The Gray-Box Based Modeling Approach Integrating Both Mechanism-Model and Data-Model: The Case of Atmospheric Contaminant Dispersion

An interpretable model for stock price movement prediction based on the hierarchical belief rule base

Stock price movement prediction is the basis for decision-making to maintain the stability and security of stock markets. It is important to generate predictions in an interpretable manner. The Belief Rule Base (BRB) has certain interpretability based on IF-THEN rule semantics. However, the interpretability of BRB in the whole process of stock prediction modeling may be weakened or lost. Therefore, this paper proposes an interpretable model for stock price movement prediction based on the hierarchical Belief Rule Base (HBRB-I). The interpretability of the model is considered, and several criteria are constructed based on the BRB expert system. First, the hierarchical structure of BRB is constructed to ensure the interpretability of the initial modeling. Second, the interpretability of the inference process is ensured by the Evidential Reasoning (ER) method as a transparent inference engine. Third, a new Projection Covariance Matrix Adaptive Evolution Strategy (P-CMA-ES) algorithm with interpretability criteria is designed to ensure the interpretability of the optimization process. The final mean squared error value of 1.69E-04 was obtained with similar accuracy to the initial BRB and enhanced in terms of interpretability. This paper is for short-term stock forecasting, and more data will be collected in the future to update the rules to enhance the forecasting capability of the rule base.

Data assimilation of structural deformation using finite element simulations and single-perspective projection data based on the example of X-ray imaging

Gaining insight in the complex deformation processes inside a vehicle during the process of crashing is to date only possible via numerical simulations. To validate these simulations experimentally, high speed, high-energy X-ray imaging is currently developed. So far, X-ray images have only been compared qualitatively to simulations as quantitative analysis proves challenging: Three dimensional (3D) information has to be reconstructed from two dimensional (2D) X-ray images showing overlapping parts which undergo complex deformation. We propose a novel analysis method which adapts ideas from data assimilation to reconstruct three dimensional motion and deformation from two dimensional measurements using high speed X-ray video data and finite element (FE) simulations. Furthermore, we analyze the accuracy of the approach based on an observing system simulation experiment.