Forecasting with Artificial Intelligence Theory and Applications /
| Corporate Author: | |
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| Other Authors: | , , |
| Summary: | XLIV, 412 p. 48 illus., 38 illus. in color. text |
| Language: | English |
| Published: |
Cham :
Springer Nature Switzerland : Imprint: Palgrave Macmillan,
2023.
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| Edition: | 1st ed. 2023. |
| Series: | Palgrave Advances in the Economics of Innovation and Technology,
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| Subjects: | |
| Online Access: | https://doi.org/10.1007/978-3-031-35879-1 |
| Format: | Electronic Book |
Table of Contents:
- 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. .