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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.
Forecasting by David Hendry,Jennifer Castle,Michael Clements Pdf
Concise, engaging, and highly intuitive—this accessible guide equips you with an understanding of all the basic principles of forecasting Making accurate predictions about the economy has always been difficult, as F. A. Hayek noted when accepting his Nobel Prize in economics, but today forecasters have to contend with increasing complexity and unpredictable feedback loops. In this accessible and engaging guide, David Hendry, Michael Clements, and Jennifer Castle provide a concise and highly intuitive overview of the process and problems of forecasting. They explain forecasting concepts including how to evaluate forecasts, how to respond to forecast failures, and the challenges of forecasting accurately in a rapidly changing world. Topics covered include: What is a forecast? How are forecasts judged? And how can forecast failure be avoided? Concepts are illustrated using real-world examples including financial crises, the uncertainty of Brexit, and the Federal Reserve’s record on forecasting. This is an ideal introduction for university students studying forecasting, practitioners new to the field and for general readers interested in how economists forecast.
Author : David F. Hendry,Neil R. Ericsson Publisher : MIT Press Page : 236 pages File Size : 49,9 Mb Release : 2003 Category : Business & Economics ISBN : 0262582422
Prediction, Projection and Forecasting by Thomas L. Saaty,Luis G. Vargas Pdf
We predict when we say in advance, foretell, or prophesy what is likely to happen in the future. We project when we calculate the numerical value associated with a future event. We forecast, a special kind of prediction, on data of past happenings to generate or cast data for future by relying happenings. Generally, one predicts (yes, no) a war, an earthquake or the outcome of a chess match, projects the value of the GNP or of unemployment, and forecasts the weather and, more scientifically, the economic trends. Prediction, projection, and forecasting must be constrained in time and space: when and where. Often the accuracy of a forecast is of interest along with how sensitive the outcome is to changes in the factors involved. Is there a basis for improving the wisdom we need to make correct and useful predictions? We believe there is, and that it can be cultivated by studying the approach given here along with the various examples. To the best of our knowledge, no other work has approached prediction in the scientific framework of hierarchies. Prediction is the synthesis of past and present in an attempt to foretell the future. In our view, creation is not the ultimate phenomenon of the world. Nature creates forms and so do we. The problem is to surmise the eventual purpose, impact, and use of creation. It is the synthesis or outcome of bringing together the results of creation that we need to predict.
How Does Weather Change? by Jennifer Boothroyd Pdf
Why is it sunny outside one day and rainy the next? Readers will learn the ins and outs of why weather changes in this book. Accessible text and appealing photos show changing weather conditions and encourage students to observe and think about the changing weather in their own environments.
Forecasting the future with advanced data models and visualizations. To envision and create the futures we want, society needs an appropriate understanding of the likely impact of alternative actions. Data models and visualizations offer a way to understand and intelligently manage complex, interlinked systems in science and technology, education, and policymaking. Atlas of Forecasts, from the creator of Atlas of Science and Atlas of Knowledge, shows how we can use data to predict, communicate, and ultimately attain desirable futures. Using advanced data visualizations to introduce different types of computational models, Atlas of Forecasts demonstrates how models can inform effective decision-making in education, science, technology, and policymaking. The models and maps presented aim to help anyone understand key processes and outcomes of complex systems dynamics, including which human skills are needed in an artificial intelligence-empowered economy; what progress in science and technology is likely to be made; and how policymakers can future-proof regions or nations. This Atlas offers a driver's seat-perspective for a test-drive of the future.
Economic Forecasting by Graham Elliott,Allan Timmermann Pdf
A comprehensive and integrated approach to economic forecasting problems Economic forecasting involves choosing simple yet robust models to best approximate highly complex and evolving data-generating processes. This poses unique challenges for researchers in a host of practical forecasting situations, from forecasting budget deficits and assessing financial risk to predicting inflation and stock market returns. Economic Forecasting presents a comprehensive, unified approach to assessing the costs and benefits of different methods currently available to forecasters. This text approaches forecasting problems from the perspective of decision theory and estimation, and demonstrates the profound implications of this approach for how we understand variable selection, estimation, and combination methods for forecasting models, and how we evaluate the resulting forecasts. Both Bayesian and non-Bayesian methods are covered in depth, as are a range of cutting-edge techniques for producing point, interval, and density forecasts. The book features detailed presentations and empirical examples of a range of forecasting methods and shows how to generate forecasts in the presence of large-dimensional sets of predictor variables. The authors pay special attention to how estimation error, model uncertainty, and model instability affect forecasting performance. Presents a comprehensive and integrated approach to assessing the strengths and weaknesses of different forecasting methods Approaches forecasting from a decision theoretic and estimation perspective Covers Bayesian modeling, including methods for generating density forecasts Discusses model selection methods as well as forecast combinations Covers a large range of nonlinear prediction models, including regime switching models, threshold autoregressions, and models with time-varying volatility Features numerous empirical examples Examines the latest advances in forecast evaluation Essential for practitioners and students alike
This handbook summarises knowledge from experts and empirical studies. It provides guidelines that can be applied in fields such as economics, sociology, and psychology. Includes a comprehensive forecasting dictionary.
