Forecasting with Artificial Intelligence : Theory and Applications

• Autore: Hamoudia Mohsen
• Pubblicazione: Cham : , : Palgrave Macmillan, , 2023; ©2023
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (441 pages)
• Disciplina: 003.2
• Altri autori: MakridakisSpyros SpiliotisEvangelos
• Nota di contenuto: Intro -- Foreword -- Preface -- Acknowledgments -- Contents -- Editors and Contributors -- About the Editors -- Contributors -- Abbreviations -- List of Figures -- List of Tables -- Part I Artificial Intelligence: Present and Future -- 1 Human Intelligence (HI) Versus Artificial Intelligence (AI) and Intelligence Augmentation (IA) -- Introduction -- Defining Intelligence -- The Distinctive Nature of AI and HI -- The Evolution of HI and Its Complementarity with AI -- AI: Current Capabilities and Future Challenges -- The Future of AI: Uncertainties and Challenges -- AVs: Achievements, Limitations, and Future Prospects -- The Current Situation -- The Four AI/HI Scenarios -- Smarter, Faster Hedgehogs -- An Army of Specialized Hedgehogs -- Fully Integrated HI and AI -- More Efficient Ways of Communicating with Computers -- Direct Brain to Computer Interface (BCI) -- Non-Invasive Forms of Interfaces -- Invasive Forms of Interfaces -- Brain-To-Brain Interfaces (BBI) -- The Future World of Transhumanism -- Runaway AGI -- Conclusions -- References -- 2 Expecting the Future: How AI's Potential Performance Will Shape Current Behavior -- Introduction -- The Black Box of Machine Learning -- Decreased Efforts -- Educational Choices -- Political Job Protection -- Greater Desire of Governments to Borrow -- Economies of Scale -- Trade Blocks -- What Happens If People Expect Money to Become Worthless -- Animal Welfare -- Conclusion -- References -- Part II The Status of Machine Learning Methods for Time Series and New Product Forecasting -- 3 Time Series Forecasting with Statistical, Machine Learning, and Deep Learning Methods: Past, Present, and Future -- Introduction -- Statistical Methods -- Machine Learning Methods -- Deep Learning Methods -- Discussion and Future Steps -- References -- 4 Machine Learning for New Product Forecasting -- Introduction. ; Categories of New Products and Data Availability -- Data Availability -- There is Data on Similar Products that Have Been Adopted in the Past -- The Product is Truly New -- Highlights of the Main Forecasting Techniques Used for New Products -- "Traditional" Methods -- Expert Opinion -- The Delphi Method -- Consensus of Sales Team -- Customers Surveys -- Drivers and Constraints -- Diffusion Models (S-Shaped Curves) -- Machine Learning Methods for Predicting New Product Demand -- Gradient Boosted Trees (GBT) -- Artificial Neural Network (ANN) -- Structuring the New Product Forecasting Problem -- Potential Machine Learning Approaches for Truly New-to-the-World Products -- A Review of Four Case Studies of Machine Learning for New Product Forecasting -- Forecasting Demand Profiles of New Products -- Description -- Test Setup -- Results -- Feature Importance and Comparable Products -- Summary and Conclusion -- Fashion Retail: Forecasting Demand for New Items -- Methodology -- Experiment and Results -- Conclusion and Future Directions -- New Product Forecasting Using Deep Learning - A Unique Way -- Methodology and Results -- Summary and Conclusion -- Forecasting New Product Demand Using Machine Learning -- Methods -- Results -- Summary and Conclusion -- Summary and Lessons from the Four Case Studies -- Summary and Conclusions -- References -- Part III Global Forecasting Models -- 5 Forecasting with Big Data Using Global Forecasting Models -- Big Data in Time Series Forecasting -- Global Forecasting Models -- A Brief History of Global Models -- Model Complexity in Global Models -- Global Forecasting Model Frameworks -- Recent Advances in Global Models -- Conclusions and Future Work -- References -- 6 How to Leverage Data for Time Series Forecasting with Artificial Intelligence Models: Illustrations and Guidelines for Cross-Learning -- Introduction. ; Background and Definitions -- Common Terminology and Building Blocks -- Locality and Globality -- Approximation Capacity of Cross-Learned Models -- Statistical Trade-Offs -- Illustration of Cross-Learning Approximation of Specific Time Series Processes -- Well-Known Families of Time Series Processes Have an Equivalent Parameterization via Local Autoregression -- Two Different Linear Autoregressive Predictive Functions Can Be Represented by a Single Linear Autoregressive with More Lags -- A Single Nonlinear Autoregression Can Represent an Arbitrary Number of Different Linear Autoregressions -- Cross-Learning for Processes with Noise or Perturbations -- Explicit Example of Transfer Learning -- Conclusions -- References -- 7 Handling Concept Drift in Global Time Series Forecasting -- Introduction -- Problem Statement: Concept Drift Types -- Related Work -- Global Time Series Forecasting -- Transfer Learning -- Concept Drift Handling -- Methodology -- Series Weighted Methods -- Our Proposed Concept Drift Handling Methods -- Error Contribution Weighting -- Gradient Descent Weighting -- Experimental Framework -- Datasets -- Error Metrics -- Evaluation -- Benchmarks -- Statistical Testing of the Results -- Results and Discussion -- Main Accuracy Results -- Statistical Testing Results -- Further Insights -- Conclusions and Future Research -- References -- 8 Neural Network Ensembles for Univariate Time Series Forecasting -- Introduction -- Experimental Design -- Initialization Seed -- Loss Function -- Input Layer Size -- Empirical Evaluation -- Time Series Data -- Forecasting Performance Measures -- Base Forecasting Neural Network -- Results and Discussion -- Initialization Seed -- Loss Function -- Input Layer Size -- Final Ensemble -- Conclusions -- References -- Part IV Meta-Learning and Feature-Based Forecasting. ; 9 Large-Scale Time Series Forecasting with Meta-Learning -- Introduction -- A Brief Review of the Meta-Learning in Time Series Forecasting -- Key Choices in Designing an Effective Meta-Learning Framework -- Selection of Base Forecasters -- Feature Extracting -- Meta-Learners -- A Python Library for Time Series Forecasting with Meta-Learning -- Raw Data Class -- Metadata Class -- Meta-Learner -- The Meta-Combination Learner with Encapsulated Features Extractor ('MetaComb') -- The Meta-Combination Learner with Unsupervised Hand-Selected Features ('MetaCombFeat') -- The Model Selection Learner Targets Identifying the Best Base Forecaster ('MetaSelection') -- The Metaloss Prediction Learner ('MetaLoss') -- Experimental Evaluation -- Datasets -- Predictive Performance -- Conclusions and Future Directions -- References -- 10 Forecasting Large Collections of Time Series: Feature-Based Methods -- Introduction -- Data Generation -- Diverse Time Series Generation -- Time Series Generation with Target Features -- Feature Extraction -- Time Series Features -- Automation of Feature Extraction -- Time Series Imaging -- Forecast Diversity -- Automatic Feature Selection -- Forecast Trimming for Combination -- Accuracy, Robustness, and Diversity -- Forecast Trimming -- Some Practical Forecasting Issues -- Intermittent Demand -- Uncertainty Estimation -- Conclusion -- References -- Part V Special Applications -- 11 Deep Learning Based Forecasting: A Case Study from the Online Fashion Industry -- Introduction -- Data for Forecasting at Zalando: An Overview -- Sales to Demand Translation -- Description of Covariates -- Demand Forecast Model -- Problem Formalization -- Model Architecture -- Input Preparation -- Encoder -- Positional Encoding -- Padding and Masking -- Decoder -- Monotonic Demand Layer -- Near and Far Future Forecasts -- Training -- Prediction. ; Empirical Results -- Accuracy Metrics -- Model Benchmarking -- On the Benefits of Transformer-based Forecasting: First Results -- Related Work -- Practical Considerations -- Conclusion -- References -- 12 The Intersection of Machine Learning with Forecasting and Optimisation: Theory and Applications -- Introduction -- Predict and Optimise -- Problem Setting -- Methodologies and Applications -- Discussion and Future Works -- Predicting the Solutions of Optimisation Problems -- Problem Setting -- Methodologies and Applications -- Discussion and Future Works -- Conclusion -- References -- 13 Enhanced Forecasting with LSTVAR-ANN Hybrid Model: Application in Monetary Policy and Inflation Forecasting -- Introduction -- LSTVAR-ANN Hybrid Model -- LSTVAR Model -- LSTAR-ANN Hybrid Model -- Research Architecture Specification -- 3D Impulse Response Function -- Monetary Policy Model with Regimes -- Data -- Results -- Unobserved Factors Extraction -- Importance on CPI Prediction -- Transition Function Output -- Response of CPI on Interest Rate Shock -- Forecasting -- Pre-COVID-19 Period -- COVID-19 and Post-COVID-19 Period -- Conclusion -- Appendix -- References -- 14 The FVA Framework for Evaluating Forecasting Performance -- What is FVA? -- Why Use FVA? -- Applying the Scientific Method to Forecasting -- FVA Analysis: Step-by-Step -- Mapping the Process -- Collecting the Data -- Analyzing the Process -- Reporting the Results -- Interpreting the Results -- FVA Challenges and Extensions -- Compete or Combine? -- Stochastic Value Added (SVA): FVA for Probabilistic Forecasts -- Summary -- References -- Author Index -- Subject Index.
• Titolo autorizzato: Forecasting with Artificial Intelligence
• ISBN: 3-031-35879-1
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910746292703321
• Lo trovi qui: Univ. Federico II
• Serie: Palgrave Advances in the Economics of Innovation and Technology Series