据了分析师大量的时间，掌握异常值检测、缺失值填补和可视化的最佳函数可以大大提高效率。接下来，您将了解用于时间序列的统计方法，如自相关性、平稳性和单位根检验。您将学习不同模型的工作原理、如何在R中设置这些模型，以及如何将其用于预测和预测分析。课程中涵盖的模型包括ARIMA、指数平滑、季节性分解和一些简单模型作为基准。当然，这些内容都伴随着大量的练习。 这些方法的应用范围非常广泛，几乎在所有定量领域都能找到，特别是在计量经济学和金融领域。例如，股票数据具有时间组件，使其成为预测技术的理想候选者。此外，本课程中的技术也应用于学术界、医学、商业和市场营销等领域。 学习这些方法是否困难？遗憾的是，关于R语言时间序列分析的学习材料往往比较技术化，需要大量的先前知识。本课程的目标是让您尽可能直观和简单地理解建模和预测。尽管您需要对统计学和统计编程有一定的了解，但课程面向没有定量领域（如数学或统计学）专业背景的学员。实际上，任何定期处理时间数据的人都能从本课程中受益。 如何准备以便充分利用本课程？这取决于您的先前知识。但一般来说，您应该知道如何在R中处理标准任务（可以参加R基础课程）。您还在等什么呢？
Understand the Now - Predict the Future! Time series analysis and forecasting is one of the key fields in statistical programming. It allows you to see patterns in time series datamodel this datafinally make forecasts based on those models Due to modern technology the amount of available data grows substantially from day to day. Successful companies know that. They also know that decisions based on data gained in the past, and modeled for the future, can make a huge difference. Proper understanding and training in time series analysis and forecasting will give you the power to understand and create those models. This can make you an invaluable asset for your company/institution and will boost your career! What will you learn in this course and how is it structured? You will learn about different ways in how you can handle date and time data in R. Things like time zones, leap years or different formats make calculations with dates and time especially tricky for the programmer. You will learn about POSIXt classes in R Base, the chron package and especially the lubridate package. You will learn how to visualize, clean and prepare your data. Data preparation takes a huge part of your time as an analyst. Knowing the best functions for outlier detection, missing value imputation and visualization can safe your day. After that you will learn about statistical methods used for time series. You will hear about autocorrelation, stationarity and unit root tests. Then you will see how different models work, how they are set up in R and how you can use them for forecasting and predictive analytics. Models taught are: ARIMA, exponential smoothing, seasonal decomposition and simple models acting as benchmarks. Of course all of this is accompanied with plenty of exercises. Where are those methods applied? In nearly any quantitatively working field you will see those methods applied. Especially econometrics and finance love time series analysis. For example stock data has a time component which makes this sort of data a prime target for forecasting techniques. But of course also in academia, medicine, business or marketing techniques taught in this course are applied. Is it hard to understand and learn those methods? Unfortunately learning material on Time Series Analysis Programming in R is quite technical and needs tons of prior knowledge to be understood. With this course it is the goal to make understanding modeling and forecasting as intuitive and simple as possible for you. While you need some knowledge in statistics and statistical programming, the course is meant for people without a major in a quantitative field like math or statistics. Basically anybody dealing with time data on a regular basis can benefit from this course. How do I prepare best to benefit from this course? It depends on your prior knowledge. But as a rule of thumb you should know how to handle standard tasks in R (course R Basics). What R you waiting for?
