Modern Time Series Forecasting with Python Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas
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Sprache:Englisch
41,99 €
inkl. gesetzl. MwSt.Beschreibung
Produktdetails
Format
ePUB 3
Kopierschutz
Nein
Family Sharing
Ja
Text-to-Speech
Ja
Erscheinungsdatum
31.10.2024
Verlag
Packt PublishingSeitenzahl
660 (Printausgabe)
Dateigröße
49846 KB
Sprache
Englisch
EAN
9781835883198
Learn traditional and cutting-edge machine learning (ML) and deep learning techniques and best practices for time series forecasting, including global forecasting models, conformal prediction, and transformer architectures Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*
Key Features- Apply ML and global models to improve forecasting accuracy through practical examples
- Enhance your time series toolkit by using deep learning models, including RNNs, transformers, and N-BEATS
- Learn probabilistic forecasting with conformal prediction, Monte Carlo dropout, and quantile regressions
- Build machine learning models for regression-based time series forecasting
- Apply powerful feature engineering techniques to enhance prediction accuracy
- Tackle common challenges like non-stationarity and seasonality
- Combine multiple forecasts using ensembling and stacking for superior results
- Explore cutting-edge advancements in probabilistic forecasting and handle intermittent or sparse time series
- Evaluate and validate your forecasts using best practices and statistical metrics
This book is ideal for data scientists, financial analysts, quantitative analysts, machine learning engineers, and researchers who need to model time-dependent data across industries, such as finance, energy, meteorology, risk analysis, and retail. Whether you are a professional looking to apply cutting-edge models to real-world problems or a student aiming to build a strong foundation in time series analysis and forecasting, this book will provide the tools and techniques you need. Familiarity with Python and basic machine learning concepts is recommended.
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