Stephen Robertson – författare

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. The package implements the wide variety of models and methods presented in the book and has tremendous pedagogical use.

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. The package implements the wide variety of models and methods presented in the book and has tremendous pedagogical use.

The phrase “Harlem in the 1920s” evokes images of the Harlem Renaissance, or of Marcus Garvey and soapbox orators haranguing crowds about politics and race. Yet the most ubiquitous feature of Harlem life between the world wars was the game of “numbers.” Thousands of wagers, usually of a dime or less, would be placed on a daily number derived from U.S. bank statistics. The rewards of “hitting the number,” a 600-to-1 payoff, tempted the ordinary men and women of the Black Metropolis with the chimera of the good life. Playing the Numbers tells the story of this illegal form of gambling and the central role it played in the lives of African Americans who flooded into Harlem in the wake of World War I.For a dozen years the “numbers game” was one of America’s rare black-owned businesses, turning over tens of millions of dollars every year. The most successful “bankers” were known as Black Kings and Queens, and they lived royally. Yet the very success of “bankers” like Stephanie St. Clair and Casper Holstein attracted Dutch Schultz, Lucky Luciano, and organized crime to the game. By the late 1930s, most of the profits were being siphoned out of Harlem.Playing the Numbers reveals a unique dimension of African American culture that made not only Harlem but New York City itself the vibrant and energizing metropolis it was. An interactive website allows readers to locate actors and events on Harlem’s streets.

In the first half of the twentieth century, Americans' intense concern with sex crimes against children led to a wave of public discussion, legislative action, and criminal prosecution. Stephen Robertson provides the first large-scale, long-term study of how American criminal courts dealt with the prosecution of sexual violence against children. Robertson describes how the nineteenth-century approach to childhood as a single phase of innocence began to shift at the end of the century to include several stages of childhood development, prompting reformers to create legal categories such as statutory rape and carnal abuse to protect children. However, while ordinary New Yorkers' involvement in the prosecution of those offenses reshaped their understandings of who was a child and produced a new concern to establish the age of their sexual partners, their beliefs in childhood innocence and in a concept of sexuality centered on sexual intercourse remained unchanged. As a result, families' use of the law and jurors' decisions ultimately diminished the protection the new laws offered to children. Robertson's study, based on the previously unexamined files of the New York County district attorney's office, reveals the importance of child sexuality and sex crimes in twentieth-century American culture.

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. The package implements the wide variety of models and methods presented in the book and has tremendous pedagogical use.

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. The package implements the wide variety of models and methods presented in the book and has tremendous pedagogical use.

Over the past three decades, the make-up of the market value of Standard & Poor 500 companies has changed dramatically. While intangible assets amounted to less than one-third of company value in the mid-1980s, this has almost tripled to four-fifths on average today. A large contributor to this trend is intellectual property (IP) – a situation that is even truer for start-up companies, where IP assets may be the only significant asset a company has for quite some time. It is therefore important that company leaders understand the intricacies of IP strategy and how it is an integral part of a successful business strategy.This guide provides a solid grounding in concepts such as IP generation, IP valuation, portfolio management and the monetisation of IP assets, as well as practical guidance on setting an IP strategy and managing the risks associated with third-party intellectual property. For a holistic understanding, less formal IP assets such as trade secrets and brand strategy are covered in addition to patents and trademarks. Topics are introduced clearly and accessibly to provide decision makers with the necessary background to make informed decisions in cooperation with specialist in-house or external advisers. Chapter features include key pointers, digestible chapter summaries, and helpful diagrams and checklists, as well as case studies to bring theory into sharp practical focus.Whether your company is firmly established or just starting out, this accessible guide is essential reading to facilitate informed decision-making on IP issues. It will also provide those working with intellectual property with a high-level overview of current thinking on IP strategy.

This book constitutes the refereed proceedings of the Second International Conference on the Theory of Information Retrieval, ICTIR 2009, held in Cambridge, UK, in September 2009. The 18 revised full papers, 14 short papers, and 11 posters presented together with one invited talk were carefully reviewed and selected from 82 submissions. The papers are categorized into four main themes: novel IR models, evaluation, efficiency, and new perspectives in IR. Twenty-one papers fall into the general theme of novel IR models, ranging from various retrieval models, query and term selection models, Web IR models, developments in novelty and diversity, to the modeling of user aspects. There are four papers on new evaluation methodologies, e.g., modeling score distributions, evaluation over sessions, and an axiomatic framework for XML retrieval evaluation. Three papers focus on the issue of efficiency and offer solutions to improve the tractability of PageRank, data cleansing practices for training classifiers, and approximate search for distributed IR. Finally, four papers look into new perspectives of IR and shed light on some new emerging areas of interest, such as the application and adoption of quantum theory in IR.

This book constitutes the refereed proceedings of the Second International Conference on the Theory of Information Retrieval, ICTIR 2009, held in Cambridge, UK, in September 2009. The 18 revised full papers, 14 short papers, and 11 posters presented together with one invited talk were carefully reviewed and selected from 82 submissions. The papers are categorized into four main themes: novel IR models, evaluation, efficiency, and new perspectives in IR. Twenty-one papers fall into the general theme of novel IR models, ranging from various retrieval models, query and term selection models, Web IR models, developments in novelty and diversity, to the modeling of user aspects. There are four papers on new evaluation methodologies, e.g., modeling score distributions, evaluation over sessions, and an axiomatic framework for XML retrieval evaluation. Three papers focus on the issue of efficiency and offer solutions to improve the tractability of PageRank, data cleansing practices for training classifiers, and approximate search for distributed IR. Finally, four papers look into new perspectives of IR and shed light on some new emerging areas of interest, such as the application and adoption of quantum theory in IR.