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Book Synopsis Time Series Forecasting in Python by : Marco Peixeiro
Download or read book Time Series Forecasting in Python written by Marco Peixeiro and published by Simon and Schuster. This book was released on 2022-11-15 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build predictive models from time-based patterns in your data. Master statistical models including new deep learning approaches for time series forecasting. In Time Series Forecasting in Python you will learn how to: Recognize a time series forecasting problem and build a performant predictive model Create univariate forecasting models that account for seasonal effects and external variables Build multivariate forecasting models to predict many time series at once Leverage large datasets by using deep learning for forecasting time series Automate the forecasting process Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You’ll explore interesting real-world datasets like Google’s daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow. About the technology You can predict the future—with a little help from Python, deep learning, and time series data! Time series forecasting is a technique for modeling time-centric data to identify upcoming events. New Python libraries and powerful deep learning tools make accurate time series forecasts easier than ever before. About the book Time Series Forecasting in Python teaches you how to get immediate, meaningful predictions from time-based data such as logs, customer analytics, and other event streams. In this accessible book, you’ll learn statistical and deep learning methods for time series forecasting, fully demonstrated with annotated Python code. Develop your skills with projects like predicting the future volume of drug prescriptions, and you’ll soon be ready to build your own accurate, insightful forecasts. What's inside Create models for seasonal effects and external variables Multivariate forecasting models to predict multiple time series Deep learning for large datasets Automate the forecasting process About the reader For data scientists familiar with Python and TensorFlow. About the author Marco Peixeiro is a seasoned data science instructor who has worked as a data scientist for one of Canada’s largest banks. Table of Contents PART 1 TIME WAITS FOR NO ONE 1 Understanding time series forecasting 2 A naive prediction of the future 3 Going on a random walk PART 2 FORECASTING WITH STATISTICAL MODELS 4 Modeling a moving average process 5 Modeling an autoregressive process 6 Modeling complex time series 7 Forecasting non-stationary time series 8 Accounting for seasonality 9 Adding external variables to our model 10 Forecasting multiple time series 11 Capstone: Forecasting the number of antidiabetic drug prescriptions in Australia PART 3 LARGE-SCALE FORECASTING WITH DEEP LEARNING 12 Introducing deep learning for time series forecasting 13 Data windowing and creating baselines for deep learning 14 Baby steps with deep learning 15 Remembering the past with LSTM 16 Filtering a time series with CNN 17 Using predictions to make more predictions 18 Capstone: Forecasting the electric power consumption of a household PART 4 AUTOMATING FORECASTING AT SCALE 19 Automating time series forecasting with Prophet 20 Capstone: Forecasting the monthly average retail price of steak in Canada 21 Going above and beyond
Book Synopsis Forecasting: principles and practice by : Rob J Hyndman
Download or read book Forecasting: principles and practice written by Rob J Hyndman and published by OTexts. This book was released on 2018-05-08 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.
Book Synopsis Time Series Analysis by : James D. Hamilton
Download or read book Time Series Analysis written by James D. Hamilton and published by Princeton University Press. This book was released on 2020-09-01 with total page 820 pages. Available in PDF, EPUB and Kindle. Book excerpt: An authoritative, self-contained overview of time series analysis for students and researchers The past decade has brought dramatic changes in the way that researchers analyze economic and financial time series. This textbook synthesizes these advances and makes them accessible to first-year graduate students. James Hamilton provides comprehensive treatments of important innovations such as vector autoregressions, generalized method of moments, the economic and statistical consequences of unit roots, time-varying variances, and nonlinear time series models. In addition, he presents basic tools for analyzing dynamic systems—including linear representations, autocovariance generating functions, spectral analysis, and the Kalman filter—in a way that integrates economic theory with the practical difficulties of analyzing and interpreting real-world data. Time Series Analysis fills an important need for a textbook that integrates economic theory, econometrics, and new results. This invaluable book starts from first principles and should be readily accessible to any beginning graduate student, while it is also intended to serve as a reference book for researchers.
Book Synopsis Fundamentals of Business Process Management by : Marlon Dumas
Download or read book Fundamentals of Business Process Management written by Marlon Dumas and published by Springer. This book was released on 2018-03-23 with total page 527 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook covers the entire Business Process Management (BPM) lifecycle, from process identification to process monitoring, covering along the way process modelling, analysis, redesign and automation. Concepts, methods and tools from business management, computer science and industrial engineering are blended into one comprehensive and inter-disciplinary approach. The presentation is illustrated using the BPMN industry standard defined by the Object Management Group and widely endorsed by practitioners and vendors worldwide. In addition to explaining the relevant conceptual background, the book provides dozens of examples, more than 230 exercises – many with solutions – and numerous suggestions for further reading. This second edition includes extended and completely revised chapters on process identification, process discovery, qualitative process analysis, process redesign, process automation and process monitoring. A new chapter on BPM as an enterprise capability has been added, which expands the scope of the book to encompass topics such as the strategic alignment and governance of BPM initiatives. The textbook is the result of many years of combined teaching experience of the authors, both at the undergraduate and graduate levels as well as in the context of professional training. Students and professionals from both business management and computer science will benefit from the step-by-step style of the textbook and its focus on fundamental concepts and proven methods. Lecturers will appreciate the class-tested format and the additional teaching material available on the accompanying website.
