Practical Time Series Forecasting with R A Hands On Guide 2nd Edition by Galit Shmueli, Kenneth Lichtendahl ISBN 0997847913 9780997847915

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ISBN 10:      0997847913
ISBN 13: 978-0997847918
Author:       Galit Shmueli,  Kenneth C. Lichtendahl

Practical Time Series Forecasting with R A Hands On Guide 2nd Table of contents:

Part I: Introduction to Time Series Forecasting

1. Introduction

   • Overview of Time Series Forecasting
   • Forecasting Process
   • Why Time Series Forecasting?
   • Forecasting Methods: A Brief Overview

2. Basic Time Series Concepts

   • Time Series Components: Trend, Seasonality, and Noise
   • Understanding Stationarity
   • Exploratory Data Analysis of Time Series
   • Visualization of Time Series Data

3. R Basics for Time Series Analysis

   • Introduction to R and RStudio
   • Working with R for Time Series Data
   • Time Series Objects in R
   • Handling and Manipulating Time Series Data in R

Part II: Forecasting Methods

4. Smoothing Methods

   • Moving Averages
   • Exponential Smoothing: Simple, Holt’s, and Holt-Winters Methods
   • Smoothing and Detrending
   • Forecasting with Smoothing Methods

5. Autoregressive Integrated Moving Average (ARIMA) Models

   • Introduction to ARIMA Models
   • Stationarity and Differencing
   • ARIMA Model Identification
   • Estimating ARIMA Parameters
   • Diagnostic Checking of ARIMA Models
   • Forecasting with ARIMA Models

6. Seasonal ARIMA (SARIMA) Models

   • The Need for SARIMA
   • Seasonal Decomposition of Time Series
   • SARIMA Model Identification
   • Estimating Seasonal ARIMA Parameters
   • Forecasting with SARIMA

Part III: Advanced Forecasting Topics

7. Exponential Smoothing State Space Models (ETS)

   • Introduction to ETS Models
   • Model Components: Error, Trend, and Seasonality
   • Estimation and Forecasting with ETS Models
   • Comparing ETS and ARIMA Models

8. Forecasting with Structural Models

   • Forecasting with Structural Models and Decomposition
   • Intervention Analysis
   • Time Series Regression Models

9. Model Evaluation and Comparison

   • Forecast Accuracy and Performance Metrics
   • Cross-Validation for Time Series Forecasting
   • Comparing Models Using Forecast Accuracy Measures
   • Selecting the Best Forecasting Model

Part IV: Real-World Applications

10. Case Studies in Time Series Forecasting

    • Forecasting Sales Data
    • Forecasting Financial Data
    • Forecasting Demand and Inventory
    • Forecasting with Missing Data

11. Forecasting for Business and Decision Making

    • Time Series Forecasting in Business Context
    • Forecasting in Marketing, Economics, and Supply Chain
    • Practical Considerations for Forecasting in Business Environments

Part V: Advanced Topics and Tools

12. Advanced Forecasting Models

    • Vector Autoregressive (VAR) Models
    • GARCH Models for Financial Data
    • Forecasting with Neural Networks
    • Long-Term Forecasting and Trend Extrapolation

13. Using R for Time Series Forecasting

    • R Packages for Time Series Forecasting
    • Visualizing Time Series Forecasts
    • Writing Custom Forecasting Functions in R
    • Using Forecasting Libraries: forecast, tseries, and More

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