Introduction to Time Series Analysis and Forecasting in R

BY
Udemy

Mode

Online

Fees

₹ 4099

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study
Mode of Delivery Video and Text Based

Course and certificate fees

Fees information
₹ 4,099
certificate availability

Yes

certificate providing authority

Udemy

The syllabus

Introduction

  • 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

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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