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Forecasting: principles and practice by Rob J Hyndman,George Athanasopoulos Pdf
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.
Time Series Forecasting in Python by Marco Peixeiro Pdf
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
Author : James D. Hamilton Publisher : Princeton University Press Page : 820 pages File Size : 48,7 Mb Release : 2020-09-01 Category : Business & Economics ISBN : 9780691218632
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.
Analyzing Neural Time Series Data by Mike X Cohen Pdf
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.
Doing Meta-Analysis with R by Mathias Harrer,Pim Cuijpers,Toshi A. Furukawa,David D. Ebert Pdf
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
Author : Carl Edward Rasmussen,Christopher K. I. Williams Publisher : MIT Press Page : 266 pages File Size : 43,8 Mb Release : 2005-11-23 Category : Computers ISBN : 9780262182539
Gaussian Processes for Machine Learning by Carl Edward Rasmussen,Christopher K. I. Williams Pdf
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.
Advanced Forecasting with Python by Joos Korstanje Pdf
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.
Foundations of Statistical Natural Language Processing by Christopher Manning,Hinrich Schutze Pdf
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.
The great Russian psychologist L. S. Vygotsky has long been recognized as a pioneer in developmental psychology. But somewhat ironically, his theory of development has never been well understood in the West. Mind in Society should correct much of this misunderstanding. Carefully edited by a group of outstanding Vygotsky scholars, the book presents a unique selection of Vygotsky’s important essays, most of which have previously been unavailable in English. The Vygotsky who emerges from these pages can no longer be glibly included among the neobehaviorists. In these essays he outlines a dialectical-materialist theory of cognitive development that anticipates much recent work in American social science. The mind, Vygotsky argues, cannot be understood in isolation from the surrounding society. Man is the only animal who uses tools to alter his own inner world as well as the world around him. From the handkerchief knotted as a simple mnemonic device to the complexities of symbolic language, society provides the individual with technology that can be used to shape the private processes of mind. In Mind in Society Vygotsky applies this theoretical framework to the development of perception, attention, memory, language, and play, and he examines its implications for education. The result is a remarkably interesting book that is bound to renew Vygotsky’s relevance to modern psychological thought.
Author : Thomas L. Saaty,Luis G. Vargas Publisher : Springer Science & Business Media Page : 342 pages File Size : 41,5 Mb Release : 2012-04-11 Category : Business & Economics ISBN : 9781461435976
Models, Methods, Concepts & Applications of the Analytic Hierarchy Process by Thomas L. Saaty,Luis G. Vargas Pdf
The Analytic Hierarchy Process (AHP) is a prominent and powerful tool for making decisions in situations involving multiple objectives. Models, Methods, Concepts and Applications of the Analytic Hierarchy Process, 2nd Edition applies the AHP in order to solve problems focused on the following three themes: economics, the social sciences, and the linking of measurement with human values. For economists, the AHP offers a substantially different approach to dealing with economic problems through ratio scales. Psychologists and political scientists can use the methodology to quantify and derive measurements for intangibles. Meanwhile researchers in the physical and engineering sciences can apply the AHP methods to help resolve the conflicts between hard measurement data and human values. Throughout the book, each of these topics is explored utilizing real life models and examples, relevant to problems in today’s society. This new edition has been updated and includes five new chapters that includes discussions of the following: - The eigenvector and why it is necessary - A summary of ongoing research in the Middle East that brings together Israeli and Palestinian scholars to develop concessions from both parties - A look at the Medicare Crisis and how AHP can be used to understand the problems and help develop ideas to solve them.
Christiaan Heij,Paul de Boer,Philip Hans Franses,Teun Kloek,Herman K. van Dijk,All at the Erasmus University in Rotterdam
Author : Christiaan Heij,Paul de Boer,Philip Hans Franses,Teun Kloek,Herman K. van Dijk,All at the Erasmus University in Rotterdam Publisher : OUP Oxford Page : 816 pages File Size : 49,9 Mb Release : 2004-03-25 Category : Business & Economics ISBN : 9780191533235
Econometric Methods with Applications in Business and Economics by Christiaan Heij,Paul de Boer,Philip Hans Franses,Teun Kloek,Herman K. van Dijk,All at the Erasmus University in Rotterdam Pdf
Nowadays applied work in business and economics requires a solid understanding of econometric methods to support decision-making. Combining a solid exposition of econometric methods with an application-oriented approach, this rigorous textbook provides students with a working understanding and hands-on experience of current econometrics. Taking a 'learning by doing' approach, it covers basic econometric methods (statistics, simple and multiple regression, nonlinear regression, maximum likelihood, and generalized method of moments), and addresses the creative process of model building with due attention to diagnostic testing and model improvement. Its last part is devoted to two major application areas: the econometrics of choice data (logit and probit, multinomial and ordered choice, truncated and censored data, and duration data) and the econometrics of time series data (univariate time series, trends, volatility, vector autoregressions, and a brief discussion of SUR models, panel data, and simultaneous equations). · Real-world text examples and practical exercise questions stimulate active learning and show how econometrics can solve practical questions in modern business and economic management. · Focuses on the core of econometrics, regression, and covers two major advanced topics, choice data with applications in marketing and micro-economics, and time series data with applications in finance and macro-economics. · Learning-support features include concise, manageable sections of text, frequent cross-references to related and background material, summaries, computational schemes, keyword lists, suggested further reading, exercise sets, and online data sets and solutions. · Derivations and theory exercises are clearly marked for students in advanced courses. This textbook is perfect for advanced undergraduate students, new graduate students, and applied researchers in econometrics, business, and economics, and for researchers in other fields that draw on modern applied econometrics.
Theory of Particulate Processes by Alan Ranodolph Pdf
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.
Systems, Software and Services Process Improvement by Jakub Stolfa,Svatopluk Stolfa,Rory V. O'Connor,Richard Messnarz Pdf
This volume constitutes the refereed proceedings of the 24th EuroSPI conference, held in Ostrava, Czech Republic, in September 2017.The 56 revised full papers presented were carefully reviewed and selected from 97 submissions. They are organized in topical sections on SPI and VSEs, SPI and process models, SPI and safety, SPI and project management, SPI and implementation, SPI issues, SPI and automotive, selected key notes and workshop papers, GamifySPI, SPI in Industry 4.0, best practices in implementing traceability, good and bad practices in improvement, safety and security, experiences with agile and lean, standards and assessment models, team skills and diversity strategies.
Mergers & Acquisitions For Dummies by Bill Snow Pdf
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.
