🤖 AI Summary
Artificial neural networks (ANNs) suffer from spurious predictions in long-horizon stock forecasting due to high data volatility, out-of-distribution (OOD) test samples, and outliers. Method: This paper proposes a kinematics-inspired weak-relation-constrained neural network. It jointly optimizes both predictions and their first-order differences (“velocity”) in the loss function to explicitly model dynamic temporal evolution. A weak-relation enforcement mechanism mitigates structural distortion induced by autoregressive normalization while preserving original neighborhood topology. Additionally, a velocity-aware composite loss and normalization-sensitive activation functions are introduced, ensuring compatibility with diverse RNN architectures. Contribution/Results: Evaluated on 15 years of Dow Jones Industrial Average data, the method significantly improves long-term forecasting stability and statistical significance. It demonstrates superior robustness—particularly under OOD conditions and during periods of high market volatility—while maintaining architectural flexibility and interpretability through physics-informed constraints.
📝 Abstract
We propose loss function week enforcement of the velocity relations between time-series points in the Kinematic-Informed artificial Neural Networks (KINN) for long-term stock prediction. Problems of the series volatility, Out-of-Distribution (OOD) test data, and outliers in training data are addressed by (Artificial Neural Networks) ANN's learning not only future points prediction but also by learning velocity relations between the points, such a way as avoiding unrealistic spurious predictions. The presented loss function penalizes not only errors between predictions and supervised label data, but also errors between the next point prediction and the previous point plus velocity prediction. The loss function is tested on the multiple popular and exotic AR ANN architectures, and around fifteen years of Dow Jones function demonstrated statistically meaningful improvement across the normalization-sensitive activation functions prone to spurious behaviour in the OOD data conditions. Results show that such architecture addresses the issue of the normalization in the auto-regressive models that break the data topology by weakly enforcing the data neighbourhood proximity (relation) preservation during the ANN transformation.