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Article
Publication date: 3 April 2017

Graham Kendall, Angelina Yee and Steven Hardy

The purpose of this paper is to support the use of unique identifiers for the authors of scientific publications. This, the authors believe, aligns with the views of many…

Abstract

Purpose

The purpose of this paper is to support the use of unique identifiers for the authors of scientific publications. This, the authors believe, aligns with the views of many others, as it would solve the problem of author disambiguation. If every researcher had a unique identifier, there would be significant opportunities to provide even more services. These extensions are proposed in this paper.

Design/methodology/approach

The authors discuss the bibliographic services that are currently available. This leads to a discussion of how these services could be developed and extended.

Findings

The authors suggest a number of ways that a unique identifier for scientific authors could support many other areas of importance to the scientific community. This will provide a much more robust system that provides a much richer and more easily maintained, scientific environment.

Originality/value

The scientific community lags behind most other communities with regard to the way it identifies individuals. Even if the current vision for a unique identifier for authors was to become more widespread, there would still be many areas where the community could improve its operations. This viewpoint paper suggests some of these, along with a financial model that could underpin the functionality.

Details

The Electronic Library, vol. 35 no. 2
Type: Research Article
ISSN: 0264-0473

Keywords

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Book part
Publication date: 1 January 2004

Artificial intelligence is a consortium of data-driven methodologies which includes artificial neural networks, genetic algorithms, fuzzy logic, probabilistic belief…

Abstract

Artificial intelligence is a consortium of data-driven methodologies which includes artificial neural networks, genetic algorithms, fuzzy logic, probabilistic belief networks and machine learning as its components. We have witnessed a phenomenal impact of this data-driven consortium of methodologies in many areas of studies, the economic and financial fields being of no exception. In particular, this volume of collected works will give examples of its impact on the field of economics and finance. This volume is the result of the selection of high-quality papers presented at a special session entitled “Applications of Artificial Intelligence in Economics and Finance” at the “2003 International Conference on Artificial Intelligence” (IC-AI ’03) held at the Monte Carlo Resort, Las Vegas, NV, USA, June 23–26 2003. The special session, organised by Jane Binner, Graham Kendall and Shu-Heng Chen, was presented in order to draw attention to the tremendous diversity and richness of the applications of artificial intelligence to problems in Economics and Finance. This volume should appeal to economists interested in adopting an interdisciplinary approach to the study of economic problems, computer scientists who are looking for potential applications of artificial intelligence and practitioners who are looking for new perspectives on how to build models for everyday operations.

Details

Applications of Artificial Intelligence in Finance and Economics
Type: Book
ISBN: 978-1-84950-303-7

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Book part
Publication date: 1 January 2004

Jane M. Binner, Graham Kendall and Alicia Gazely

This work applies state-of-the-art artificial intelligence forecasting methods to provide new evidence of the comparative performance of statistically weighted Divisia…

Abstract

This work applies state-of-the-art artificial intelligence forecasting methods to provide new evidence of the comparative performance of statistically weighted Divisia indices vis-à-vis their simple sum counterparts in a simple inflation forecasting experiment. We develop a new approach that uses co-evolution (using neural networks and evolutionary strategies) as a predictive tool. This approach is simple to implement yet produces results that outperform stand-alone neural network predictions. Results suggest that superior tracking of inflation is possible for models that employ a Divisia M2 measure of money that has been adjusted to incorporate a learning mechanism to allow individuals to gradually alter their perceptions of the increased productivity of money. Divisia measures of money outperform their simple sum counterparts as macroeconomic indicators.

Details

Applications of Artificial Intelligence in Finance and Economics
Type: Book
ISBN: 978-1-84950-303-7

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Book part
Publication date: 1 January 2004

Abstract

Details

Applications of Artificial Intelligence in Finance and Economics
Type: Book
ISBN: 978-1-84950-303-7

To view the access options for this content please click here
Book part
Publication date: 1 January 2004

Abstract

Details

Applications of Artificial Intelligence in Finance and Economics
Type: Book
ISBN: 978-1-84950-303-7

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Book part
Publication date: 1 January 2004

Sam Mirmirani and Hsi Cheng Li

This study applies VAR and ANN techniques to make ex-post forecast of U.S. oil price movements. The VAR-based forecast uses three endogenous variables: lagged oil price…

Abstract

This study applies VAR and ANN techniques to make ex-post forecast of U.S. oil price movements. The VAR-based forecast uses three endogenous variables: lagged oil price, lagged oil supply and lagged energy consumption. However, the VAR model suggests that the impacts of oil supply and energy consumption has limited impacts on oil price movement. The forecast of the genetic algorithm-based ANN model is made by using oil supply, energy consumption, and money supply (M1). Root mean squared error and mean absolute error have been used as the evaluation criteria. Our analysis suggests that the BPN-GA model noticeably outperforms the VAR model.

