{"product_id":"advanced-data-modeling-and-forecasting-with-python-time-series-advanced-modeling-and-real-world-systems-9781067559281","title":"Advanced Data Modeling and Forecasting with Python: Time Series, Advanced Modeling, and Real-World Systems","description":"\u003cp\u003e • Author(s): Shouke Wei\u003cbr\u003e • Publisher: Deepsim Press\u003cbr\u003e • Publisher Imprint: Deepsim Press\u003cbr\u003e • BISAC: Languages - Python\u003c\/p\u003e\u003cp\u003eReal-world modeling problems rarely conform to the assumptions of standard workflows. Data may evolve over time, exhibit hidden structure, or suffer from imbalance and noise. Models that perform well in controlled settings often degrade when exposed to dynamic environments. Deploying a model introduces additional challenges, including integration, monitoring, and continuous adaptation. \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003eThis book addresses these realities.\u003cb\u003ePurpose of This Book\u003c\/b\u003e\u003cbr\u003eThe aim of this volume is to extend the modeling process beyond isolated techniques and toward \u003cb\u003ecomplete, real-world systems\u003c\/b\u003e.. \u003cp\u003e\u003c\/p\u003eRather than focusing on individual algorithms, the book emphasizes: \u003cul\u003e\n\u003cli\u003emodeling under temporal dependence\u003c\/li\u003e\n\u003cli\u003ediscovering structure in unlabeled data\u003c\/li\u003e\n\u003cli\u003ehandling imperfect and imbalanced datasets\u003c\/li\u003e\n\u003cli\u003ecombining methods into hybrid approaches\u003c\/li\u003e\n\u003cli\u003edeploying models into operational environments\u003c\/li\u003e\n\u003cli\u003eapplying modeling techniques to real-world domains\u003c\/li\u003e\n\u003c\/ul\u003e\u003cb\u003eWhat This Book Covers\u003c\/b\u003eThis book is organized into eight parts, each addressing a key extension of the modeling framework.\u003cbr\u003e\u003cb\u003ePart I - From Models to Systems\u003c\/b\u003e introduces the broader perspective required for advanced data science. It examines the limitations of standard modeling assumptions and outlines how modeling fits into larger, dynamic systems.\u003cbr\u003e\u003cb\u003ePart II - Time Series and Forecasting\u003c\/b\u003e focuses on data with temporal structure. It covers foundational concepts, classical models such as ARIMA and SARIMA, and modern machine learning approaches to forecasting.\u003cbr\u003e\u003cb\u003ePart III - Unsupervised Learning and Representation\u003c\/b\u003e explores techniques for discovering structure without labeled data, including clustering, dimensionality reduction, and representation learning methods such as autoencoders.\u003cbr\u003e\u003cb\u003ePart IV - Handling Real-World Dat\u003c\/b\u003ea Challenges addresses practical issues that frequently arise in applied settings, with particular emphasis on imbalanced data and its impact on evaluation and model performance.\u003cbr\u003e\u003cb\u003ePart V - Advanced and Hybrid Modeling\u003c\/b\u003e examines strategies for combining models and integrating statistical and machine learning approaches to achieve improved performance and flexibility.\u003cbr\u003e\u003cb\u003ePart VI - Deployment and Production Systems\u003c\/b\u003e moves beyond model development to operational considerations, including model packaging, API construction, deployment pipelines, monitoring, and model maintenance.\u003cbr\u003e\u003cb\u003ePart VII - Business Applications\u003c\/b\u003e demonstrates how modeling techniques are applied in practice, with examples in business decision-making, financial forecasting, and customer segmentation.\u003cbr\u003e\u003cb\u003ePart VIII - End-to-End Framework \u003c\/b\u003esynthesizes the material into a unified perspective, providing a practical reference for designing, evaluating, and maintaining complete data science systems.\u003cb\u003eFinal Remarks\u003c\/b\u003eAs models become more sophisticated, the challenges shift from implementation to design, evaluation, and integration.\u003cbr\u003eThe central idea of this book is simple: \u003cp\u003e\u003c\/p\u003e\u003ci\u003e\"effective data science requires not only good models, but well-designed systems.\"\u003c\/i\u003e \u003cp\u003e\u003c\/p\u003eBy extending the modeling framework into more realistic and demanding settings, this book aims to provide the tools and perspective needed to move from isolated models to reliable, real-world solutions.","brand":"Deepsim Press","offers":[{"title":"Paperback","offer_id":47882632921239,"sku":"9781067559281","price":6467.0,"currency_code":"INR","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9781067559281.webp?v=1781096380","url":"https:\/\/atlanticbooks.com\/products\/advanced-data-modeling-and-forecasting-with-python-time-series-advanced-modeling-and-real-world-systems-9781067559281","provider":"Atlantic Books","version":"1.0","type":"link"}