Modeling Financial Time Series with Artificial Neural Networks

Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001

MATSuMoTo: The MATLAB Surrogate Model Toolbox For Computationally Expensive Black-Box Global Optimization Problems

MATSuMoTo is the MATLAB Surrogate Model Toolbox for computationally expensive, black-box, global optimization problems that may have continuous, mixed-integer, or pure integer variables. Due to the black-box nature of the objective function, derivatives are not available. Hence, surrogate models are used as computationally cheap approximations of the expensive objective function in order to guide the search for improved solutions. Due to the computational expense of doing a single function evaluation, the goal is to find optimal solutions within very few expensive evaluations. The multimodality of the expensive black-box function requires an algorithm that is able to search locally as well as globally. MATSuMoTo is able to address these challenges. MATSuMoTo offers various choices for surrogate models and surrogate model mixtures, initial experimental design strategies, and sampling strategies. MATSuMoTo is able to do several function evaluations in parallel by exploiting MATLAB's Parallel Computing Toolbox.Comment: 13 pages, 7 figure

Modelling and validation of off-road vehicle ride dynamics

Increasing concerns on human driver comfort/health and emerging demands on suspension systems for off-road vehicles call for an effective and efficient off-road vehicle ride dynamics model. This study devotes both analytical and experimental efforts in developing a comprehensive off-road vehicle ride dynamics model. A three-dimensional tire model is formulated to characterize tire–terrain interactions along all the three translational axes. The random roughness properties of the two parallel tracks of terrain profiles are further synthesized considering equivalent undeformable terrain and a coherence function between the two tracks. The terrain roughness model, derived from the field-measured responses of a conventional forestry skidder, was considered for the synthesis. The simulation results of the suspended and unsuspended vehicle models are derived in terms of acceleration PSD, and weighted and unweighted rms acceleration along the different axes at the driver seat location. Comparisons of the model responses with the measured data revealed that the proposed model can yield reasonably good predictions of the ride responses along the translational as well as rotational axes for both the conventional and suspended vehicles. The developed off-road vehicle ride dynamics model could serve as an effective and efficient tool for predicting vehicle ride vibrations, to seek designs of primary and secondary suspensions, and to evaluate the roles of various operating conditions

Large scale optimization of transonic axial compressor rotor blades

[First Paragraphs] In the present work the Multipoint Approximation Method (MAM) by Toropov et al. (1993) has been applied to the shape optimization of an existing transonic compressor rotor (NASA rotor 37) as a benchmark case. Simulations were performed using the Rolls-Royce plc. PADRAM-HYDRA system (Shahpar and Lapworth 2003, Lapworth and Shahpar 2004) that includes the parameterization of the blade shape, meshing, CFD analysis, postprocessing, and objective/constraints evaluation. The parameterization approach adopted in this system is very flexible but can result in a large scale optimization problem. For this pilot study, a relatively coarse mesh has been used including around 470,000 nodes. The parameterization was done using 5 engineering blade parameters like axial movement of sections along the engine axis in mm (XCEN), circumferential movements of sections in degrees (DELT), solid body rotation of sections in degrees (SKEW), and leading/trailing edge recambering (LEM0/TEMO) in degrees. The design variables were specified using 6 control points at 0 % (hub), 20%, 40%, 60%, 80%, and 100% (tip) along the span. Thus the total number of independent design variables N was 30. B-spline interpolation was used through the control points to generate smooth design perturbations in the radial direction