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Predict the Next Bull or Bear Market and Win: How to Use Key Indicators to Profit in Any Market
by Michael SincereThe secrets to making money--no matter what the market conditions!A fundamental guide to investing, Predict the Next Bull or Bear Market and Win shows you how to build your wealth and protect your investments in an ever-changing market. With author and financial expert Michael Sincere's guidance, you'll learn everything you need to know about the key economic indicators that can help you predict the market's performance and better understand when to sell and when to buy.Unlike competing books that attempt a comprehensive survey of all market indicators, Sincere focuses only on those that make a real impact. His clear, concise strategies show you how to prosper during bull markets, be cautious during sideways markets, and make a profit when the market is going down.Predict the Next Bull or Bear Market and Win thoroughly educates you on the small number of indicators that are essential to a growing portfolio in a tumultuous market. By understanding the right economic indicators, you'll learn how to make money in any kind of market!
Predict the Next Bull or Bear Market and Win: How to Use Key Indicators to Profit in Any Market
by Michael SincereThe secrets to making money--no matter what the market conditions!A fundamental guide to investing, Predict the Next Bull or Bear Market and Win shows you how to build your wealth and protect your investments in an ever-changing market. With author and financial expert Michael Sincere's guidance, you'll learn everything you need to know about the key economic indicators that can help you predict the market's performance and better understand when to sell and when to buy.Unlike competing books that attempt a comprehensive survey of all market indicators, Sincere focuses only on those that make a real impact. His clear, concise strategies show you how to prosper during bull markets, be cautious during sideways markets, and make a profit when the market is going down.Predict the Next Bull or Bear Market and Win thoroughly educates you on the small number of indicators that are essential to a growing portfolio in a tumultuous market. By understanding the right economic indicators, you'll learn how to make money in any kind of market!
Predictability of Chaotic Dynamics: A Finite-time Lyapunov Exponents Approach (Springer Series in Synergetics)
by Juan C. Vallejo Miguel A. SanjuanThis book is primarily concerned with the computational aspects of predictability of dynamical systems - in particular those where observations, modeling and computation are strongly interdependent. Unlike with physical systems under control in laboratories, in astronomy it is uncommon to have the possibility of altering the key parameters of the studied objects. Therefore, the numerical simulations offer an essential tool for analysing these systems, and their reliability is of ever-increasing interest and importance. In this interdisciplinary scenario, the underlying physics provide the simulated models, nonlinear dynamics provides their chaoticity and instability properties, and the computer sciences provide the actual numerical implementation. This book introduces and explores precisely this link between the models and their predictability characterization based on concepts derived from the field of nonlinear dynamics, with a focus on the strong sensitivity to initial conditions and the use of Lyapunov exponents to characterize this sensitivity. This method is illustrated using several well-known continuous dynamical systems, such as the Contopoulos, Hénon-Heiles and Rössler systems. This second edition revises and significantly enlarges the material of the first edition by providing new entry points for discussing new predictability issues on a variety of areas such as machine decision-making, partial differential equations or the analysis of attractors and basins. Finally, the parts of the book devoted to the application of these ideas to astronomy have been greatly enlarged, by first presenting some basics aspects of predictability in astronomy and then by expanding these ideas to a detailed analysis of a galactic potential.
Predicting Flow-Induced Acoustics at Near-Stall Conditions in an Automotive Turbocharger Compressor
by Roberto Navarro GarcíaThis thesis offers new insights into the fluid flow behavior of automotive centrifugal compressors operating under near-stall conditions. Firstly it discusses the validation of three-dimensional computational fluid dynamics (CFD) unsteady simulations against acoustic experimental measurements using an original procedure based on plane wave pressure decomposition. It then examines the configuration of the CFD cases, highlighting the key parameters needed for a successful calculation. Moreover, it describes both the compressor mean and unsteady flow field from best-efficiency to near-surge operating points. Lastly, it provides readers with explanations of the various phenomena that arise when the mass flow rate is reduced and the compressor is driven to poor and noisy performance. Written for students, researchers and professionals who want to improve their understanding of the complex fluid flow behavior in centrifugal compressors, the thesis offers valuable practical insights into reducing the acoustic emissions of turbochargers.
