Modern Time Series Forecasting with Python : Exploring statistical models, machine learning, and deep learning for cutting-edge time series forecasting (English Edition)

Ravindra Rapaka

ISBN 10: 9365893623 / ISBN 13: 9789365893625

Edité par Mercer Education Press Mär 2026, 2026

A propos de cet article

Neuware - Time series forecasting is driving decision-making in everything from financial markets to supply chain logistics. This book provides a hands-on roadmap to mastering this technology, bridging the gap between classical statistical rigor and cutting-edge artificial intelligence. Understand time series fundamentals by exploring decomposition, stationarity, and ACF/PACF analysis before mastering preprocessing and feature engineering. You will build foundational ARIMA, SARIMA, and Holt-Winters' models before pivoting to machine learning with XGBoost and Scikit-learn. The journey accelerates into deep learning, designing RNNs, LSTMs, and hybrid CNN-LSTM architectures for univariate and multivariate forecasting. After exploring advanced VAR and VECM models, you will implement walk-forward validation and professional error metrics. The final sections cover scalability and MLOps, teaching you to handle big data with Dask and deploy production-ready models via FastAPI and Apache Kafka. By the end of this book, you will be a competent practitioner capable of building high-performance forecasting pipelines for stock prices, demand, and sensor data. You will possess the technical expertise to deploy scalable, ethical, and accurate models in real-world cloud environments with confidence.What you will learn? Diagnose trend and seasonality using Statsmodels stationarity.? Build ARIMA/SARIMA and smoothing models using Statsmodels.? Engineer lag, rolling, and calendar-based forecasting features.? Deploy FastAPI pipelines and monitor Kafka drift.? Build LSTM and GRU architectures with TensorFlow.? Backtest, compare, and ensemble models with confidence.? Deploy, monitor, and retrain forecasting pipelines at scale.Who this book is forThis book is designed for data scientists, machine learning engineers, and analysts mastering temporal data. Proficiency in Python and basic statistics is required, while experience with cloud deployment or deep learning helps professional engineers scale models using the featured technical frameworks.Table of Contents1. Introduction to Time Series Data and Analysis2. Data Pre-processing and Feature Engineering3. Exploratory and Statistical Analysis of Time Series4. Autoregressive Models5. Moving Average and ARMA Models6. ARIMA and SARIMA Models7. Exponential Smoothing Methods8. Feature-based Machine Learning for Time Series Forecasting9. Introduction to Deep Learning for Time Series10. Building and Training LSTM Models for Time Series11. Advanced Deep Learning Architectures and Multivariate Forecasting12. Multivariate Time Series Forecasting13. Model Evaluation, Selection, and Ensembling14. Forecasting at Scale and Model Deployment15. Time Series Forecasting in Practice.

N° de réf. du vendeur 9789365893625

Détails bibliographiques

• Titre: Modern Time Series Forecasting with Python : Exploring statistical models, machine learning, and deep learning for cutting-edge time series forecasting (English Edition)
• Éditeur: Mercer Education Press Mär 2026
• Date de parution: 2026
• Langue: anglais
• ISBN à 10 chiffres: 9365893623
• ISBN à 13 chiffres: 9789365893625
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 235 L x 191 H x 23 P (en millimètres) 235 millimètres de largeur par 191 millimètres de hauteur par 23 millimètres de profondeur
• Poids: 763 grammes

A propos de ce titre

Synopsis

Time series forecasting is driving decision-making in everything from financial markets to supply chain logistics. This book provides a hands-on roadmap to mastering this technology, bridging the gap between classical statistical rigor and cutting-edge artificial intelligence.

Understand time series fundamentals by exploring decomposition, stationarity, and ACF/PACF analysis before mastering preprocessing and feature engineering. You will build foundational ARIMA, SARIMA, and Holt-Winters’ models before pivoting to machine learning with XGBoost and Scikit-learn. The journey accelerates into deep learning, designing RNNs, LSTMs, and hybrid CNN-LSTM architectures for univariate and multivariate forecasting. After exploring advanced VAR and VECM models, you will implement walk-forward validation and professional error metrics. The final sections cover scalability and MLOps, teaching you to handle big data with Dask and deploy production-ready models via FastAPI and Apache Kafka.

By the end of this book, you will be a competent practitioner capable of building high-performance forecasting pipelines for stock prices, demand, and sensor data. You will possess the technical expertise to deploy scalable, ethical, and accurate models in real-world cloud environments with confidence.

What you will learn

● Diagnose trend and seasonality using Statsmodels stationarity.

● Build ARIMA/SARIMA and smoothing models using Statsmodels.

● Engineer lag, rolling, and calendar-based forecasting features.

● Deploy FastAPI pipelines and monitor Kafka drift.

● Build LSTM and GRU architectures with TensorFlow.

● Backtest, compare, and ensemble models with confidence.

● Deploy, monitor, and retrain forecasting pipelines at scale.

Who this book is for

This book is designed for data scientists, machine learning engineers, and analysts mastering temporal data. Proficiency in Python and basic statistics is required, while experience with cloud deployment or deep learning helps professional engineers scale models using the featured technical frameworks.

Table of Contents

1. Introduction to Time Series Data and Analysis

2. Data Pre-processing and Feature Engineering

3. Exploratory and Statistical Analysis of Time Series

4. Autoregressive Models

5. Moving Average and ARMA Models

6. ARIMA and SARIMA Models

7. Exponential Smoothing Methods

8. Feature-based Machine Learning for Time Series Forecasting

9. Introduction to Deep Learning for Time Series

10. Building and Training LSTM Models for Time Series

11. Advanced Deep Learning Architectures and Multivariate Forecasting

12. Multivariate Time Series Forecasting

13. Model Evaluation, Selection, and Ensembling

14. Forecasting at Scale and Model Deployment

15. Time Series Forecasting in Practice