Book Synopsis Gaussian Processes for Machine Learning by : Carl Edward Rasmussen
Download or read book Gaussian Processes for Machine Learning written by Carl Edward Rasmussen and published by MIT Press. This book was released on 2005-11-23 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.
Book Synopsis Analyzing Neural Time Series Data by : Mike X Cohen
Download or read book Analyzing Neural Time Series Data written by Mike X Cohen and published by MIT Press. This book was released on 2014-01-17 with total page 615 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive guide to the conceptual, mathematical, and implementational aspects of analyzing electrical brain signals, including data from MEG, EEG, and LFP recordings. This book offers a comprehensive guide to the theory and practice of analyzing electrical brain signals. It explains the conceptual, mathematical, and implementational (via Matlab programming) aspects of time-, time-frequency- and synchronization-based analyses of magnetoencephalography (MEG), electroencephalography (EEG), and local field potential (LFP) recordings from humans and nonhuman animals. It is the only book on the topic that covers both the theoretical background and the implementation in language that can be understood by readers without extensive formal training in mathematics, including cognitive scientists, neuroscientists, and psychologists. Readers who go through the book chapter by chapter and implement the examples in Matlab will develop an understanding of why and how analyses are performed, how to interpret results, what the methodological issues are, and how to perform single-subject-level and group-level analyses. Researchers who are familiar with using automated programs to perform advanced analyses will learn what happens when they click the “analyze now” button. The book provides sample data and downloadable Matlab code. Each of the 38 chapters covers one analysis topic, and these topics progress from simple to advanced. Most chapters conclude with exercises that further develop the material covered in the chapter. Many of the methods presented (including convolution, the Fourier transform, and Euler's formula) are fundamental and form the groundwork for other advanced data analysis methods. Readers who master the methods in the book will be well prepared to learn other approaches.
Book Synopsis Advanced Forecasting with Python by : Joos Korstanje
Download or read book Advanced Forecasting with Python written by Joos Korstanje and published by Apress. This book was released on 2021-07-03 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cover all the machine learning techniques relevant for forecasting problems, ranging from univariate and multivariate time series to supervised learning, to state-of-the-art deep forecasting models such as LSTMs, recurrent neural networks, Facebook’s open-source Prophet model, and Amazon’s DeepAR model. Rather than focus on a specific set of models, this book presents an exhaustive overview of all the techniques relevant to practitioners of forecasting. It begins by explaining the different categories of models that are relevant for forecasting in a high-level language. Next, it covers univariate and multivariate time series models followed by advanced machine learning and deep learning models. It concludes with reflections on model selection such as benchmark scores vs. understandability of models vs. compute time, and automated retraining and updating of models. Each of the models presented in this book is covered in depth, with an intuitive simple explanation of the model, a mathematical transcription of the idea, and Python code that applies the model to an example data set. Reading this book will add a competitive edge to your current forecasting skillset. The book is also adapted to those who have recently started working on forecasting tasks and are looking for an exhaustive book that allows them to start with traditional models and gradually move into more and more advanced models. What You Will Learn Carry out forecasting with Python Mathematically and intuitively understand traditional forecasting models and state-of-the-art machine learning techniques Gain the basics of forecasting and machine learning, including evaluation of models, cross-validation, and back testing Select the right model for the right use case Who This Book Is For The advanced nature of the later chapters makes the book relevant for applied experts working in the domain of forecasting, as the models covered have been published only recently. Experts working in the domain will want to update their skills as traditional models are regularly being outperformed by newer models.
