受神经可塑性启发，动态调整网络结构以提升多任务需求预测精度。
Neuroplasticity-inspired dynamic ANNs for multi-task demand forecasting
- 基于生物神经可塑性，训练时动态重构网络结构。
- 在三个真实数据集上，RMSE与标准差均优于现有方法。
- 适合需要持续学习的时序预测场景，如供应链管理。
本文提出一种新型动态人工神经网络（D-ANN）框架——神经可塑性多任务网络（NMT-Net），用于多任务需求预测。不同于传统方法侧重推理阶段的动态性或计算效率，本方法在训练过程中实现计算图的结构自适应，灵感源自生物系统中的神经可塑性。每个新任务触发网络动态调整，包括基于相似性的任务识别、候选网络头的择优训练，并根据性能评估整合到模型中。我们在Kaggle上的三个真实世界多任务需求预测数据集上进行了评估，结果表明该框架在性能和稳定性上均显著优于传统基线及当前先进多任务学习方法，表现出更低的均方根误差（RMSE）与标准差。NMT-Net为时间序列预测中的多任务与持续学习提供了可扩展、可适应的解决方案。完整代码已开源至GitHub。
原文摘要 · Abstract (English)
This paper introduces a novel approach to Dynamic Artificial Neural Networks (D-ANNs) for multi-task demand forecasting called Neuroplastic Multi-Task Network (NMT-Net). Unlike conventional methods focusing on inference-time dynamics or computational efficiency, our proposed method enables structural adaptability of the computational graph during training, inspired by neuroplasticity as seen in biological systems. Each new task triggers a dynamic network adaptation, including similarity-based task identification and selective training of candidate ANN heads, which are then assessed and integrated into the model based on their performance. We evaluated our framework using three real-world multi-task demand forecasting datasets from Kaggle. We demonstrated its superior performance and consistency, achieving lower RMSE and standard deviation compared to traditional baselines and state-of-the-art multi-task learning methods. NMT-Net offers a scalable, adaptable solution for multi-task and continual learning in time series prediction. The complete code for NMT-Net is available from our GitHub repository.
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