Forecasting With The Theta Method

Theory and Applications
 
 
Standards Information Network (Verlag)
  • 1. Auflage
  • |
  • erschienen am 28. Dezember 2018
  • |
  • 200 Seiten
 
E-Book | PDF mit Adobe-DRM | Systemvoraussetzungen
978-1-119-32072-2 (ISBN)
 
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 competition in the world in 2000: the M3 competition. Written by two of the leading experts in the forecasting field, it illuminates the exact replication of the method and under what conditions the method outperforms other forecasting methods. Recent developments such as multivariate models are also included, as are a series of practical applications in finance, economics, and healthcare. The book also offers practical tools in MS Excel and guidance, as well as provisional access, for the use of R source code and respective packages. Forecasting with the Theta Method: Theory and Applications includes three main parts. The first part, titled Theory, Methods, Models & Applications details the new theory about the method. The second part, Applications & Performance in Forecasting Competitions, describes empirical results and simulations on the method. The last part roadmaps future research and also include contributions from another leading scholar of the method - Dr. Fotios Petropoulos. * First ever book to be published on the Theta Method * Explores new theory and exact conditions under which methods would outperform most forecasting benchmarks * Clearly written with practical applications * Employs R - open source code with all included implementations Forecasting with the Theta Method: Theory and Applications is a valuable tool for both academics and practitioners involved in forecasting and respective software development.
1. Auflage
  • Englisch
  • Newark
  • |
  • Großbritannien
John Wiley & Sons Inc
  • Für Beruf und Forschung
  • 2,01 MB
978-1-119-32072-2 (9781119320722)
weitere Ausgaben werden ermittelt
KOSTAS I. NIKOLOPOULOS is Associate Dean (Research) & Chair in Business Analytics College of Business, Law, Education and Social Sciences, Prifysgol Bangor University. He is also the Director of the forLAB forecasting laboratory (www.forlab.eu) and has several years of experience in the UK and Greece as Consultant and Software Engineer in projects with Siemens Healthcare Diagnostics, DoH, RevOil. and the Treasury and Home Office (in Greece).

DIMITRIOS D. THOMAKOS is Head of Department & Chair in Applied Econometrics, School of Economics, Management and Informatics, University of Peloponnese, Greece, and Senior Fellow, Member of the Scientific Committee, Rimini Centre for Economic Analysis, Italy. He holds a PhD from Columbia University and has been advising many financial institutions on financial predictive analytics.
  • 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 ?-method Applied to the Unit Root with Drift DGP
  • 2.2.1 Main Results
  • 2.2.2 Alternative Trend Functions and the Original ?-line Approach
  • 2.2.3 Implementing the ?-method under the Unit Root DGP
  • 2.3 The ?-method Applied to the Trend-stationary DGP
  • 2.3.1 Implementing the ?-method under the Trend-stationary DGP
  • 2.3.2 Is the AR(1)-forecast a ?-forecast?
  • Chapter 3 The Multivariate ?-method
  • 3.1 The Bivariate ?-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 ?-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 ?-method
  • Chapter 6 ?-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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