Book Synopsis Doing Meta-Analysis with R by : Mathias Harrer
Download or read book Doing Meta-Analysis with R written by Mathias Harrer and published by CRC Press. This book was released on 2021-09-15 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: Doing Meta-Analysis with R: A Hands-On Guide serves as an accessible introduction on how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including calculation and pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools. Advanced but highly relevant topics such as network meta-analysis, multi-three-level meta-analyses, Bayesian meta-analysis approaches and SEM meta-analysis are also covered. A companion R package, dmetar, is introduced at the beginning of the guide. It contains data sets and several helper functions for the meta and metafor package used in the guide. The programming and statistical background covered in the book are kept at a non-expert level, making the book widely accessible. Features • Contains two introductory chapters on how to set up an R environment and do basic imports/manipulations of meta-analysis data, including exercises • Describes statistical concepts clearly and concisely before applying them in R • Includes step-by-step guidance through the coding required to perform meta-analyses, and a companion R package for the book
Book Synopsis Mergers & Acquisitions For Dummies by : Bill Snow
Download or read book Mergers & Acquisitions For Dummies written by Bill Snow and published by John Wiley & Sons. This book was released on 2018-08-30 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mergers & Acquisitions For Dummies (9781119543862) was previously published as Mergers & Acquisitions For Dummies (9780470385562). While this version features a new Dummies cover and design, the content is the same as the prior release and should not be considered a new or updated product. The easy way to make smart business transactions Are you a business owner, investor, venture capitalist, or member of a private equity firm looking to grow your business by getting involved in a merger with, or acquisition of, another company? Are you looking for a plain-English guide to how mergers and acquisitions can affect your investments? Look no further. Mergers & Acquisitions For Dummies explains the entire process step by step—from the different types of transactions and structures to raising funds and partnering. Plus, you'll get expert advice on identifying targets, business valuation, doing due diligence, closing the purchase agreement, and integrating new employees and new ways of doing business. Step-by-step techniques and real-world advice for making successful mergers and acquisitions Covers international laws and regulations How to take advantage of high-value deals Going beyond the case studies of other books, Mergers & Acquisitions For Dummies is your one-stop reference for making business growth a success.
Book Synopsis Mohs Micrographic Surgery by : Stephen N. Snow
Download or read book Mohs Micrographic Surgery written by Stephen N. Snow and published by Univ of Wisconsin Press. This book was released on 2004 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mohs Micrographic Surgery, an advanced treatment procedure for skin cancer, offers the highest potential for recovery--even if the skin cancer has been previously treated. This procedure is a state-of-the-art treatment in which the physician serves as surgeon, pathologist, and reconstructive surgeon. It relies on the accuracy of a microscope to trace and ensure removal of skin cancer down to its roots. This procedure allows dermatologists trained in Mohs Surgery to see beyond the visible disease and to precisely identify and remove the entire tumor, leaving healthy tissue unharmed. This procedure is most often used in treating two of the most common forms of skin cancer: basal cell carcinoma and squamous cell carcinoma. The cure rate for Mohs Micrographic Surgery is the highest of all treatments for skin cancer--up to 99 percent even if other forms of treatment have failed. This procedure, the most exact and precise method of tumor removal, minimizes the chance of regrowth and lessens the potential for scarring or disfigurement
Book Synopsis Foundations of Statistical Natural Language Processing by : Christopher Manning
Download or read book Foundations of Statistical Natural Language Processing written by Christopher Manning and published by MIT Press. This book was released on 1999-05-28 with total page 719 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.
Book Synopsis Theory of Particulate Processes by : Alan Ranodolph
Download or read book Theory of Particulate Processes written by Alan Ranodolph and published by Elsevier. This book was released on 2012-12-02 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theory of Particulate Processes: Analysis and Techniques of Continuous Crystallization, Second Edition covers the numerous population balance-based particulate studies. This edition emerged from the notes for an industrial short course on crystallization. This book is divided into 10 chapters and begins with an outline of the methods for representation of particle distributions and a systematic approach to the predictive modeling of processes where there is a need to characterize distributions in time and space and by some identifying property. The succeeding chapters provide a specific and more elementary approach to modeling crystal size distributions, as well as the modeling the kinetics of crystal nucleation and growth rates. Other chapters discuss a wide range of system analysis and design considerations specific to crystallization for both the steady state and unsteady state. The final chapters illustrate the use of a population balance analysis to interpret data from both laboratory and process equipment. These chapters also explore a wide variety of particulate processes and systems for which the population balance analysis is useful. This book is of great value to graduate students with particulate systems course.
Book Synopsis High-Dimensional Data Analysis with Low-Dimensional Models by : John Wright
Download or read book High-Dimensional Data Analysis with Low-Dimensional Models written by John Wright and published by Cambridge University Press. This book was released on 2022-01-13 with total page 718 pages. Available in PDF, EPUB and Kindle. Book excerpt: Connecting theory with practice, this systematic and rigorous introduction covers the fundamental principles, algorithms and applications of key mathematical models for high-dimensional data analysis. Comprehensive in its approach, it provides unified coverage of many different low-dimensional models and analytical techniques, including sparse and low-rank models, and both convex and non-convex formulations. Readers will learn how to develop efficient and scalable algorithms for solving real-world problems, supported by numerous examples and exercises throughout, and how to use the computational tools learnt in several application contexts. Applications presented include scientific imaging, communication, face recognition, 3D vision, and deep networks for classification. With code available online, this is an ideal textbook for senior and graduate students in computer science, data science, and electrical engineering, as well as for those taking courses on sparsity, low-dimensional structures, and high-dimensional data. Foreword by Emmanuel Candès.
