Modern Time Series Forecasting with Python
Cód:
491_9781803246802
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Build real-world time series forecasting systems which scale to millions of time series by applying modern machine learning and deep learning conceptsKey Features:Explore industry-tested machine learning techniques used to forecast millions of time seriesGet started with the revolutionary paradigm of global forecasting modelsGet to grips with new concepts by applying them to real-world datasets of energy forecastingBook Description:We live in a serendipitous era where the explosion in the quantum of data collected and a renewed interest in data-driven techniques such as machine learning (ML), has changed the landscape of analytics, and with it, time series forecasting. This book, filled with industry-tested tips and tricks, takes you beyond commonly used classical statistical methods such as ARIMA and introduces to you the latest techniques from the world of ML.This is a comprehensive guide to analyzing, visualizing, and creating state-of-the-art forecasting systems, complete with common topics such as ML and deep learning (DL) as well as rarely touched-upon topics such as global forecasting models, cross-validation strategies, and forecast metrics. Youll begin by exploring the basics of data handling, data visualization, and classical statistical methods before moving on to ML and DL models for time series forecasting. This book takes you on a hands-on journey in which youll develop state-of-the-art ML (linear regression to gradient-boosted trees) and DL (feed-forward neural networks, LSTMs, and transformers) models on a real-world dataset along with exploring practical topics such as interpretability.By the end of this book, youll be able to build world-class time series forecasting systems and tackle problems in the real world.What You Will Learn:Find out how to manipulate and visualize time series data like a proSet strong baselines with popular models such as ARIMADiscover how time series forecasting can be cast as regressionEngineer features for machine lea
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