Author : John B. Guerard, Jr. Publisher : Springer Science & Business Media Page : 245 pages File Size : 52,7 Mb Release : 2013-01-04 Category : Business & Economics ISBN : 9781461452393
Introduction to Financial Forecasting in Investment Analysis by John B. Guerard, Jr. Pdf
Forecasting—the art and science of predicting future outcomes—has become a crucial skill in business and economic analysis. This volume introduces the reader to the tools, methods, and techniques of forecasting, specifically as they apply to financial and investing decisions. With an emphasis on "earnings per share" (eps), the author presents a data-oriented text on financial forecasting, understanding financial data, assessing firm financial strategies (such as share buybacks and R&D spending), creating efficient portfolios, and hedging stock portfolios with financial futures. The opening chapters explain how to understand economic fluctuations and how the stock market leads the general economic trend; introduce the concept of portfolio construction and how movements in the economy influence stock price movements; and introduce the reader to the forecasting process, including exponential smoothing and time series model estimations. Subsequent chapters examine the composite index of leading economic indicators (LEI); review financial statement analysis and mean-variance efficient portfolios; and assess the effectiveness of analysts’ earnings forecasts. Using data from such firms as Intel, General Electric, and Hitachi, Guerard demonstrates how forecasting tools can be applied to understand the business cycle, evaluate market risk, and demonstrate the impact of global stock selection modeling and portfolio construction.
What does it mean to forecast the weather? How do weather forecasters do their jobs? Readers will learn the ins and outs of weather forecasting in this book. Accessible text and appealing photos show forecasters at work and encourage students to think about how forecasting works and what weather forecasts mean to them.
Statistical Postprocessing of Ensemble Forecasts by Stéphane Vannitsem,Daniel S. Wilks,Jakob Messner Pdf
Statistical Postprocessing of Ensemble Forecasts brings together chapters contributed by international subject-matter experts describing the current state of the art in the statistical postprocessing of ensemble forecasts. The book illustrates the use of these methods in several important applications including weather, hydrological and climate forecasts, and renewable energy forecasting. After an introductory section on ensemble forecasts and prediction systems, the second section of the book is devoted to exposition of the methods available for statistical postprocessing of ensemble forecasts: univariate and multivariate ensemble postprocessing are first reviewed by Wilks (Chapters 3), then Schefzik and Möller (Chapter 4), and the more specialized perspective necessary for postprocessing forecasts for extremes is presented by Friederichs, Wahl, and Buschow (Chapter 5). The second section concludes with a discussion of forecast verification methods devised specifically for evaluation of ensemble forecasts (Chapter 6 by Thorarinsdottir and Schuhen). The third section of this book is devoted to applications of ensemble postprocessing. Practical aspects of ensemble postprocessing are first detailed in Chapter 7 (Hamill), including an extended and illustrative case study. Chapters 8 (Hemri), 9 (Pinson and Messner), and 10 (Van Schaeybroeck and Vannitsem) discuss ensemble postprocessing specifically for hydrological applications, postprocessing in support of renewable energy applications, and postprocessing of long-range forecasts from months to decades. Finally, Chapter 11 (Messner) provides a guide to the ensemble-postprocessing software available in the R programming language, which should greatly help readers implement many of the ideas presented in this book. Edited by three experts with strong and complementary expertise in statistical postprocessing of ensemble forecasts, this book assesses the new and rapidly developing field of ensemble forecast postprocessing as an extension of the use of statistical corrections to traditional deterministic forecasts. Statistical Postprocessing of Ensemble Forecasts is an essential resource for researchers, operational practitioners, and students in weather, seasonal, and climate forecasting, as well as users of such forecasts in fields involving renewable energy, conventional energy, hydrology, environmental engineering, and agriculture. Consolidates, for the first time, the methodologies and applications of ensemble forecasts in one succinct place Provides real-world examples of methods used to formulate forecasts Presents the tools needed to make the best use of multiple model forecasts in a timely and efficient manner