Predicting Pandemics in a Globally Connected World, Volume 1: Toward a Multiscale, Multidisciplinary Framework through Modeling and Simulation (Modeling and Simulation in Science, Engineering and Technology)
by Nicola Bellomo Mark A. J. ChaplainThis contributed volume investigates several mathematical techniques for the modeling and simulation of viral pandemics, with a special focus on COVID-19. Modeling a pandemic requires an interdisciplinary approach with other fields such as epidemiology, virology, immunology, and biology in general. Spatial dynamics and interactions are also important features to be considered, and a multiscale framework is needed at the level of individuals and the level of virus particles and the immune system. Chapters in this volume address these items, as well as offer perspectives for the future.
Predicting Pandemics in a Globally Connected World, Volume 2: Toward a Multiscale, Multidisciplinary Framework through Modeling and Simulation (Modeling and Simulation in Science, Engineering and Technology)
by Nicola Bellomo Mark Chaplain Maíra AguiarIn an increasingly globally-connected world, the ability to predict, monitor, and contain pandemics is essential to ensure the health and well-being of all. This contributed volume investigates several mathematical techniques for the modeling and simulation of viral pandemics, with a special focus on COVID-19. Modeling a pandemic requires an interdisciplinary approach with other fields such as epidemiology, virology, immunology, and biology in general. Spatial dynamics and interactions are also important features to be considered, and a multiscale framework is needed at the societal level, the level of individuals, and the level of virus particles and the immune system. Chapters in this volume explore the latest research related to these items to demonstrate the utility of a variety of mathematical methods. Perspectives for the future are also offered
Predicting Stock Returns
by David G McMillanThis book provides a comprehensive analysis of asset price movement. It examines different aspects of stock return predictability, the interaction between stock return and dividend growth predictability, the relationship between stocks and bonds, and the resulting implications for asset price movement. By contributing to our understanding of the factors that cause price movement, this book will be of benefit to researchers, practitioners and policy makers alike.
Predicting the Future
by Henry AbarbanelThrough the development of an exact path integral for use in transferring information from observations to a model of the observed system, the author provides a general framework for the discussion of model building and evaluation across disciplines. Through many illustrative examples drawn from models in neuroscience, geosciences, and nonlinear electrical circuits, the concepts are exemplified in detail. Practical numerical methods for approximate evaluations of the path integral are explored, and their use in designing experiments and determining a model's consistency with observations is explored.
Predictive Analytics
by Thomas H. Davenport Eric Siegel"The Freakonomics of big data."--Stein Kretsinger, founding executive of Advertising.com; former lead analyst at Capital OneThis book is easily understood by all readers. Rather than a "how to" for hands-on techies, the book entices lay-readers and experts alike by covering new case studies and the latest state-of-the-art techniques.You have been predicted -- by companies, governments, law enforcement, hospitals, and universities. Their computers say, "I knew you were going to do that!" These institutions are seizing upon the power to predict whether you're going to click, buy, lie, or die.Why? For good reason: predicting human behavior combats financial risk, fortifies healthcare, conquers spam, toughens crime fighting, and boosts sales.How? Prediction is powered by the world's most potent, booming unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn.Predictive analytics unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future -- lifting a bit of the fog off our hazy view of tomorrow -- means pay dirt.In this rich, entertaining primer, former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction:What type of mortgage risk Chase Bank predicted before the recession.Predicting which people will drop out of school, cancel a subscription, or get divorced before they are even aware of it themselves.Why early retirement decreases life expectancy and vegetarians miss fewer flights.Five reasons why organizations predict death, including one health insurance company.How U.S. Bank, European wireless carrier Telenor, and Obama's 2012 campaign calculated the way to most strongly influence each individual.How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy!How companies ascertain untold, private truths -- how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job.How judges and parole boards rely on crime-predicting computers to decide who stays in prison and who goes free.What's predicted by the BBC, Citibank, ConEd, Facebook, Ford, Google, IBM, the IRS, Match.com, MTV, Netflix, Pandora, PayPal, Pfizer, and Wikipedia. A truly omnipresent science, predictive analytics affects everyone, every day. Although largely unseen, it drives millions of decisions, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate.Predictive analytics transcends human perception. This book's final chapter answers the riddle: What often happens to you that cannot be witnessed, and that you can't even be sure has happened afterward -- but that can be predicted in advance?Whether you are a consumer of it -- or consumed by it -- get a handle on the power of Predictive Analytics.
