Author: W.Allen Spivey and William W. WeckerPublisher:
ISBN:
Category :
Languages : en
Pages : 14
Book Description
A Nonparametric approach to the construction of prediction intervals for time series forecasts.Working Paper No.63
Author: W.Allen Spivey and William W. WeckerPublisher:
ISBN:
Category :
Languages : en
Pages : 14
Book Description
A Nonparametric Approach to the Construction of Prediction Intervals for Time Series Forecasts
Author: W. Allen SpiveyTime-Series Forecasting
Author: Chris ChatfieldPublisher: CRC Press
ISBN: 1420036203
Category : Business & Economics
Languages : en
Pages : 281
Book Description
From the author of the bestselling "Analysis of Time Series," Time-Series Forecasting offers a comprehensive, up-to-date review of forecasting methods. It provides a summary of time-series modelling procedures, followed by a brief catalogue of many different time-series forecasting methods, ranging from ad-hoc methods through ARIMA and state-space
Time Series and Forecasting
Author: Bruce L. BowermanPublisher: Brooks/Cole
ISBN:
Category : Mathematics
Languages : en
Pages : 504
Book Description
Forecasting and multiple regression analysis; Forecasting time series described by trend and irregular components; Forecasting seasonal time series; The box-jenkins methodology.
Forecasting and Time Series
Author: Bruce L. BowermanPublisher: South Western Educational Publishing
ISBN:
Category : Business & Economics
Languages : en
Pages : 746
Book Description
This comprehensive book introduces students to time series and forecasting techniques. The prerequisites are college algebra and basic statistics. It contains complete coverage of linear regression analysis, which provides much of the conceptual foundation of forecasting.
Advances in Time Series Forecasting
Author: Cagdas Hakan AladagPublisher: Bentham Science Publishers
ISBN: 1608053733
Category : Mathematics
Languages : en
Pages : 143
Book Description
"Time series analysis is applicable in a variety of disciplines such as business administration, economics, public finances, engineering, statistics, econometrics, mathematics and actuarial sciences. Forecasting the future assists in critical organizationa"
Nonlinear Time Series
Author: Jianqing FanPublisher: Springer Science & Business Media
ISBN: 0387693955
Category : Mathematics
Languages : en
Pages : 565
Book Description
This is the first book that integrates useful parametric and nonparametric techniques with time series modeling and prediction, the two important goals of time series analysis. Such a book will benefit researchers and practitioners in various fields such as econometricians, meteorologists, biologists, among others who wish to learn useful time series methods within a short period of time. The book also intends to serve as a reference or text book for graduate students in statistics and econometrics.
A Combined Parametric and Nonparametric Approach to Time Series Analysis
Author: Stefan KriebolPublisher: IOS Press
ISBN: 9781586031206
Category : Mathematics
Languages : en
Pages : 150
Book Description
The analysis and prediction of natural phenomena is an interesting and challenging task. Time series obtained from the observation of one or more features of a phenomenon are often the only access to the data generating system. Unfortunately, time series analysis is usually done by specialists in the field of the phenomenon with traditional analysis techniques. The application of modern analysis and prediction tools is often avoided due to their complexity or the risk of failure. This issue can be surmounted by an interdisciplinary approach. This work is an example for the possible synergetic effect of interdisciplinary research. In the field of oceanography the coastal upwelling phenomenon is analysed in experimental studies with a numerical model in order to develop a parametric prediction model. Artificial neural networks seem to be a suitable parametric model. However, in the field of computer science traditional artificial neural techniques showed limitations in the analysis and prediction of time series obtained from natural phenomena, particularly with nonlinear and nonstationary time series. Motivated by this limitations a new approach to time series analysis and prediction is presented in this work, the mixture of nonparametric segmented experts (MONSE). The MONSE approach is exploiting the synergetic effect of a combined nonparametric and parametric analysis. It is supposed to be applied to explorative time series analysis and prediction in various fields, i.e. in a context where hardly any kowledge about the time series of concern is available.
Time Series Analysis
Author: Chun-Kit NganPublisher: BoD – Books on Demand
ISBN: 1789847788
Category : Mathematics
Languages : en
Pages : 131
Book Description
This book aims to provide readers with the current information, developments, and trends in a time series analysis, particularly in time series data patterns, technical methodologies, and real-world applications. This book is divided into three sections and each section includes two chapters. Section 1 discusses analyzing multivariate and fuzzy time series. Section 2 focuses on developing deep neural networks for time series forecasting and classification. Section 3 describes solving real-world domain-specific problems using time series techniques. The concepts and techniques contained in this book cover topics in time series research that will be of interest to students, researchers, practitioners, and professors in time series forecasting and classification, data analytics, machine learning, deep learning, and artificial intelligence.
Nonlinear Time Series and the Stationary Bootstrap
Author: Publisher:
ISBN:
Category :
Languages : en
Pages : 48
Book Description
Time series analysis is used in numerous fields. Time series data is everywhere, from economics, statistics, biology, and beyond. Most often, we wish to make predictions or forecasts for future events. We would ultimately like to know what would happen in the future, given all the information seen in the past. Methods for analyzing forecasts and creating prediction intervals for linear time series analysis are well known. However, there is a need to develop methods to construct prediction intervals for non-linear time series. Non-linear time series data is generated from the Rössler system, and the nonparametric method of thin plate splines is used to model the time series. This thesis examines the use, and presents the results of, forecasts and corresponding prediction intervals based on the stationary bootstrap.