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Hamoudia / Spiliotis / Makridakis

Forecasting with Artificial Intelligence

Theory and Applications

Medium: Buch
ISBN: 978-3-031-35878-4
Verlag: Springer Nature Switzerland
Erscheinungstermin: 21.09.2023
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This book is a comprehensive guide that explores the intersection of artificial intelligence and forecasting, providing the latest insights and trends in this rapidly evolving field.

The book contains fourteen chapters covering a wide range of topics, including the concept of AI, its impact on economic decision-making, traditional and machine learning-based forecasting methods, challenges in demand forecasting, global forecasting models, meta-learning and feature-based forecasting, ensembling, deep learning, scalability in industrial and optimization applications, and forecasting performance evaluation. With key illustrations, state-of-the-art implementations, best practices, and notable advances, this book offers practical insights into the theory and practice of AI-based forecasting. This book is a valuable resource for anyone involved in forecasting, including forecasters, statisticians, data scientists, business analysts, or decision-makers.

Produkteigenschaften

  • Artikelnummer: 9783031358784
  • Medium: Buch
  • ISBN: 978-3-031-35878-4
  • Verlag: Springer Nature Switzerland
  • Erscheinungstermin: 21.09.2023
  • Sprache(n): Englisch
  • Auflage: 2023
  • Serie: Palgrave Advances in the Economics of Innovation and Technology
  • Produktform: Gebunden
  • Gewicht: 698 g
  • Seiten: 412
  • Format (B x H x T): 153 x 216 x 29 mm
  • Ausgabetyp: Kein, Unbekannt

Themen

Autoren/Hrsg.

Herausgeber

Part I. Artificial intelligence : present and future.- 1. Human intelligence (HI) versus artificial intelligence (AI) and intelligence augmentation (IA).- 2. Expecting the future: How AI's potential performance will shape current behavior.- Part II. The status of machine learning methods for time series and new products forecasting.- 3. Forecasting with statistical, machine learning, and deep learning models: Past, present and future.- 4. Machine Learning for New Product Forecasting.- Part III. Global forecasting models.- 5. Forecasting in Big Data with Global Forecasting Models.- 6. How to leverage data for Time Series Forecasting with Artificial Intelligence models: Illustrations and Guidelines for Cross-learning.- 7. Handling Concept Drift in Global Time Series Forecasting.- 8. Neural network ensembles for univariate time series forecasting.- Part IV. Meta-learning and feature-based forecasting.- 9. Large scale time series forecasting with meta-learning.- 10. Forecasting large collections of time series: feature-based methods.- Part V. Special applications.- 11. Deep Learning based Forecasting: a case study from the online fashion industry.- 12. The intersection of machine learning with forecasting and optimisation: theory and applications.- 13. Enhanced forecasting with LSTVAR-ANN hybrid model: application in monetary policy and inflation forecasting.- 14. The FVA framework for evaluating forecasting performance.