Predictive Analytics: Modeling and Optimization (Advanced Research in Reliability and System Assurance Engineering)
by Edited by Vijay Kumar and Mangey RamPredictive analytics refers to making predictions about the future based on different parameters which are historical data, machine learning, and artificial intelligence. This book provides the most recent advances in the field along with case studies and real-world examples. It discusses predictive modeling and analytics in reliability engineering and introduces current achievements and applications of artificial intelligence, data mining, and other techniques in supply chain management. It covers applications to reliability engineering practice, presents numerous examples to illustrate the theoretical results, and considers and analyses case studies and real-word examples. The book is written for researchers and practitioners in the field of system reliability, quality, supply chain management, and logistics management. Students taking courses in these areas will also find this book of interest.
Predictive Analytics
by Eric Siegel"Mesmerizing & fascinating..." --The Seattle Post-Intelligencer "The Freakonomics of big data." --Stein Kretsinger, founding executive of Advertising.com Award-winning | Used by over 30 universities | Translated into 9 languages An introduction for everyone. In this rich, fascinating -- surprisingly accessible -- introduction, leading expert Eric Siegel reveals how predictive analytics works, and how it affects everyone every day. Rather than a "how to" for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques. Prediction is booming. It reinvents industries and runs the world. Companies, governments, law enforcement, hospitals, and universities are seizing upon the power. These institutions predict whether you're going to click, buy, lie, or die. Why? For good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections. How? Prediction is powered by the world's most potent, flourishing unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn. Predictive Analytics unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate. In this lucid, captivating introduction -- now in its Revised and Updated edition -- former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: What type of mortgage risk Chase Bank predicted before the recession. Predicting which people will drop out of school, cancel a subscription, or get divorced before they even know it themselves. Why early retirement predicts a shorter life expectancy and vegetarians miss fewer flights. Five reasons why organizations predict death -- including one health insurance company. How U.S. Bank and Obama for America calculated -- and Hillary for America 2016 plans to calculate -- the way to most strongly persuade each individual. Why the NSA wants all your data: machine learning supercomputers to fight terrorism. How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! How companies ascertain untold, private truths -- how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. How judges and parole boards rely on crime-predicting computers to decide how long convicts remain in prison. 182 examples from Airbnb, the BBC, Citibank, ConEd, Facebook, Ford, Google, the IRS, LinkedIn, Match.com, MTV, Netflix, PayPal, Pfizer, Spotify, Uber, UPS, Wikipedia, and more. How does predictive analytics work? This jam-packed book satisfies by demystifying the intriguing science under the hood. For future hands-on practitioners pursuing a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more. A truly omnipresent science, predictive analytics constantly affects our daily lives. Whether you
Predictive Analytics: Parametric Models for Regression and Classification Using R (Wiley Series in Probability and Statistics)
by Ajit C. TamhaneProvides a foundation in classical parametric methods of regression and classification essential for pursuing advanced topics in predictive analytics and statistical learning This book covers a broad range of topics in parametric regression and classification including multiple regression, logistic regression (binary and multinomial), discriminant analysis, Bayesian classification, generalized linear models and Cox regression for survival data. The book also gives brief introductions to some modern computer-intensive methods such as classification and regression trees (CART), neural networks and support vector machines. The book is organized so that it can be used by both advanced undergraduate or masters students with applied interests and by doctoral students who also want to learn the underlying theory. This is done by devoting the main body of the text of each chapter with basic statistical methodology illustrated by real data examples. Derivations, proofs and extensions are relegated to the Technical Notes section of each chapter, Exercises are also divided into theoretical and applied. Answers to selected exercises are provided. A solution manual is available to instructors who adopt the text. Data sets of moderate to large sizes are used in examples and exercises. They come from a variety of disciplines including business (finance, marketing and sales), economics, education, engineering and sciences (biological, health, physical and social). All data sets are available at the book’s web site. Open source software R is used for all data analyses. R codes and outputs are provided for most examples. R codes are also available at the book’s web site. Predictive Analytics: Parametric Models for Regression and Classification Using R is ideal for a one-semester upper-level undergraduate and/or beginning level graduate course in regression for students in business, economics, finance, marketing, engineering, and computer science. It is also an excellent resource for practitioners in these fields.