Book Synopsis Foundations of Signal Processing by : Martin Vetterli
Download or read book Foundations of Signal Processing written by Martin Vetterli and published by Cambridge University Press. This book was released on 2014-09-04 with total page 745 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive and accessible textbook introduces students to the basics of modern signal processing techniques.
Book Synopsis A Playful Production Process by : Richard Lemarchand
Download or read book A Playful Production Process written by Richard Lemarchand and published by MIT Press. This book was released on 2021-10-12 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: How to achieve a happier and healthier game design process by connecting the creative aspects of game design with techniques for effective project management. This book teaches game designers, aspiring game developers, and game design students how to take a digital game project from start to finish—from conceptualizing and designing to building, playtesting, and iterating—while avoiding the uncontrolled overwork known among developers as “crunch.” Written by a legendary game designer, A Playful Production Process outlines a process that connects the creative aspects of game design with proven techniques for effective project management. The book outlines four project phases—ideation, preproduction, full production, and post-production—that give designers and developers the milestones they need to advance from the first glimmerings of an idea to a finished game.
Book Synopsis Business Process Implementation by : M. A. Jackson
Download or read book Business Process Implementation written by M. A. Jackson and published by Addison-Wesley Professional. This book was released on 1997 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The speed with which today's businesses have to adapt to ever-changing circumstances and the complexity required to remain ahead in a competitive environment have forced the development of automated business process systems. Business Process Implementation describes a new method for designing and implementingoffice workflow systems.This method was developed by Graham Twaddle and his colleagues, and has been analysed and described by Michael Jackson, one of the pre-eminent contributors to current software development practices. It has been tried and tested by Sherwood International UK who have incorporated it into their award-winning Logicware software support system. The method consists of a carefully crafted combination of data and process modelling that permits highly parallel, and therefore highly efficient and flexible workflow. Moreover, it embodies a novel approach to database design and implementation that gives many of the benefits usually ascribed to object orientation but requires only a conventional relational database environment.Key Features of the book include: - A clear and insightful description of the method, its range of applicability and its use.- Innovative diagram and textual notations for data and process modelling.- A thorough description of the Logicware software support system which provides insight into implementation issues and shows how to mechanize and simplify development.- Questions at the end of each chapter, providing readers with an opportunity to check their understanding of the concepts involved. This book will be invaluable for anyone who has to develop and use business systems for information and administration and especially to those working inan environment where office workflow is an important concern. It will provide the reader with a way of clearly documenting the processes and process-related knowledge that are the end result of a Business Process Reengineering study. It also provides an approach to implementing the reengineered processes that has been tried and tested successfully in large systems for critical business applications.Michael Jackson has worked in software for thirty five years during which time he has been involved in all aspects of program and system development. In 1971 he started his own company offering courses, project support and tools for software developers. He now works as an independent consultant in London and works part-time in software development research at AT&T Research in New Jersey. He played the leading role in developing the JSP and JSD methods of program and system development, and described them in his previous books Principles of Program Design and System Development. His most recent book Software Requirements and Specifications was published by Addison Wesley in June 1995.Graham Twaddle is technical Director of Sherwood International a company specialising in the design and production of computer systems for the insurance industry. Prior to this Graham was the founding director of Beta Computers a company specialising in tools for assisting rapid application development in many industry areas.Shelf category: Software Engineering Design / Business Systems
Book Synopsis Parallel Distributed Processing by : David E. Rumelhart
Download or read book Parallel Distributed Processing written by David E. Rumelhart and published by . This book was released on 1986 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: What makes people smarter than computers? These volumes by a pioneering neurocomputing group suggest that the answer lies in the massively parallel architecture of the human mind. They describe a new theory of cognition called connectionism that is challenging the idea of symbolic computation that has traditionally been at the center of debate in theoretical discussions about the mind. The authors' theory assumes the mind is composed of a great number of elementary units connected in a neural network. Mental processes are interactions between these units which excite and inhibit each other in parallel rather than sequential operations. In this context, knowledge can no longer be thought of as stored in localized structures; instead, it consists of the connections between pairs of units that are distributed throughout the network. Volume 1 lays the foundations of this exciting theory of parallel distributed processing, while Volume 2 applies it to a number of specific issues in cognitive science and neuroscience, with chapters describing models of aspects of perception, memory, language, and thought.
