Advanced Forecasting with Python : Mastering Modern Forecasting Techniques with Machine Learning and Cloud Tools / by Joos Korstanje.

Format:

Book

Author/Creator:

Korstanje, Joos.

Series:

Professional and Applied Computing Series

Language:

English

Subjects (All):

Python (Computer program language).

Machine learning.

Time-series analysis--Data processing.

Time-series analysis.

Physical Description:

1 online resource (321 pages)

Edition:

2nd ed. 2025.

Place of Publication:

Berkeley, CA : Apress : Imprint: Apress, 2025.

Summary:

Advanced Forecasting with Python, Second Edition, is a comprehensive and practical guide to mastering modern forecasting techniques using Python. Designed for data scientists, analysts, and machine learning practitioners, this updated edition bridges the gap between classical forecasting models and cutting-edge, AI-powered techniques that are reshaping the field. The book begins with foundational models like AR, MA, ARIMA, and SARIMA, offering intuitive and mathematical explanations alongside hands-on Python implementations. It then expands into multivariate models (VAR, VARMAX), supervised machine learning (Random Forests, XGBoost, LightGBM, CatBoost), and deep learning architectures such as LSTMs, NBEATS, and Transformers. Each chapter not only teaches the theory and code but also tracks model performance using MLflow, enabling efficient benchmarking and experimentation management. The second edition stands out for its extensive new content. Readers will now explore Orbit by Uber, AutoGluon by AWS, Prophet by Meta, Microsoft Azure AutoML, Google GCP AutoML, and TimeGPT by Nixtla, equipping them with the latest tools from top cloud providers. These additions make sure that readers stay current in an ever-evolving landscape. Moreover, the new chapters highlight practical deployment strategies and trade-offs between performance, explainability, and scalability. Whether you are just beginning your forecasting journey or seeking to enhance your expertise with state-of-the-art tools and cloud-based solutions, this book offers a rich, hands-on learning experience. With step-by-step Python examples, detailed model insights, and modern forecasting workflows, it is an indispensable resource for staying ahead in the realm of predictive analytics. You Will: < Build robust forecasting solutions using Python Gain both intuitive and mathematical insights into traditional and cutting-edge forecasting models Master model evaluation through cross-validation, backtesting, and MLflow-based tracking Leverage cloud-based platforms and Model-as-a-Service tools for scalable forecasting deployments.

Contents:

PART I: Machine Learning for Forecasting

Chapter 1: Models for Forecasting

Chapter 2: Model Evaluation for Forecasting

Chapter 3: Model Management and Benchmarking using MLflow

PART II: Univariate Time Series Models

Chapter 4: The AR model

Chapter 5: The MA model

Chapter 6: The ARMA model

Chapter 7: The ARIMA model

Chapter 8: The SARIMA model

PART III: Multivariate Time Series Models

Chapter 9: The SARIMAX model

Chapter 10: The VAR model

Chapter 11: The VARMAX model

PART IV: Supervised Models

Chapter 12: The Linear Regression

Chapter 13: The Decision Tree Model

Chapter 14: The kNN model

Chapter 15: The Random Forest

Chapter 16: Gradient Boosting with XGBoost, LightGBM, and CatBoost

Chapter 17: Bayesian Models with pyBATS

PART V: Neural Networks

Chapter 18: Neural Networks

Chapter 19: RNNs using SimpleRNN and GRU

Chapter 20: LSTM RNNs

PART VI: Black Box and Cloud Based Models

Chapter 21: The NBEATS model with Darts

Chapter 22: The Transformer model with Darts

Chapter 23: The NeuralProphet model

Chapter 24: The DeepAR model and AWS Sagemaker AI

Chapter 25: Uber's Orbit Model

Chapter 26: AutoML with Microsoft Azure

Chapter 27: AutoML with Vertex AI on Google Cloud Platform

Chapter 28: Nixtla Suite and TimeGPT

Chapter 29: Model Selection.

Notes:

Description based upon print version of record.

Description based on publisher supplied metadata and other sources.

ISBN:

979-88-6882-028-1

OCLC:

1555347768