Predictive Analytics for Mechanical Engineering: A Beginners Guide (SpringerBriefs in Applied Sciences and Technology)
by Parikshit N. Mahalle Pravin P. Hujare Gitanjali Rahul ShindeThis book focus on key component required for building predictive maintenance model. The current trend of Maintenance 4.0 leans towards the preventive mechanism enabled by predictive approach and condition-based smart maintenance. The intelligent decision support, earlier detection of spare part failure, fatigue detection is the main slices of intelligent and predictive maintenance system (PMS) leading towards Maintenance 4.0 This book presents prominent use cases of mechanical engineering using PMS along with the benefits. Basic understanding of data preparation is required for development of any AI application; in view of this, the types of the data and data preparation processes, and tools are also presented in this book.
Predictive Analytics for the Modern Enterprise: A Practitioner's Guide To Designing And Implementing Solutions
by Nooruddin Abbas AliThe surging predictive analytics market is expected to grow from $10.5 billion today to $28 billion by 2026. With the rise in automation across industries, the increase in data-driven decision-making, and the proliferation of IoT devices, predictive analytics has become an operational necessity in today's forward-thinking companies.If you're a data professional, you need to be aligned with your company's business activities more than ever before. This practical book provides the background, tools, and best practices necessary to help you design, implement, and operationalize predictive analytics on-premises or in the cloud. Explore ways that predictive analytics can provide direct input back to your businessUnderstand mathematical tools commonly used in predictive analyticsLearn the development frameworks used in predictive analytics applicationsAppreciate the role of predictive analytics in the machine learning processExamine industry implementations of predictive analyticsBuild, train, and retrain predictive models using Python and TensorFlow
Predictive Analytics in System Reliability (Springer Series in Reliability Engineering)
by Vijay Kumar Hoang PhamThis book provides engineers and researchers knowledge to help them in system reliability analysis using machine learning, artificial intelligence, big data, genetic algorithm, information theory, multi-criteria decision making, and other techniques. It will also be useful to students learning reliability engineering.The book brings readers up to date with how system reliability relates to the latest techniques of AI, big data, genetic algorithm, information theory, and multi-criteria decision making and points toward future developments in the subject.
Predictive Analytics of Psychological Disorders in Healthcare: Data Analytics on Psychological Disorders (Lecture Notes on Data Engineering and Communications Technologies #128)
by Mamta Mittal Lalit Mohan GoyalThis book discusses an interdisciplinary field which combines two major domains: healthcare and data analytics. It presents research studies by experts helping to fight discontent, distress, anxiety and unrealized potential by using mathematical models, machine learning, artificial intelligence, etc. and take preventive measures beforehand. Psychological disorders and biological abnormalities are significantly related with the applications of cognitive illnesses which has increased significantly in contemporary years and needs rapid investigation. The research content of this book is helpful for psychological undergraduates, health workers and their trainees, therapists, medical psychologists, and nurses.
Predictive Analytics Using Rattle and Qlik Sense
by Ferran Garcia PagansIf you are a business analyst who wants to understand how to improve your data analysis and how to apply predictive analytics, then this book is ideal for you. This book assumes you have some basic knowledge of statistics and a spreadsheet editor such as Excel, but knowledge of QlikView is not required.
Predictive Analytics with KNIME: Analytics for Citizen Data Scientists
by Frank AcitoThis book is about data analytics, including problem definition, data preparation, and data analysis. A variety of techniques (e.g., regression, logistic regression, cluster analysis, neural nets, decision trees, and others) are covered with conceptual background as well as demonstrations of KNIME using each tool. The book uses KNIME, which is a comprehensive, open-source software tool for analytics that does not require coding but instead uses an intuitive drag-and-drop workflow to create a network of connected nodes on an interactive canvas. KNIME workflows provide graphic representations of each step taken in analyses, making the analyses self-documenting. The graphical documentation makes it easy to reproduce analyses, as well as to communicate methods and results to others. Integration with R is also available in KNIME, and several examples using R nodes in a KNIME workflow are demonstrated for special functions and tools not explicitly included in KNIME.
