1,650 research outputs found
Tools for non-linear time series forecasting in economics - an empirical comparison of regime switching vector autoregressive models and recurrent neural networks
No full textThe purpose of this study is to contrast the forecasting performance of two non-linear models, a regime-switching vector autoregressive model (RS-VAR) and a recurrent neu-ral network (RNN), to that of a linear benchmark VAR model. Our specific forecasting experiment is UK inflation and we utilize monthly data from 1969-2003. The RS-VAR and the RNN perform approximately on par over both monthly and annual forecast hori-zons. Both non-linear models perform significantly better than the VAR model. Keywords: Inflation forecasting, regime-switching vector autoregressive model, recurrent neural network
SEARCHING FOR DIVISIA/INFLATION RELATIONSHIPS WITH THE AGGREGATE FEEDFORWARD NEURAL NETWORK
No full textDivisia 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.</p
Quantitative decision-making rules for the next generation of smarter evacuations
No full textIn this chapter we discuss the mathematical modelling of the next generation of smarter evacuations. Alongside a burgeoning literature on resilience we formulate a quantitative decision-making framework through which Social Media can be used to deliver more efficient evacuations. Our approach is flexible and improves upon existing models by allowing incoming information to be incorporated sequentially. Further, our model is the first of its kind to consider the effects of information quality (including abuse) and over-crowding upon network systems. In a high-quality information regime the potential benefits of Social Media increase as the size of the potential delays increases. Simulation results show that by not using updated information, as proposed in this study, final evacuation times are increased by 20% and in some cases can be more than doubled. In a low-quality regime Social Media provides noisy information and other alternatives—including random allocation strategies and peer-to-peer communication—may be more effective.</p
Co-evolving neural networks with evolutionary strategies: a new application to Divisia money
No full textThis 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.</p
Biography of Mary Jane Oliver
No full textTypescript of a sketch biography about Mary Jane (Oliver) Barlow, who came came from England around 1851 and with her husband, Oswald Barlow, helped to settle Saint George. Author unknown, but copied on January 13, 1937 by Virginia M. Lee of the Federal Writers Project, WPA, at Ogden, Uta
The light of the eye : doctrine, piety and reform in the works of Thomas Sherlock, Hannah More and Jane Austen
No full textBibliography: leaves 376-401.This thesis investigates the ways in which three eighteenth-century writers, Bishop Thomas Sherlock, Hannah More and Jane Austen embody orthodox Anglican doctrine according to their individual perceptions of the enlightening properties of Protestant Christianity. After situating them in their respective gender, literary and ecclesiastical contexts, I examine some of their key doctrines and analyse excerpts from their works. My selection of passages from Sherlock's works is fairly comprehensive, but in the case of More and Austen, where there is already a formidable body of literary criticism, it is more selective. Thus, I focus on doctrine in More's tracts, Strictures on the System of Female Education, An Essay on St Paul and most especially Coelebs in Search of a Wife and in the case of Austen, on her prayers and select passages from Sense and Sensibility and Mansfield Park. I conclude that, although diverse in their particular kind of Anglicanism (High, Evangelical and Median) and in their choice of genre, transparency or obscurity (anonymity and pseudonymity) and the various narratological strategies some of them invoke to circumvent certain taboos, Sherlock, More and Austen champion the same central orthodox doctrines, defend them against current alternatives to orthodoxy such as Latitudinarianism, Deism and various forms of Freethinking, and promote similar moral and ecclesiastical reforms. However, indirectly (through female characters who resist male representation or control) the women writers subject their ostensibly authorially-endorsed male narrators/characters to scrutiny and sometimes (when the males objectify the women) subversion
Applications of artificial intelligence in finance and economics
No full textStatistical analysis of genetic algorithms in discovering technical trading strategies / Chueh-Yung Tsao, Shu-Heng Chen -- Co-evolving neural networks with evolutionary strategies : a new application to divisia money / Jane M. Binner, Graham Kendall, Alicia Gazely -- Forecasting the EMU inflation rate : linear econometric vs. non-linear computational models using genetic neural fuzzy systems / Stefan Kooths, Timo Mitze, Eric Ringhut -- Finding or not finding rules in time series / Jessica Lin, Eamonn Keogh -- A comparison of var and neural networks with genetic algorithm in forecasting price of oil / Sam Mirmirani, Hsi Cheng Li -- Searching for divisia/inflation relationships with the aggregate feedforward neural network / Vincent A. Schmidt, Jane M. Binner -- Predicting housing value : genetic algorithm attribute selection and dependence modelling utilising the gamma test / Ian D. Wilson, Antonia J. Jones, David H. Jenkins, J.A. Ware -- A genetic programming approach to model international short-term capital flow / Tina Yu, Shu-Heng Chen, Tzu-Wen Kuo -- Tools for non-linear time series forecasting in economics : an empirical comparison of regime switching vector autoregressive models and recurrent neural networks / Jane M. Binner, Thomas Elger, Birger Nilsson, Jonathan A. Tepper -- Using non-parametric search algorithms to forecast daily excess stock returns / Nathan Lael Joseph, David S. Bre, Efstathios KalyvasArtificial 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, Nevada, 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. There are still many important Artificial Intelligence disciplines yet to be covered. Among them are the methodologies of independent component analysis, reinforcement learning, inductive logical programming, classifier systems and Bayesian networks, not to mention many ongoing and highly fascinating hybrid systems. A way to make up for their omission is to visit this subject again later. We certainly hope that we can do so in the near future with another volume of Applications of Artificial Intelligence in Economics and Financ
City evacuations an interdisciplinary approach
No full textEvacuating a city is a complex problem that involves issues of governance, preparedness education, warning, information sharing, population dynamics, resilience and recovery. As natural and anthropogenic threats to cities grow, it is an increasingly pressing problem for policy makers and practitioners. The book is the result of a unique interdisciplinary collaboration between researchers in the physical and social sciences to consider how an interdisciplinary approach can help plan for large scale evacuations. It draws on perspectives from physics, mathematics, organisation theory, economics, sociology and education. Importantly it goes beyond disciplinary boundaries and considers how interdisciplinary methods are necessary to approach a complex problem involving human actors and increasingly complex communications and transportation infrastructures. Using real world case studies and modelling the book considers new approaches to evacuation dynamics. It addresses questions of complexity, not only in terms of theory, but examining the latest challenges for cities and emergency responders. Factors such as social media, information quality and visualisation techniques are examined to consider the ‘new’ dynamics of warning and informing, evacuation and recovery
Co-evolution vs. Neural Networks; An Evaluation of UK Risky Money
Get PDFThe performance of a "capital certain" Divisia index constructed using the same components included in the Bank of England"s MSI plus national savings; a "risky" Divisia index constructed by adding bonds, shares and unit trusts to the list of assets included in the first index; and a capital certain simple sum index for comparison is compared. nce suggests that co-evolutionary strategies are superior to neural networks in the majority of cases. The risky money index performs at least as well as the Bank of England Divisia index when combined with interest rate information. Notably, the provision of long term interest rates improves the out-of-sample forecasting performance of the Bank of England Divisia index in all cases examinedEvolutionary Strategies, Risk Adjusted Divisia, Inflation, Neural Networks
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