• Rob J. Hyndman
  • Anne B. Koehler
  • J. Keith Ord
  • Ralph D. Snyder
Forecasting with Exponential Smoothing
Springer Logo
The State Space Approach

Table of contents

Part I: Introduction

  1. Basic concepts
  2. Getting started (sample chapter available)

Part II: Essentials

  1. Linear innovations state space models
  2. Non-linear and heteroscedastic innovations state space models
  3. Estimation of innovations state space models
  4. Prediction distributions and intervals
  5. Selection of models

Part III: Further topics

  1. Normalizing seasonal components
  2. Models with regressor variables
  3. Some properties of linear models
  4. Reduced forms and relationships with ARIMA models
  5. Linear innovations state space models with random seed states
  6. Conventional state space models
  7. Time series with multiple seasonal patterns (with Phillip Gould)
  8. Non-linear models for positive data (with Muhammad Akram)
  9. Models for count data
  10. Vector exponential smoothing (with Ashton de Silva)

Part IV: Applications

  1. Inventory control application
  2. Conditional heteroscedasticity and applications in finance
  3. Economic applications: the Beveridge-Nelson decomposition (with Chin Nam Low and
    Heather Anderson)