The Detection of Turning-points in "noisy" Time-series (with Particular Reference to Share-price Time-series) PDF Download
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Author: David John Smith
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: David John Smith
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Marc Wildi
Publisher: Springer
ISBN: 9783540229353
Category : Business & Economics
Languages : en
Pages : 279
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Book Description
The material contained in this book originated in interrogations about modern practice in time series analysis. • Why do we use models optimized with respect to one-step ahead foreca- ing performances for applications involving multi-step ahead forecasts? • Why do we infer 'long-term' properties (unit-roots) of an unknown process from statistics essentially based on short-term one-step ahead forecasting performances of particular time series models? • Are we able to detect turning-points of trend components earlier than with traditional signal extraction procedures? The link between 'signal extraction' and the first two questions above is not immediate at first sight. Signal extraction problems are often solved by su- ably designed symmetric filters. Towards the boundaries (t = 1 or t = N) of a time series a particular symmetric filter must be approximated by asymm- ric filters. The time series literature proposes an intuitively straightforward solution for solving this problem: • Stretch the observed time series by forecasts generated by a model. • Apply the symmetric filter to the extended time series. This approach is called 'model-based'. Obviously, the forecast-horizon grows with the length of the symmetric filter. Model-identification and estimation of unknown parameters are then related to the above first two questions. One may further ask, if this approximation problem and the way it is solved by model-based approaches are important topics for practical purposes? Consider some 'prominent' estimation problems: • The determination of the seasonally adjusted actual unemployment rate.
Author: Jeffrey A. Cuddy
Publisher: Windsor Books/Probus
ISBN: 9780930233617
Category : Business & Economics
Languages : en
Pages : 176
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Book Description
This "first of its kind" unique trading book introduces the author's specialized turning point detection method, known as PAMA--the Pivotal Area of Market Analysis. Using a small but powerful arsenal of new technical techniques, PAMA monitors the market daily, then steps up its analytical focus when a potential turn zone is detected. Jeff Cuddy's presentation of the PAMA method is laid out in precisely the same sequence as his original creation (and subsequent application) of this brilliant trading idea.
Author: Andrew Harvey
Publisher: OUP Oxford
ISBN: 019151554X
Category : Business & Economics
Languages : en
Pages : 472
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This volume presents a collection of readings which give the reader an idea of the nature and scope of unobserved components (UC) models and the methods used to deal with them. The book is intended to give a self-contained presentation of the methods and applicative issues. Harvey has made major contributions to this field and provides substantial introductions throughout the book to form a unified view of the literature. - ;This book presents a collection of readings which give the reader an idea of the nature and scope of unobserved components (UC) models and the methods used to deal with them. It contains four parts, three of which concern recent theoretical developments in classical and Bayesian estimation of linear, nonlinear, and non Gaussian UC models, signal extraction and testing, and one is devoted to selected econometric applications. The first part focuses on the linear state space model; the readings provide insight on prediction theory, signal extraction, and likelihood inference for non stationary and non invertible processes, diagnostic checking, and the use of state space methods for spline smoothing. Part II deals with applications of linear UC models to various estimation problems concerning economic time series, such as trend-cycle decompositions, seasonal adjustment, and the modelling of the serial correlation induced by survey sample design. The issues involved in testing in linear UC models are the theme of part III, which considers tests concerned with whether or not certain variance parameters are zero, with special reference to stationarity tests. Finally, part IV is devoted to the advances concerning classical and Bayesian inference for non linear and non Gaussian state space models, an area that has been evolving very rapidly during the last decade, paralleling the advances in computational inference using stochastic simulation techniques. The book is intended to give a relatively self-contained presentation of the methods and applicative issues. For this purpose, each part comes with an introductory chapter by the editors that provides a unified view of the literature and the many important developments that have occurred in the last years. -
Author:
Publisher:
ISBN:
Category : CUSUM technique
Languages : en
Pages :
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Author: Brian Millard
Publisher: Harriman House Limited
ISBN: 187185704X
Category : Business & Economics
Languages : en
Pages : 302
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Like the work of J. M. Hurst, Millard's forbear, Future Trends "shows what is possible when approaching the markets with a measured, logical technique based on firm mathematical and scientific logic".
Author: Stefan Schipper
Publisher:
ISBN:
Category : Securities
Languages : en
Pages : 152
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Author: Marco Corazza
Publisher: World Scientific
ISBN: 1800615221
Category : Computers
Languages : en
Pages : 429
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We wrote this book to help financial experts and investors to understand the state of the art of artificial intelligence and machine learning in finance. But first, what is artificial intelligence? The foundations of artificial intelligence lie in the human desire to automate. Often this desire has had foundations in grand civilization-defining visions or economic needs, such as the Antikythera mechanism, circa 200 BCE. Considered to be the oldest known example of an analog computer, it is thought that the mechanism automated the prediction of the positions of the sun, the moon, and the planets to assist in navigation.No matter the specific industry or application, AI has become a new engine of growth. Both finance and banking have been leveraging AI technologies and algorithms, applying them to automate routine tasks, procedures and forecasting, thereby improving overall customer experience.The topics covered in this book make it an invaluable resource for academics, researchers, policymakers, and practitioners alike who want to understand how AI has affected the banking and financial industries and how it will continue to change them in the years to come.
Author: C. Lewis
Publisher:
ISBN:
Category :
Languages : en
Pages : 32
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Author: Ashok Narain Srivastava
Publisher:
ISBN:
Category : Signal processing
Languages : en
Pages : 204
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