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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 alternative for time series forecasting. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.

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Among the different sorts of challenges for the modeling and simulation community, two types of challenges face us: challenges that optimize space and time for the computer, and challenges that improve the human interface to the modeling and simulation process itself. While of these types of challenges are important for the future health of simulation, we present a grand challenge of the latter variety, based on an area termed integrative multimodeling. The purpose of integrative multimodeling is to provide a human-computer interaction environment that allows components of different model types to be linked to one another-most notably dynamic models used in simulation to geometry models for the phenomena being modeled. We specify current modeling practices in simulation and proceed to justify a need for the challenge. We then follow this with two areas: aesthetic computing and the RUBE software framework, which supports customized "notations" for dynamic models constructed using the eXtensible Markup Language (XML).

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.

The graphics processing unit (GPU) has evolved into a flexible and powerful processor of relatively low cost, compared to processors used for other available parallel computing systems. The majority of studies using the GPU within the graphics and simulation communities have focused on the use of the GPU for models that are traditionally simulated using regular time increments, whether these increments are accomplished through the addition of a time delta (i.e., numerical integration) or event scheduling using the delta (i.e., discrete event approximations of continuous-time systems). These types of models have the property of being decomposable over a variable or parameter space. In prior studies, discrete event simulation has been characterized as being an inefficient application for the GPU primarily due to the inherent synchronicity of the GPU organization and an apparent mismatch between the classic event scheduling cycle and the GPU’s basic functionality. However, we have found that irregular time advances of the sort common in discrete event models can be successfully mapped to a GPU, thus making it possible to execute discrete event systems on an inexpensive personal computer platform at speedups close to 10x. This speedup is achieved through the development of a special purpose code library we developed that uses an approximate time-based event scheduling approach. We present the design and implementation of this library, which is based on the compute unified device architecture (CUDA) general purpose parallel applications programming interface for the NVIDIA class of GPUs.