Time Series

Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that's so clouded in hype?...

This book provides a broad, mature, and systematic introduction to current financial econometric models and their applications to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout...

Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more...

Mathematics is beautiful--and it can be fun and exciting as well as practical. Good Math is your guide to some of the most intriguing topics from two thousand years of mathematics: from Egyptian fractions to Turing machines; from the real meaning of...

Learn how to apply the principles of machine learning to time series modeling with this indispensable resource Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of...

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9780648783084Author Bob MatherFormat PaperbackPage Count 222Imprint Bob MatherPublisher Bob MatherWeight(grams) 268gDimensions(mm) 210mm * 148mm * 12mm

An essential guide on high dimensional multivariate time series including all the latest topics from one of the leading experts in the field Following the highly successful and much lauded book, Time Series Analysis--Univariate and Multivariate...

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...

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9780648783077Author Bob MatherFormat HardbackPage Count 222Imprint Bob MatherPublisher Bob MatherWeight(grams) 417gDimensions(mm) 210mm * 148mm * 14mm

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9789888406272Author Riccardo LucchettiFormat PaperbackPage Count 366Imprint Samurai Media LimitedPublisher Samurai Media LimitedWeight(grams)...

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9781608055227Author Cagdas Hakan AladagFormat PaperbackPage Count 142Imprint Bentham Science PublishersPublisher Bentham Science PublishersWeight(grams) 216g

A much-needed introduction to the field of discrete-valued time series, with a focus on count-data time series Time series analysis is an essential tool in a wide array of fields, including business, economics, computer science, epidemiology, finance,...

This book gives a brief survey of the theory of multidimensional (multivariate), weakly stationary time series, with emphasis on dimension reduction and prediction. Understanding the covered material requires a certain mathematical maturity, a degree of...

Dynamic Time Series Models using R-INLA: An Applied Perspective is the outcome of a joint effort to systematically describe the use of R-INLA for analysing time series and showcasing the code and description by several examples. This book introduces the...

The only integrative approach to chaos and random fractal theory Chaos and random fractal theory are two of the most important theories developed for data analysis. Until now, there has been no single book that encompasses all of the basic concepts...

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...

This book proposes some novel models based on the autoregressive and moving average structures under various distributional assumptions of the innovation series for analysing non-stationary bivariate time series of counts. A useful resource for scholars,...