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Elements of forecasting / Francis X. Diebold
Elements of forecasting / Francis X. Diebold
Autore DIEBOLD, Francis X.
Edizione [2. ed.]
Pubbl/distr/stampa Cincinnati : South Western, 2000
Descrizione fisica XV, 392 p. ; 24 cm
Disciplina 003.2
Soggetto topico Previsioni
ISBN 0-32402-393-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-990005516190203316
DIEBOLD, Francis X.  
Cincinnati : South Western, 2000
Materiale a stampa
Lo trovi qui: Univ. di Salerno
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Eloge de la prospective : point d'étape de travaux de prospective depuis cinquante années, en France et dans le monde / / Jacques de Courson
Eloge de la prospective : point d'étape de travaux de prospective depuis cinquante années, en France et dans le monde / / Jacques de Courson
Autore Courson Jacques de
Pubbl/distr/stampa Paris : , : L'Harmattan, , [2020]
Descrizione fisica 1 online resource (229 pages)
Disciplina 003.2
Collana Collection "Inter-national"
Soggetto topico Forecasting - History
ISBN 2-14-016713-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione fre
Record Nr. UNINA-9910794586103321
Courson Jacques de  
Paris : , : L'Harmattan, , [2020]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Eloge de la prospective : point d'étape de travaux de prospective depuis cinquante années, en France et dans le monde / / Jacques de Courson
Eloge de la prospective : point d'étape de travaux de prospective depuis cinquante années, en France et dans le monde / / Jacques de Courson
Autore Courson Jacques de
Pubbl/distr/stampa Paris : , : L'Harmattan, , [2020]
Descrizione fisica 1 online resource (229 pages)
Disciplina 003.2
Collana Collection "Inter-national"
Soggetto topico Forecasting - History
ISBN 2-14-016713-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione fre
Record Nr. UNINA-9910808129803321
Courson Jacques de  
Paris : , : L'Harmattan, , [2020]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Forecasting catastrophic events in technology, nature and medicine / / Anton Panda, Volodymyr Nahornyi
Forecasting catastrophic events in technology, nature and medicine / / Anton Panda, Volodymyr Nahornyi
Autore Panda Anton
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (XVII, 97 p. 75 illus., 51 illus. in color.)
Disciplina 003.2
Collana SpringerBriefs in Computational Intelligence
Soggetto topico Forecasting - Methodology
ISBN 3-030-65328-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction -- Analysis of current state of forecasting objects and phenomena -- Specification of problems solutions -- Application of the developed forecasting methodology in various spheres of human activity -- Conclusion -- References.
Record Nr. UNINA-9910483904603321
Panda Anton  
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Forecasting with Artificial Intelligence : Theory and Applications
Forecasting with Artificial Intelligence : Theory and Applications
Autore Hamoudia Mohsen
Edizione [1st ed.]
Pubbl/distr/stampa Cham : , : Palgrave Macmillan, , 2023
Descrizione fisica 1 online resource (441 pages)
Disciplina 003.2
Altri autori (Persone) MakridakisSpyros
SpiliotisEvangelos
Collana Palgrave Advances in the Economics of Innovation and Technology Series
ISBN 3-031-35879-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
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.
Record Nr. UNINA-9910746292703321
Hamoudia Mohsen  
Cham : , : Palgrave Macmillan, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Foresight
Foresight
Pubbl/distr/stampa Bradford, : Emerald Group Publishing, 1999-
Disciplina 003.2
Soggetto topico Forecasting - Study and teaching
Prévision - Étude et enseignement
Prévision
Soggetto genere / forma Periodicals.
