Applied Time Series Analysis and Forecasting with R

Description

In this course, you will :

• Learn how to create time series models with trend or seasonality.
• Investigate models such as ARIMA, exponential smoothing, and neural networks in greater depth.
• Learn how to use dygraphs to interactively visualise time series.
• When you complete this course, you will be able to apply standard time series models to a univariate time series.

Syllabus :

1. Using R for Time Series Analysis

•  Course Roadmap 
•  Common Functions for Time Series Analysis

 

2. Modeling Unemployment Rates

•  Working with Trending Data 
•  The Project Dataset 
•  Exponential Smoothing for Trending Data 
•  Holt Trend Model with Damping Parameter 
•  ARIMA for Trending Data 
•  Time Series Model Comparison Plots 

 

3. Forecasting Inflation Rates

•  Working with Seasonality 
•  The Project Dataset 
•  Data Import 
•  Seasonal Decomposition 
•  ARIMA for Seasonal Data 
•  Exponential Smoothing for Seasonal Data 

 

4. Predicting Sales Using Neural Networks

•  The Project Dataset 
•  Getting the Data Ready 
•  Neural Networks for Time Series 
•  Interactive Charts with Dygraphs 

Show more