Forecasting with artificial neural networks: the state of the art

Time series forecasting using a hybrid ARIMA and neural network model

Over the last decade, ‘user experience’ (UX) became a buzzword in the field of human – computer interaction (HCI) and interaction design. As technology matured, interactive products became not only more useful and usable, but also fashionable, fascinating things to desire. Driven by the impression that a narrow focus on interactive products as tools does not capture the variety and emerging aspects of technology use, practitioners and researchers alike, seem to readily embrace the notion of UX as a viable alternative to traditional HCI. And, indeed, the term promises change and a fresh look, without being too specific about its definite meaning. The present introduction to the special issue on ‘Empirical studies of the user experience’ attempts to give a provisional answer to the question of what is meant by ‘the user experience’. It provides a cursory sketch of UX and how we think UX research will look like in the future. It is not so much meant as a forecast of the future, but as a proposal – a stimulus for further UX research.

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User experience - a research agenda

We present a unifying framework for studying the solution of multiclass categorization problems by reducing them to multiple binary problems that are then solved using a margin-based binary learning algorithm. The proposed framework unifies some of the most popular approaches in which each class is compared against all others, or in which all pairs of classes are compared to each other, or in which output codes with error-correcting properties are used. We propose a general method for combining the classifiers generated on the binary problems, and we prove a general empirical multiclass loss bound given the empirical loss of the individual binary learning algorithms. The scheme and the corresponding bounds apply to many popular classification learning algorithms including support-vector machines, AdaBoost, regression, logistic regression and decision-tree algorithms. We also give a multiclass generalization error analysis for general output codes with AdaBoost as the binary learner. Experimental results with SVM and AdaBoost show that our scheme provides a viable alternative to the most commonly used multiclass algorithms.

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Reducing multiclass to binary: a unifying approach for margin classifiers

本文对国际科学计量学杂志《Ｓｃｉｅｎｔｏｍｅｔｒｉｃｓ》１９７９－１９９１年的研究论文内容、栏目、作者及国别和编委及国别作了计量分析，揭示出科学计量学研究的重点、活动的中心及发展趋势，说明了学科带头人在发展科学计量学这门新兴学科中的作用。

国际刊物《Scientometrics》文献计量研究

We discuss the results of a comparative study of the performance of neural networks and conventional methods in forecasting time series Our work was initially inspired by previously published work

Time series forecasting using neural networks vs. Box- Jenkins methodology

From the Publisher:
Simulation can be broken down into 3 areas. 1)Model Design 2)Model Execution and 3)Execution Analysis. Most Simulation books spend considerable time on #3 but very little on #1 and #2.

Simulation Model Design and Execution: Building Digital Worlds

We have studied neural networks as models for time series forecasting, and our research compares the Box-Jenkins method against the neural network method for long and short term memory series. Our work was inspired by previously published works that yielded inconsistent results about comparative performance. We have since experimented with 16 time series of differing complexity using neural networks. The performance of the neural networks is compared with that of the Box-Jenkins method. Our experiments indicate that for time series with long memory, both methods produced comparable results. However, for series with short memory, neural networks outperformed the Box-Jenkins model. Because neural networks can be easily built for multiple-step-ahead forecasting, they may present a better long term forecast model than the Box-Jenkins method. We discussed the representation ability, the model building process and the applicability of the neural net approach. Neural networks appear to provide a promising altern...

Feedforward Neural Nets as Models for Time Series Forecasting

The web has a short history, but has grown at an exponential pace since its introduction six years ago. Web-based simulation represents the connection between the web and the field of simulation. Web based simulation is not an existing field but rather an idea which represents an interest on the part of simulationists to exploit web technology. To further this cause, I present some issues and concepts in web based simulation to serve as a back drop for a more formal discussion, and potentially the formation of a new simulation sub-area.

Web-based simulation: some personal observations

Qualitative models arising in artificial intelligence domain often concern real systems that are difficult to represent with traditional means. However, some promise for dealing with such systems is offered by research in simulation methodology. Such research produces models that combine both continuous and discrete-event formalisms. Nevertheless, the aims and approaches of the AI and the simulation communities remain rather mutually ill understood. Consequently, there is a need to bridge theory and methodology in order to have a uniform language when either analyzing or reasoning about physical systems. This article introduces a methodology and formalism for developing multiple, cooperative models of physical systems of the type studied in qualitative physics. The formalism combines discrete-event and continuous models and offers an approach to building intelligent machines capable of physical modeling and reasoning.

Paul A. Fishwick

Papers

Time series forecasting using neural networks vs. Box- Jenkins methodology

Simulation Model Design and Execution: Building Digital Worlds

Feedforward Neural Nets as Models for Time Series Forecasting

Web-based simulation: some personal observations

A multimodel methodology for qualitative model engineering