Ressource Internet (Descripteur de forme)
Périodique électronique (Descripteur de forme)
ISSN 1463-6689
Formato Materiale a stampa
Livello bibliografico Periodico
Lingua di pubblicazione eng
Altri titoli varianti Foresight, the journal of future studies, strategic thinking and policy
Record Nr. UNISA-996398442703316
Bradford, : Emerald Group Publishing, 1999-
Materiale a stampa
Lo trovi qui: Univ. di Salerno
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Foresight
Foresight
Pubbl/distr/stampa Bradford, : Emerald Group Publishing, 1999-
Disciplina 003.2
Soggetto topico Forecasting - Study and teaching
Prévision - Étude et enseignement
Prévision
Soggetto genere / forma Periodicals.
Ressource Internet (Descripteur de forme)
Périodique électronique (Descripteur de forme)
ISSN 1463-6689
Formato Materiale a stampa
Livello bibliografico Periodico
Lingua di pubblicazione eng
Altri titoli varianti Foresight, the journal of future studies, strategic thinking and policy
Record Nr. UNINA-9910132892103321
Bradford, : Emerald Group Publishing, 1999-
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Il cigno nero : come l'improbabile governa la nostra vita / Nassim Nicholas Taleb ; traduzione di Elisabetta Nifosi
Il cigno nero : come l'improbabile governa la nostra vita / Nassim Nicholas Taleb ; traduzione di Elisabetta Nifosi
Autore Taleb, Nassim Nicholas
Pubbl/distr/stampa Milano, : Il saggiatore, 2014
Descrizione fisica 379 p. : ill. ; 19 cm
Disciplina 123.3
003.2
Collana Piccola cultura
Soggetto non controllato Probabilità
Caso - Teorie
Previsione e previsioni
ISBN 978-88-428-2027-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione ita
Record Nr. UNINA-9910554001403321
Taleb, Nassim Nicholas  
Milano, : Il saggiatore, 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Il futuro : previsione, pronostico e profezia / a cura di Antonio Lepschy e Manlio Pastore Stocchi
Il futuro : previsione, pronostico e profezia / a cura di Antonio Lepschy e Manlio Pastore Stocchi
Pubbl/distr/stampa Venezia : Istituto veneto di scienze, lettere ed arti, 2005
Descrizione fisica 406 p. ; 24 cm
Disciplina 003.2
Collana Varie ed atti di convegni
Soggetto non controllato Futurologia - Congressi - Venezia - 2000
ISBN 88-88143-39-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione ita
Record Nr. UNINA-990008344460403321
Venezia : Istituto veneto di scienze, lettere ed arti, 2005
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Intelligent Fashion Forecasting Systems: Models and Applications / / edited by Tsan-Ming Choi, Chi-Leung Hui, Yong Yu
Intelligent Fashion Forecasting Systems: Models and Applications / / edited by Tsan-Ming Choi, Chi-Leung Hui, Yong Yu
Edizione [1st ed. 2014.]
Pubbl/distr/stampa Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2014
Descrizione fisica 1 online resource (199 p.)
Disciplina 003.2
Soggetto topico Information technology
Business—Data processing
Application software
Production management
IT in Business
Information Systems Applications (incl. Internet)
Operations Management
ISBN 3-642-39869-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Part I: Introduction, review and exploratory studies -- 1.1 Introduction: Intelligent Fashion Forecasting -- 1.2  Sales forecasting in Apparel and Fashion Industry: a review -- Collaborative Planning Forecasting Replenishment Schemes in Apparel Supply Chain Systems: Cases and Research Opportunities -- Part II: Theoretical modeling research -- 2.1  Measuring Forecasting Accuracy: Problems and Recommendations (by the example of SKU-level judgmental adjustments) -- 2.2 Forecasting Demand for Fashion Goods: A Hierarchical Bayesian Approach -- Forecasting Fashion Store Reservations: Booking Horizon Forecasting with Dynamic Updating -- Part III: Intelligent fashion forecasting: applications and analysis -- 3.1 Fuzzy Forecast Combining for Apparel Demand Forecasting -- 3.2 Intelligent Fashion Colour Trend Forecasting Schemes: A Comparative Study -- 3.3 Neural Networks Based for Romanian Clothing Sector.      .
Record Nr. UNINA-9910298535203321
Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
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