Short-horizon AUD/USD direction forecasting is a noisy classification problem relevant to risk monitoring and foreign-exchange exposure management. Many exchange-rate forecasting studies evaluate model classes after combining technical, financial, and macro-related predictors, which can obscure whether performance differences arise from the learner itself or from economic information channels. This paper uses a paired feature-group diagnostic design that combines a full-feature benchmark, OwnMarket-based inclusion, AllGroups-based ablation, window-length sensitivity, and expanding-window robustness analysis. The 45 daily predictors are grouped as OwnMarket, CrossFX, Commodities, EquityRisk, and DollarRatesSpreads. After conservative lag-1 alignment, the best full-feature MCC is 0.076, achieved by the 1D-CNN. Validation-based inclusion-ablation diagnostics rank EquityRisk first among the external groups, driven mainly by its positive ablation contribution. Commodity effects are mixed across models, lookback windows, and expanding-window periods, while overall directional skill remains limited. The results show that paired feature-group diagnostics provide a disciplined way to interpret information-source contributions when model-only rankings are weakly informative.
Hengren Xu, Jinchuan Zheng, Zhenwei Cao et al.· International Conference on...· 0 citations
In this paper, a comparative study of convolutional neural network (CNN) architectural choices for traffic sign recognition is presented. The effects of different network depths, convolutional kernel sizes, pooling strategies, and activation functions on traffic sign classification performance are examined. In practical scenes, traffic sign images are often affected by illumination changes, scale variation, viewpoint variation, partial occlusion, motion blur, and complex backgrounds. These conditions make accurate traffic sign classification difficult. Therefore, CNNs need to extract discriminative visual features. Experiments are conducted on the German Traffic Sign Recognition Benchmark (GTSRB). The number of learnable parameters is used to analyse model complexity, and a row-normalised confusion matrix is used to examine class-level classification behaviour. Results show that the CNN with three convolutional layers, 4 × 4 convolutional kernels, max pooling, and ReLU activation achieves the best classification performance among the tested configurations. Among the examined architectural choices, network depth shows the largest difference in classification performance, followed by pooling strategy, convolutional kernel size, and activation function.
Bei Zhang, Zhi-Hong Man· International Conference on...· 0 citations
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