Forecasting with the Theta method : theory and applications

Forecasting with the Theta method theory and applications

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The first book to be published on the Theta method, outlining under what conditions the method outperforms other forecasting methods This book is the first to detail the Theta method of forecasting – one of the most difficult-to-beat forecasting benchmarks, which topped the biggest forecasting compe...

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Detalles Bibliográficos
Otros Autores: Nikolopoulos, Kostas I., author (author), Thomakos, Dimitrios D., author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Hoboken, NJ : Wiley 2019.
Edición:1st edition
Colección:THEi Wiley ebooks.
Materias:
Business forecasting.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630370206719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright
  • Contents
  • Author Biography
  • Preface
  • Part I Theory, Methods and Models
  • Chapter 1 The -legacy
  • 1.1 The Origins…
  • 1.1.1 The Quest for Causality
  • 1.2 The Original Concept: THETA as in THErmosTAt
  • 1.2.1 Background: A Decomposition Approach to Forecasting
  • 1.2.2 The Original Basic Model of the Theta Method
  • 1.2.3 How to Build and Forecast with the Basic Model
  • 1.2.4 SES with Drift
  • 1.2.5 The Exact Setup in the M3 Competition
  • 1.2.6 Implementing the Basic Version in Microsoft Excel
  • 1.2.7 The FutuRe is Written in R
  • 1.A Appendix
  • Chapter 2 From the From the -method to a -model
  • 2.1 Stochastic and Deterministic Trends and their DGPs
  • 2.2 The θ‐method Applied to the Unit Root with Drift DGP
  • 2.2.1 Main Results
  • 2.2.2 Alternative Trend Functions and the Original θ‐line Approach
  • 2.2.3 Implementing the θ‐method under the Unit Root DGP
  • 2.3 The θ‐method Applied to the Trend‐stationary DGP
  • 2.3.1 Implementing the θ‐method under the Trend‐stationary DGP
  • 2.3.2 Is the AR(1)‐forecast a θ‐forecast?
  • Chapter 3 The Multivariate θ‐method
  • 3.1 The Bivariate θ‐method for the Unit Root DGP
  • 3.2 Selection of Trend Function and Extensions
  • Part II Applications and Performance in Forecasting Competitions
  • Chapter 4 Empirical Applications with the θ‐method
  • 4.1 Setting up the Analysis
  • 4.1.1 Sample Use, Evaluation Metrics, and Models/Methods Used
  • 4.1.2 Data
  • 4.2 Series CREDIT
  • 4.3 Series UNRATE
  • 4.4 Series EXPIMP
  • 4.5 Series TRADE
  • 4.6 Series JOBS
  • 4.7 Series FINANCE
  • 4.8 Summary of Empirical Findings
  • Chapter 5 Applications in Health Care
  • 5.1 Forecasting the Number of Dispensed Units of Branded and Generic Pharmaceuticals
  • 5.2 The Data
  • 5.2.1 Prescribed vs. Dispensed
  • 5.2.2 The Dataset
  • 5.3 Results for Branded
  • 5.4 Results for Generic.
  • Part III The Future of the θ‐method
  • Chapter 6 θ‐Reflections from the Next Generation of Forecasters
  • 6.1 Design
  • 6.2 Seasonal Adjustment
  • 6.3 Optimizing the Theta Lines
  • 6.4 Adding a Third Theta Line
  • 6.5 Adding a Short‐term Linear Trend Line
  • 6.6 Extrapolating Theta Lines
  • 6.7 Combination Weights
  • 6.8 A Robust Theta Method
  • 6.9 Applying Theta Method in R Statistical Software
  • Chapter 7 Conclusions and the Way Forward
  • References
  • Index
  • EULA.

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