Applied Time Series Analysis and Forecasting with R Projects
4.4 (401 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
2,874 students enrolled

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.
4.4 (401 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
2,874 students enrolled
Last updated 7/2018
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Current price: $65.99 Original price: $94.99 Discount: 31% off
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This course includes
  • 3.5 hours on-demand video
  • 5 articles
  • 2 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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
Expand all 34 lectures 03:21:51
+ Project I Trending Data: Singapur Labor Force Participation Rate
8 lectures 47:06
Importing The Data
07:02
Mission Statement
03:43
Project I Script
00:52
The Exponential Smoothing Family of Models
03:17
The Holt Linear Trend Model
09:15
The ARIMA Model
09:24
Comparison Plot with 'Ggplot2'
08:26
Bonus Exercise: In-Sample Forecasts vs Actual Data
05:07
+ Project II Seasonal Data: Monthly Inflation Rates of Germany
8 lectures 48:55
Getting Familiar with The Data
04:47
Importing The Data
05:55
Mission Statement
04:01
Project II Script
00:30
Seasonal Decomposition
11:20
Seasonal ARIMA
07:28
Exponential Smoothing with ETS
07:23
Time Series Cross Validation
07:31
+ Project III Irregularly Spaced Data: Analyze A Biotech Stock
6 lectures 44:01
Mission Statement
04:43
Scraping the Data From Yahoo Finance
08:39
Exploring the Data
09:32
Project III Script
01:00
Getting a Regular Time Series
12:42
Visually Analyzing the Data
07:25
+ Project IV Neural Networks: Neural Nets and Interactive Graphs
7 lectures 40:03
Mission Statement
04:43
Getting Familiar with the Dataset
02:48
Project IV Script
00:38
Cleaning with tidyr
09:04
Fitting the Neural Net Model
06:10
Interactive Graph with Dygraphs
11:55
Course Summary and Further Options
04:45
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