{"title":"Wayne A Woodward","description":"\u003cp\u003eDelve into the thrilling world of Wayne A. Woodward, where gripping mysteries and suspenseful tales await. Perfect for fans of crime fiction with a twist, discover your next captivating read here.\u003c\/p\u003e","products":[{"product_id":"applied-time-series-analysis-with-r-book-wayne-a-woodward-9781498734226","title":"Applied Time Series Analysis with R","description":"Virtually any random process developing chronologically can be viewed as a time series. In economics closing prices of stocks, the cost of money, the jobless rate, and retail sales are just a few examples of many. Developed from course notes and extensively classroom-tested, Applied Time Series Analysis with R, Second Edition includes examples across a variety of fields, develops theory, and provides an R-based software package to aid in addressing time series problems in a broad spectrum of fields. The material is organized in an optimal format for graduate students in statistics as well as in the natural and social sciences to learn to use and understand the tools of applied time series analysis.  Features         Gives readers the ability to actually solve significant real-world problems      Addresses many types of nonstationary time series and cutting-edge methodologies       Promotes understanding of the data and associated models rather than viewing it as the output of a \"black box\"      Provides the R package tswge available on CRAN which contains functions and over 100 real and simulated data sets to accompany the book. Extensive help regarding the use of tswge functions is provided in appendices and on an associated website.      Over 150 exercises and extensive support for instructors  The second edition includes additional real-data examples, uses R-based code that helps students easily analyze data, generate realizations from models, and explore the associated characteristics. It also adds discussion of new advances in the analysis of long memory data and data with time-varying frequencies (TVF).","brand":"WoB","offers":[{"title":"- \/ - \/ -","offer_id":50697549775121,"sku":"","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ GARDNERS","offer_id":50697551872273,"sku":"NGR9781498734226","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"US \/ GOOD \/ SBYB","offer_id":51481626345745,"sku":"CIN1498734227G","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"GB \/ NEW \/ INGRAM","offer_id":52481002701073,"sku":"NLS9781498734226","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ VERY_GOOD \/ INTERNAL","offer_id":52725781037329,"sku":"GOR014600132","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/B074KQ9GNK.jpg?v=1750828046"},{"product_id":"time-series-for-data-science-book-wayne-a-woodward-9780367537944","title":"Time Series for Data Science","description":"Practical Time Series Analysis for Data Science is an accessible guide that doesnt require a background in calculus to be engaging but does not shy away from deeper explanations of the techniques discussed.","brand":"WoB","offers":[{"title":"GB \/ NEW \/ GARDNERS","offer_id":50698174169361,"sku":"NGR9780367537944","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"US \/ NEW \/ INGRAM","offer_id":51001542934801,"sku":"NIN9780367537944","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"GB \/ NEW \/ INGRAM","offer_id":52432068641041,"sku":"NLS9780367537944","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/036753794X.jpg?v=1750696978"},{"product_id":"time-series-for-data-science-book-wayne-a-woodward-9780367543891","title":"Time Series for Data Science","description":"Data Science students and practitioners want to find a forecast that “works” and don’t want to be constrained to a single forecasting strategy, Time Series for Data Science: Analysis and Forecasting discusses techniques of ensemble modelling for combining information from several strategies. Covering time series regression models, exponential smoothing, Holt-Winters forecasting, and Neural Networks. It places a particular emphasis on classical ARMA and ARIMA models that is often lacking from other textbooks on the subject.  This book is an accessible guide that doesn’t require a background in calculus to be engaging but does not shy away from deeper explanations of the techniques discussed.  Features:    Provides a thorough coverage and comparison of a wide array of time series models and methods: Exponential Smoothing, Holt Winters, ARMA and ARIMA, deep learning models including RNNs, LSTMs, GRUs, and ensemble models composed of combinations of these models. Introduces the factor table representation of ARMA and ARIMA models. This representation is not available in any other book at this level and is extremely useful in both practice and pedagogy. Uses real world examples that can be readily found via web links from sources such as the US Bureau of Statistics, Department of Transportation and the World Bank. There is an accompanying R package that is easy to use and requires little or no previous R experience. 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Features            Gives readers the ability to actually solve significant real-world problems          Addresses many types of nonstationary time series and cutting-edge methodologies           Promotes understanding of the data and associated models rather than viewing it as the output of a \"black box\"          Provides the R package tswge available on CRAN which contains functions and over 100 real and simulated data sets to accompany the book. Extensive help regarding the use of tswge functions is provided in appendices and on an associated website.          Over 150 exercises and extensive support for instructors        The second edition includes additional real-data examples, uses R-based code that helps students easily analyze data, generate realizations from models, and explore the associated characteristics. 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Developed from course notes and extensively classroom-tested, Applied Time Series Analysis includes examples across a variety of fields, develops theory, and provides software to address time series problems in a broad spectrum of fields. The authors organize the information in such a format that graduate students in applied science, statistics, and economics can satisfactorily navigate their way through the book while maintaining mathematical rigor.   One of the unique features of Applied Time Series Analysis is the associated software, GW-WINKS, designed to help students easily generate realizations from models and explore the associated model and data characteristics. The text explores many important new methodologies that have developed in time series, such as ARCH and GARCH processes, time varying frequencies (TVF), wavelets, and more. Other programs (some written in R and some requiring S-plus) are available on an associated website for performing computations related to the material in the final four chapters.","brand":"WoB","offers":[{"title":"US \/ GOOD \/ SBYB","offer_id":52153667780881,"sku":"CIN1439818371G","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9781439818374.jpg?v=1757621169"}],"url":"https:\/\/www.worldofbooks.com\/collections\/author-books-by-wayne-a-woodward.oembed","provider":"World of Books ","version":"1.0","type":"link"}