Financial Risk Forecasting is a complete introduction to practical quantitative risk management, with a focus on market risk. Derived from the authors teaching notes and years spent training practitioners in risk management techniques, it brings together the three key disciplines of finance, statistics and modeling (programming), to provide a thorough grounding in risk management techniques. Written by renowned risk expert Jon Danielsson, the book begins with an introduction to financial markets and market prices, volatility clusters, fat tails and nonlinear dependence. It then goes on to present volatility forecasting with both univatiate and multivatiate methods, discussing the various methods used by industry, with a special focus on the GARCH family of models. The evaluation of the quality of forecasts is discussed in detail. Next, the main concepts in risk and models to forecast risk are discussed, especially volatility, value-at-risk and expected shortfall. The focus is both on risk in basic assets such as stocks and foreign exchange, but also calculations of risk in bonds and options, with analytical methods such as delta-normal VaR and duration-normal VaR and Monte Carlo simulation. The book then moves on to the evaluation of risk models with methods like backtesting, followed by a discussion on stress testing. The book concludes by focussing on the forecasting of risk in very large and uncommon events with extreme value theory and considering the underlying assumptions behind almost every risk model in practical use – that risk is exogenous – and what happens when those assumptions are violated. Every method presented brings together theoretical discussion and derivation of key equations and a discussion of issues in practical implementation. Each method is implemented in both MATLAB and R, two of the most commonly used mathematical programming languages for risk forecasting with which the reader can implement the models illustrated in the book. The book includes four appendices. The first introduces basic concepts in statistics and financial time series referred to throughout the book. The second and third introduce R and MATLAB, providing a discussion of the basic implementation of the software packages. And the final looks at the concept of maximum likelihood, especially issues in implementation and testing. The book is accompanied by a website - www.financialriskforecasting.com – which features downloadable code as used in the book.
Superforecasting by Philip E. Tetlock,Dan Gardner Pdf
NEW YORK TIMES BESTSELLER • NAMED ONE OF THE BEST BOOKS OF THE YEAR BY THE ECONOMIST “The most important book on decision making since Daniel Kahneman's Thinking, Fast and Slow.”—Jason Zweig, The Wall Street Journal Everyone would benefit from seeing further into the future, whether buying stocks, crafting policy, launching a new product, or simply planning the week’s meals. Unfortunately, people tend to be terrible forecasters. As Wharton professor Philip Tetlock showed in a landmark 2005 study, even experts’ predictions are only slightly better than chance. However, an important and underreported conclusion of that study was that some experts do have real foresight, and Tetlock has spent the past decade trying to figure out why. What makes some people so good? And can this talent be taught? In Superforecasting, Tetlock and coauthor Dan Gardner offer a masterwork on prediction, drawing on decades of research and the results of a massive, government-funded forecasting tournament. The Good Judgment Project involves tens of thousands of ordinary people—including a Brooklyn filmmaker, a retired pipe installer, and a former ballroom dancer—who set out to forecast global events. Some of the volunteers have turned out to be astonishingly good. They’ve beaten other benchmarks, competitors, and prediction markets. They’ve even beaten the collective judgment of intelligence analysts with access to classified information. They are "superforecasters." In this groundbreaking and accessible book, Tetlock and Gardner show us how we can learn from this elite group. Weaving together stories of forecasting successes (the raid on Osama bin Laden’s compound) and failures (the Bay of Pigs) and interviews with a range of high-level decision makers, from David Petraeus to Robert Rubin, they show that good forecasting doesn’t require powerful computers or arcane methods. It involves gathering evidence from a variety of sources, thinking probabilistically, working in teams, keeping score, and being willing to admit error and change course. Superforecasting offers the first demonstrably effective way to improve our ability to predict the future—whether in business, finance, politics, international affairs, or daily life—and is destined to become a modern classic.
Positive feedback--when A produces B, which in turn produces even more A--drives not only abrupt climate changes, but also disruptive events in economics and finance, from asset bubbles to debt crises, bank runs, even corporate corruption. But economists, with few exceptions, have ignored this reality for fifty years, holding on to the unreasonable belief in the wisdom of the market. It's past time to be asking how markets really work. Can we replace economic magical thinking with a better means of predicting what the financial future holds, in order to prepare for--or even avoid--the next extreme economic event? Here, physicist and acclaimed science writer Mark Buchanan answers these questions and more in a master lesson on a smarter economics, which accepts that markets act much like weather. Market instability is as natural--and dangerous--as a prairie twister. With Buchanan's help, perhaps we can better govern the markets and weather their storms.