Predictive and Simulation Analytics: Deeper Insights for Better Business Decisions
by Walter R. PaczkowskiThis book connects predictive analytics and simulation analytics, with the end goal of providing Rich Information to stakeholders in complex systems to direct data-driven decisions. Readers will explore methods for extracting information from data, work with simple and complex systems, and meld multiple forms of analytics for a more nuanced understanding of data science. The methods can be readily applied to business problems such as demand measurement and forecasting, predictive modeling, pricing analytics including elasticity estimation, customer satisfaction assessment, market research, new product development, and more. The book includes Python examples in Jupyter notebooks, available at the book's affiliated Github.This volume is intended for current and aspiring business data analysts, data scientists, and market research professionals, in both the private and public sectors.
Predictive Control: for Linear and Hybrid Systems
by Francesco Borrelli Alberto Bemporad Manfred MorariModel Predictive Control (MPC), the dominant advanced control approach in industry over the past twenty-five years, is presented comprehensively in this unique book. With a simple, unified approach, and with attention to real-time implementation, it covers predictive control theory including the stability, feasibility, and robustness of MPC controllers. The theory of explicit MPC, where the nonlinear optimal feedback controller can be calculated efficiently, is presented in the context of linear systems with linear constraints, switched linear systems, and, more generally, linear hybrid systems. Drawing upon years of practical experience and using numerous examples and illustrative applications, the authors discuss the techniques required to design predictive control laws, including algorithms for polyhedral manipulations, mathematical and multiparametric programming and how to validate the theoretical properties and to implement predictive control policies. The most important algorithms feature in an accompanying free online MATLAB toolbox, which allows easy access to sample solutions. Predictive Control for Linear and Hybrid Systems is an ideal reference for graduate, postgraduate and advanced control practitioners interested in theory and/or implementation aspects of predictive control.
Predictive Inference (Chapman And Hall/crc Monographs On Statistics And Applied Probability Ser. #55)
by Seymour GeisserThe author's research has been directed towards inference involving observables rather than parameters. In this book, he brings together his views on predictive or observable inference and its advantages over parametric inference. While the book discusses a variety of approaches to prediction including those based on parametric, nonparametric, and nonstochastic statistical models, it is devoted mainly to predictive applications of the Bayesian approach. It not only substitutes predictive analyses for parametric analyses, but it also presents predictive analyses that have no real parametric analogues. It demonstrates that predictive inference can be a critical component of even strict parametric inference when dealing with interim analyses. This approach to predictive inference will be of interest to statisticians, psychologists, econometricians, and sociologists.
Predictive Intelligence in Medicine: Second International Workshop, PRIME 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings (Lecture Notes in Computer Science #11843)
by Islem Rekik Ehsan Adeli Sang Hyun ParkThis book constitutes the proceedings of the Second International Workshop on Predictive Intelligence in Medicine, PRIME 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. The 18 papers presented in this volume were carefully reviewed and selected for inclusion in this book. The contributions describe new cutting-edge predictive models and methods that solve challenging problems in the medical field for a high-precision predictive medicine.
Predictive Statistics: Analysis And Inference Beyond Models (Cambridge Series In Statistical And Probabilistic Mathematics #46)
by Bertrand S. Clarke Jennifer L. ClarkeAll scientific disciplines prize predictive success. Conventional statistical analyses, however, treat prediction as secondary, instead focusing on modeling and hence estimation, testing, and detailed physical interpretation, tackling these tasks before the predictive adequacy of a model is established. <P><P>This book outlines a fully predictive approach to statistical problems based on studying predictors; the approach does not require predictors correspond to a model although this important special case is included in the general approach. Throughout, the point is to examine predictive performance before considering conventional inference. These ideas are traced through five traditional subfields of statistics, helping readers to refocus and adopt a directly predictive outlook. The book also considers prediction via contemporary 'black box' techniques and emerging data types and methodologies where conventional modeling is so difficult that good prediction is the main criterion available for evaluating the performance of a statistical method. Well-documented open-source R code in a Github repository allows readers to replicate examples and apply techniques to other investigations.<P> Connects statistical theory directly to the goals of machine learning, data mining, and modern applied science.<P> Positions statisticians to cope with emerging, non-traditional data types.<P> Well-documented R code in a Github repository allows readers to replicate examples.
