Forecasting With The Theta Method

Theory and Applications

Inbunden, Engelska, 2019

AvKostas I. Nikolopoulos,Dimitrios D. Thomakos,Kostas I. (Decision Sciences and Ops Management) Nikolopoulos,Kostas I Nikolopoulos,Dimitrios D Thomakos

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The first book to be published on the Theta method, outlining under what conditions the method outperforms other forecasting methodsThis 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 MethodExplores new theory and exact conditions under which methods would outperform most forecasting benchmarksClearly written with practical applicationsEmploys R – open source code with all included implementationsForecasting with the Theta Method: Theory and Applications is a valuable tool for both academics and practitioners involved in forecasting and respective software development.

Produktinformation

Utgivningsdatum2019-03-15
Mått152 x 231 x 15 mm
Vikt386 g
FormatInbunden
SpråkEngelska
Antal sidor200
FörlagJohn Wiley & Sons Inc
ISBN9781119320760

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KOSTAS I. NIKOLOPOULOS is Associate Dean (Research) & Chair in Business AnalyticsCollege 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.

Author Biography ixPreface xiPart I Theory, Methods and Models 11 The 𝚯-legacy 31.1 The Origins… 31.1.1 The Quest for Causality 51.2 The Original Concept: THETA as in THErmosTAt 61.2.1 Background: A Decomposition Approach to Forecasting 71.2.2 The Original Basic Model of the Theta Method 71.2.3 How to Build and Forecast with the Basic Model 101.2.4 SES with Drift 111.2.5 The Exact Setup in the M3 Competition 121.2.6 Implementing the Basic Version in Microsoft Excel 151.2.7 The FutuRe is Written in R 181.A Appendix 182 From the 𝜽-method to a 𝜽-model 372.1 Stochastic and Deterministic Trends and their DGPs 372.2 The 𝜃-method Applied to the Unit Root with Drift DGP 382.2.1 Main Results 382.2.2 Alternative Trend Functions and the Original 𝜃-line Approach 422.2.3 Implementing the 𝜃-method under the Unit Root DGP 452.3 The 𝜃-method Applied to the Trend-stationary DGP 452.3.1 Implementing the 𝜃-method under the Trend-stationary DGP 492.3.2 Is the AR(1)-forecast a 𝜃-forecast? 503 The Multivariate 𝜽-method 513.1 The Bivariate 𝜃-method for the Unit Root DGP 513.2 Selection of Trend Function and Extensions 54Part II Applications and Performance in Forecasting Competitions 594 Empirical Applications with the 𝜽-method 614.1 Setting up the Analysis 614.1.1 Sample Use, Evaluation Metrics, and Models/Methods Used 614.1.2 Data 644.2 Series Credit 654.3 Series Unrate 734.4 Series Expimp 794.5 Series Trade 854.6 Series Jobs 914.7 Series Finance 1034.8 Summary of Empirical Findings 1155 Applications in Health Care 1175.1 Forecasting the Number of Dispensed Units of Branded and Generic Pharmaceuticals 1175.2 The Data 1185.2.1 Prescribed vs. Dispensed 1195.2.2 The Dataset 1205.3 Results for Branded 1215.4 Results for Generic 1425.A Appendix 152Part III The Future of the 𝜽-method 1616 𝜽-Reflections from the Next Generation of Forecasters 163Fotios Petropoulos6.1 Design 1646.2 Seasonal Adjustment 1646.3 Optimizing the Theta Lines 1666.4 Adding a Third Theta Line 1686.5 Adding a Short-term Linear Trend Line 1696.6 Extrapolating Theta Lines 1706.7 Combination Weights 1726.8 A Robust Theta Method 1746.9 Applying Theta Method in R Statistical Software 1747 Conclusions and the Way Forward 177References 181Index 185