Regression Extension Techniques for Time-Series Data
In this chapter, you will learn how to leverage regression techniques to solve time-series problems efficiently. Regression is a supervised learning technique in machine learning where you try to estimate target variables using one or multiple regressors.
- PDF / 17,853,284 Bytes
- 420 Pages / 439.43 x 666.14 pts Page_size
- 54 Downloads / 261 Views
Hands-on Time Series Analysis with Python From Basics to Bleeding Edge Techniques
B V Vishwas Ashish Patel
Hands-on Time Series Analysis with Python: From Basics to Bleeding Edge Techniques B V Vishwas Infosys Bengaluru, India
Ashish Patel Cygnet Infotech Pvt Ltd Ahmedabad, India
ISBN-13 (pbk): 978-1-4842-5991-7 ISBN-13 (electronic): 978-1-4842-5992-4 https://doi.org/10.1007/978-1-4842-5992-4
Copyright © 2020 by B V Vishwas and Ashish Patel This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Trademarked names, logos, and images may appear in this Book. Rather than use a trademark symbol with every occurrence of a trademarked name, logo, or image we use the names, logos, and images only in an editorial fashion and to the benefit of the trademark owner, with no intention of infringement of the trademark. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. While the advice and information in this Book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Managing Director, Apress Media LLC: Welmoed Spahr Acquisitions Editor: Celestin Suresh John Development Editor: James Markham Coordinating Editor: Aditee Mirashi Cover designed by eStudioCalamar Cover image designed by Pixabay Distributed to the book trade worldwide by Springer Science+Business Media New York, 233 Spring Street, 6th Floor, New York, NY 10013. Phone 1-800-SPRINGER, fax (201) 348-4505, e-mail [email protected], or visit www.springeronline.com. Apress Media, LLC is a California LLC and the sole member (owner) is Springer Science + Business Media Finance Inc (SSBM Finance Inc). SSBM Finance Inc is a Delaware corporation. For information on translations, please e-mail [email protected]; for reprint, paperback, or audio rights, please e-mail [email protected]. Apress titles may be purchased in bulk for academic, corporate, or promotional use. eBook versions and licenses are also available for most titles. For more information, reference our Print and eBook Bulk Sales web page at www.apress.com/bulk-sales. Any source code or other supplementary material referenced by the author in this Book is available to readers on GitHub via the Book’s product page, located at www.apress.com
Data Loading...
