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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.
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| 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 |
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Courson Jacques de
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| 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 |
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Courson Jacques de
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| 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 |
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Panda Anton
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| 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 |
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Hamoudia Mohsen
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| 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 |
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Taleb, Nassim Nicholas
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| 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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