Modern Time Series Forecasting With Python Second Edition 2nd Edition Manu Joseph by Manu Joseph, Jeffrey Tackes instant download after payment.

Predict the future with confidence with this practical guide on building and deploying powerful time series forecasting models. New content on transformers and probabilistic modeling makes it a must-read for tackling complex forecasting challenges.
 
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Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas
 
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
 
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