Название: Predictive Time Series Forecasting with Python: Mastering Machine Learning and Deep Learning Approaches for Industry-Ready Predictions
Автор: Aarav Joshi
Издательство: 101 Books
Год: 2025
Страниц: 1878
Язык: английский
Формат: epub (true)
Размер: 16.4 MB

"Predictive Time Series Forecasting with Python" is a comprehensive guide that equips data scientists, analysts, and Machine Learning practitioners with the skills to build industry-ready forecasting models. The book bridges the gap between theoretical concepts and practical implementation, covering everything from classical statistical methods to cutting-edge Deep Learning approaches. Readers will master essential techniques for handling time-dependent data, including feature engineering, model selection, and evaluation strategies specific to temporal data.

The book explores both univariate and multivariate forecasting, probabilistic predictions, and advanced architectures like RNNs, transformers, and N-BEATS. Through hands-on examples and real-world case studies, readers will learn to build robust forecasting pipelines that can be deployed in production environments. Whether you're predicting financial markets, energy demand, retail sales, or website traffic, this book provides the tools and techniques to create accurate, scalable, and interpretable forecasting systems. With a focus on Python implementation using libraries like Pandas, PyTorch, and Scikit-learn, readers will develop practical skills that can be immediately applied to solve complex forecasting challenges across industries.

Time series data forms the backbone of predictive modeling for sequential observations. These structured collections of data points, indexed in chronological order, are fundamental to forecasting future values and understanding temporal patterns. Time series analysis differs significantly from conventional statistical methods because the temporal ordering of observations introduces dependencies that must be carefully addressed. Getting familiar with the various structures and representations of time series data is essential before implementing sophisticated forecasting models. The way we organize, store, and manipulate time series data directly impacts our analysis capabilities and the accuracy of our predictions. Let’s explore the fundamental concepts and practical implementations of time series data structures that form the foundation for effective forecasting.

A time series is a sequence of data points collected at consecutive time intervals. Each observation in a time series is associated with a specific timestamp, making the temporal ordering a critical component of the data structure. Unlike other data types, the sequence matters tremendously in time series analysis, as current values often depend on past observations.