Details

Applications of Artificial Intelligence in Finance and Economics
Type: Book
ISBN: 978-1-84950-303-7

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Book part
Publication date: 1 January 2004

Jessica Lin and Eamonn Keogh

Given the recent explosion of interest in streaming data and online algorithms, clustering of time series subsequences has received much attention. In this work we make a…

Abstract

Given the recent explosion of interest in streaming data and online algorithms, clustering of time series subsequences has received much attention. In this work we make a surprising claim. Clustering of time series subsequences is completely meaningless. More concretely, clusters extracted from these time series are forced to obey a certain constraint that is pathologically unlikely to be satisfied by any dataset, and because of this, the clusters extracted by any clustering algorithm are essentially random. While this constraint can be intuitively demonstrated with a simple illustration and is simple to prove, it has never appeared in the literature. We can justify calling our claim surprising, since it invalidates the contribution of dozens of previously published papers. We will justify our claim with a theorem, illustrative examples, and a comprehensive set of experiments on reimplementations of previous work.

Details

Applications of Artificial Intelligence in Finance and Economics
Type: Book
ISBN: 978-1-84950-303-7

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Book part
Publication date: 1 January 2004

Nathan Lael Joseph, David S. Brée and Efstathios Kalyvas

Are the learning procedures of genetic algorithms (GAs) able to generate optimal architectures for artificial neural networks (ANNs) in high frequency data? In this…

Abstract

Are the learning procedures of genetic algorithms (GAs) able to generate optimal architectures for artificial neural networks (ANNs) in high frequency data? In this experimental study, GAs are used to identify the best architecture for ANNs. Additional learning is undertaken by the ANNs to forecast daily excess stock returns. No ANN architectures were able to outperform a random walk, despite the finding of non-linearity in the excess returns. This failure is attributed to the absence of suitable ANN structures and further implies that researchers need to be cautious when making inferences from ANN results that use high frequency data.

Details

Applications of Artificial Intelligence in Finance and Economics
Type: Book
ISBN: 978-1-84950-303-7

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Book part
Publication date: 1 January 2004

Vincent A. Schmidt and Jane M. Binner

Divisia component data is used in the training of an Aggregate Feedforward Neural Network (AFFNN), a general-purpose connectionist system designed to assist with data…

Abstract

Divisia component data is used in the training of an Aggregate Feedforward Neural Network (AFFNN), a general-purpose connectionist system designed to assist with data mining activities. The neural network is able to learn the money-price relationship, defined as the relationships between the rate of growth of the money supply and inflation. Learned relationships are expressed in terms of an automatically generated series of human-readable and machine-executable rules, shown to meaningfully and accurately describe inflation in terms of the original values of the Divisia component dataset.

Details

Applications of Artificial Intelligence in Finance and Economics
Type: Book
ISBN: 978-1-84950-303-7

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Book part
Publication date: 1 January 2004

Ian D. Wilson, Antonia J. Jones, David H. Jenkins and J.A. Ware

In this paper we show, by means of an example of its application to the problem of house price forecasting, an approach to attribute selection and dependence modelling…

Abstract

In this paper we show, by means of an example of its application to the problem of house price forecasting, an approach to attribute selection and dependence modelling utilising the Gamma Test (GT), a non-linear analysis algorithm that is described. The GT is employed in a two-stage process: first the GT drives a Genetic Algorithm (GA) to select a useful subset of features from a large dataset that we develop from eight economic statistical series of historical measures that may impact upon house price movement. Next we generate a predictive model utilising an Artificial Neural Network (ANN) trained to the Mean Squared Error (MSE) estimated by the GT, which accurately forecasts changes in the House Price Index (HPI). We present a background to the problem domain and demonstrate, based on results of this methodology, that the GT was of great utility in facilitating a GA based approach to extracting a sound predictive model from a large number of inputs in a data-point sparse real-world application.

Details

Applications of Artificial Intelligence in Finance and Economics
Type: Book
ISBN: 978-1-84950-303-7

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