Pregnancy Outcomes of Unmarried Women in Japan: From Abortion to Birth (SpringerBriefs in Population Studies)
by Yukiko SendaThis book provides a key to understanding why there was an increase in extra-marital fertility in Japan from the 1990s to the 2010s, particularly between 1995 and 2015, and the factors which contribute to the multistratification of unmarried mothers, the number of which has increased ensuingly. It also allows for international comparison by providing data on outcomes of extra-marital childbirth. Previously, it was believed that the idea of a ‘second demographic transition’ did not apply to Japan, which had a relatively low rate of extra-marital fertility. However, more recently, though still at a low level, a subtle but gradual rise is seen in the number of women who become unmarried mothers as a result of births outside marriage. This trend suggests that the social environment surrounding pregnancy, childbirth, and marriage is changing. In this book, various data such as national statistics, nationwide surveys, and media discourse are analysed with a view to revealing the factors affecting unmarried women’s decisions when they discover they are pregnant. Various matters are discussed, such as changes in sexual activity and contraceptive use, advance in reproductive technology, the law and government policies pertaining to adoption, social consciousness towards unwed mothers, the change in perception of abortion from the religious perspective, and difference of socioeconomic status depending on the women’s occupation. Facts from vital statistics are first laid out, showing that, while abortion has consistently been on the decrease from the 1990s onward, shotgun marriages have peaked out. Adoption is rare and remains very small in proportion, while extra-marital fertility is on the rise. The author then points to the possibility that greater lenience found in the social consciousness towards unwed mothers in recent years is a pull factor for the increase in extra-marital fertility. Further, by analysing vital statistics, it is revealed that the probability of becoming a mother without marrying changed with the woman’s occupation, explicable by the stability of employment and level of income, and that between 1995 and 2015, the effects of the job factor are changing. If we assume that, unlike the first demographic transition model, the ‘second demographic transition’ may show a similar direction but be on a different scale according to the country, it is possible to say that Japan too is experiencing the ‘second demographic transition’.
Prehistoric Warfare and Violence: Quantitative and Qualitative Approaches (Quantitative Methods in the Humanities and Social Sciences)
by Andrea Dolfini Rachel J. Crellin Christian Horn Marion UckelmannThis is the first book to explore prehistoric warfare and violence by integrating qualitative research methods with quantitative, scientific techniques of analysis such as paleopathology, morphometry, wear analysis, and experimental archaeology. It investigates early warfare and violence from the standpoint of four broad interdisciplinary themes: skeletal markers of violence and weapon training; conflict in prehistoric rock-art; the material culture of conflict; and intergroup violence in archaeological discourse. The book has a wide-ranging chronological and geographic scope, from early Neolithic to late Iron Age and from Western Europe to East Asia. It includes world-renowned sites and artefact collections such as the Tollense Valley Bronze Age battlefield (Germany), the UNESCO World Heritage Site at Tanum (Sweden), and the British Museum collection of bronze weaponry from the late Shang period (China). Original case studies are presented in each section by a diverse international authorship.The study of warfare and violence in prehistoric and pre-literate societies has been at the forefront of archaeological debate since the publication of Keeley’s provocative monograph ‘War Before Civilization’ (Oxford 1996). The problem has been approached from a number of standpoints including anthropological and behavioural studies of interpersonal violence, osteological examinations of sharp lesions and blunt-force traumas, wear analysis of ancient weaponry, and field experiments with replica weapons and armour. This research, however, is often confined within the boundaries of the various disciplines and specialist fields. In particular, a gap can often be detected between the research approaches grounded in the humanities and social sciences and those based on the archaeological sciences. The consequence is that, to this day, the subject is dominated by a number of undemonstrated assumptions regarding the nature of warfare, combat, and violence in non-literate societies. Moreover, important methodological questions remain unanswered: can we securely distinguish between violence-related and accidental trauma on skeletal remains? To what extent can wear analysis shed light on long-forgotten fighting styles? Can we design meaningful combat tests based on historic martial arts? And can the study of rock-art unlock the social realities of prehistoric warfare? By breaking the mould of entrenched subject boundaries, this edited volume promotes interdisciplinary debate in the study of prehistoric warfare and violence by presenting a number of innovative approaches that integrate qualitative and quantitative methods of research and analysis.