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    Introduction to Time Series Analysis and Forecasting in R

    BY
    Udemy

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    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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