Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Subscribe
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
Applied Time Series Analysis and Forecasting with R Projects
Rating: 4.6 out of 5(843 ratings)
5,602 students

Applied Time Series Analysis and Forecasting with R Projects

Use R to work on real world time series analysis and forecasting examples. Applied data science with R.
Created byR-Tutorials Training
Last updated 7/2018
English

What you'll learn

  • Perform standard time series analysis tasks
  • Get ARIMA and exponential smoothing models in R
  • Do forecasting in R
  • Work with irregularly spaced time series
  • Model time series with trend and seasonality
  • Scrape stock data from yahoo finance
  • Import different types of time series data
  • Use automatic model selection in R
  • Select the best packages for time series analysis in R

Course content

5 sections • 34 lectures • 3h 21m total length

  • Introduction2:51
  • Managing Expectations5:35

    Explore applied time series analysis and forecasting with R through three projects on trend, seasonality, and irregular data, using real-world finance and labor data, with model comparison and visualization.

  • Main R Functions in Time Series Analysis7:37
  • Supporting Resources5:35

    Explore practical R time series resources—from the R Time Series Task View and CRAN packages like forecast to tutorials and books—enabling applied, daily work with univariate and multivariate analysis.

  • Course Link List0:07

Requirements

  • You need R/RStudio on your computer (add on packages will be outlined)
  • Basic R skills are required
  • Basic statistics skills would be helpful

Description

Welcome to the world of R and Time Series Analysis!

At the moment R is the leading open source software for time series analysis and forecasting. No other tool, not even python, comes close to the functions and features available in R. Things like exponential smoothing, ARIMA models, time series cross validation, missing data handling, visualizations and forecasts are easily accessible in R and its add on packages. Therefore, R is the right choice for time series analysis and this course gives you an opportunity to train and practice it.

So how is the course structured?

This is a hands on course with 3 distinct projects to solve! Each project has a main topic and a secondary topic. Both are discussed on real world data. In the first project you work with trending data, and as a secondary topic you will learn how to create standard and ggplot2 time series visualizations. The dataset for that project will be an employment rate dataset.

The second project with the German monthly inflation rates over the last 10 years shows how to model seasonal datasets. And you will also compare the models with time series cross validation.

In the third project you will connect R to yahoo finance and scrape stock data. The resulting data requires loads of pre-processing and cleaning including missing data imputation. Once we prepared the data, we will check out which weekday is the best for buying and selling the Novartis stock.

You should know some R to be able to follow along. There is for example the introduction to time series analysis and forecasting course. That course is more a step by step guide while this one is an applied and project based one. Both courses can be taken on their own, or you take a look at both and learn the subject from 2 different angles.

As always you will get the course script as a text file. Of course you get all the standard Udemy benefits like 30 days money back guarantee, lifetime access, instructor support and a certificate for your CV.

Who this course is for:

  • Data scientists, economists and all sorts of professionals working with time series datasets
  • Entrepreneurs and marketing experts interested in finding patterns in time series data
  • Students required to perform time series analysis

Report abuse