Modern Time Series Forecasting With Python Industryready Machine Learning And Deep Learning Time Series Analysis With Pytorch Manu Tackes Joseph Jeffrey by Manu. Tackes Joseph (jeffrey.) 9781835883181, 1835883184 instant download after payment.

Starting with the basics, this data science book introduces fundamental time series concepts, such as ARIMA and exponential smoothing, before gradually progressing to advanced topics, such as machine learning for time series, deep neural networks, and transformers. As part of your fundamentals training, you’ll learn preprocessing, feature engineering, and model evaluation. As you progress, you’ll also explore global forecasting models, ensemble methods, and probabilistic forecasting techniques.

This new edition goes deeper into transformer architectures and probabilistic forecasting, including new content on the latest time series models, conformal prediction, and hierarchical forecasting. Whether you seek advanced deep learning insights or specialized architecture implementations, this edition provides practical strategies and new content to elevate your forecasting skills.

What you will learn

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