Inhoudsopgave:
|
\u003cp\u003e\u003cb\u003eLearn traditional and cutting-edge machine learning (ML) and deep learning techniques and best practices for time series forecasting, including global forecasting models, conformal prediction, and transformer architectures\u003c/b\u003e\u003c/p\u003e\u003ch4\u003eKey Features\u003c/h4\u003e\u003cul\u003e\u003cli\u003eApply ML and global models to improve forecasting accuracy through practical examples\u003c/li\u003e\u003cli\u003eEnhance your time series toolkit by using deep learning models, including RNNs, transformers, and N-BEATS\u003c/li\u003e\u003cli\u003eLearn probabilistic forecasting with conformal prediction, Monte Carlo dropout, and quantile regressions\u003c/li\u003e\u003cli\u003ePurchase of the print or Kindle book includes a free eBook in PDF format\u003c/li\u003e\u003c/ul\u003e\u003ch4\u003eBook Description\u003c/h4\u003ePredicting the future, whether it's market trends, energy demand, or website traffic, has never been more crucial. This practical, hands-on guide empowers you to build and deploy powerful time series forecasting models. Whether youâre working with traditional statistical methods or cutting-edge deep learning architectures, this book provides structured learning and best practices for both.\nStarting 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, youâll learn preprocessing, feature engineering, and model evaluation. As you progress, youâll also explore global forecasting models, ensemble methods, and probabilistic forecasting techniques.\nThis 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.\u003ch4\u003eWhat you will learn\u003c/h4\u003e\u003cul\u003e\u003cli\u003eBuild machine learning models for regression-based time series forecasting\u003c/li\u003e\u003cli\u003eApply powerful feature engineering techniques to enhance prediction accuracy\u003c/li\u003e\u003cli\u003eTackle common challenges like non-stationarity and seasonality\u003c/li\u003e\u003cli\u003eCombine multiple forecasts using ensembling and stacking for superior results\u003c/li\u003e\u003cli\u003eExplore cutting-edge advancements in probabilistic forecasting and handle intermittent or sparse time series\u003c/li\u003e\u003cli\u003eEvaluate and validate your forecasts using best practices and statistical metrics\u003c/li\u003e\u003c/ul\u003e\u003ch4\u003eWho this book is for\u003c/h4\u003eThis book is ideal for data scientists, financial analysts, quantitative analysts, machine learning engineers, and researchers who need to model time-dependent data across industries, such as finance, energy, meteorology, risk analysis, and retail. Whether you are a professional looking to apply cutting-edge models to real-world problems or a student aiming to build a strong foundation in time series analysis and forecasting, this book will provide the tools and techniques you need. Familiarity with Python and basic machine learning concepts is recommended. |
nl
en