Brochure
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- Welcome to the Course Introduction to Time Series Analysis and Forecasting in R
- Managing Expectations
- Basics of Time Series Analysis and Forecasting
- Method Selection in Forecasting
- Forecasting: Step by Step Guide
- Time Series Analysis and Forecasting Use Case: IT Store Staff Allocation
- Script for the Example
- Package Overview and the R Time Series Task View
- Datasets To Be Used
- Course Links
- Time Series Analysis Intro
Quick Facts
| particular | details | |||
|---|---|---|---|---|
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course and certificate fees
Fees information
₹ 4,099
certificate availability
Yes
certificate providing authority
Udemy
The syllabus
Introduction
Working With Dates And Time In R
- Welcome to this Section - What Is this Section About?
- Working with Different Date and Time Classes: POSIXt, Date and Chron
- Format Conversion from String to Date / Time - Function strptime
- The Lubridate Package
- Exercise: Using Lubridate on a Data Frame
- Date and Time Calculations with Lubridate
- Lubridate: Data Handling Exercise
- Section Script TD
Time Series Data Pre-Processing and Visualization
- Creating Time Series
- Exercise - Time Series Formatting
- Time Series Charts and Graphs
- Exercise: Seasonplot
- Importing Time Series Data From Excel or Other Sources
- Working with Irregular Time Series
- Working with Missing Data and Outliers
- Section Script TSPP
- Time Series Data Preparation
Statistical Background For Time Series Analysis And Forecasting
- Time Series Vectors and Lags
- Time Series Characteristics
- Basic Forecasting Models
- Model Comparison and Accuracy
- The Importance of Residuals in Time Series Analysis
- Stationarity
- Autocorrelation
- Functions acf() and pacf()
- Exercise: Forecast Comparison
- Section Script STAT
- Statistical Background
Time Series Analysis And Forecasting
- Selecting a Suitable Model - Quantitative Forecasting Models
- Seasonal Decomposition Intro
- Decomposition Demo
- Exercise: Decomposition
- Simple Moving Average
- Exponential Smoothing with ETS
- Judgmental Forecasts - Qualitative Forecasting Methods
- Section Script TSA
ARIMA Models
- What is Coming Up Next? ARIMA Models in Time Series Analysis
- Introduction to ARIMA Models
- Automated ARIMA Model Selection with auto.arima
- ARIMA Model Calculations
- Simulating Time Series Based on ARIMA
- Manual ARIMA Parameter Selection
- How to Indentify ARIMA Model Parameters
- ARIMA Forecasts
- ARIMA with Explanatory Variables - Adding a Second Variable to the Model
- Section Script ARIMA
Multivariate Time Series Analysis
- What is Coming Up Next? Multivariate Time Series Analysis in R
- Understanding Multivariate Time Series and Their Structure
- Multivariate Time Series Objects and Project Dataset
- Main R Packages for Multivariate Time Series Analysis
- Stationarity in Multivariate Time Series
- Vector Autoregressive Model Theory
- Implementing VAR Models in R
- Test for Residual Correlation and Model Diagnostics
- The Granger Test for Causality
- Forecasting a VAR Model
- Section Script
Neural Networks in Time Series Analysis
- What is Coming Up Next? Time Series Analysis Using Neural Networks
- Intro to Neural Networks for TSA
- Getting Familiar with the Dataset
- The Time Series Task View for Neural Nets - What is Available?
- Implementation of Neural Networks in R - Underlying Functions
- Practical Implementation of an Autoregressive Neural Net
- Implementing an External Regressor - Multivariate Neural Net
- Section Script
- Further Resources and Where to Go Next
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