Abstract
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.
Abstract (translated)
本文介绍了一种新颖的方法，用于多任务需求预测的动态人工神经网络（D-ANN），称为神经可塑性多任务网络（NMT-Net）。与传统方法专注于推理时间动态或计算效率不同，我们提出的方法在训练期间启用了计算图的结构适应性，灵感来自生物系统中的神经可塑性。每一个新任务都会触发动态网络调整，包括基于相似性的任务识别和候选ANN头的选择性训练，然后根据其性能进行评估并整合到模型中。我们使用Kaggle上的三个现实世界的多任务需求预测数据集对我们的框架进行了评估，并展示了其优越的性能和一致性，在均方根误差（RMSE）和标准差方面优于传统的基准方法和最新的多任务学习方法。 NMT-Net为时间序列预测中的多任务和持续学习提供了可扩展、适应性强的解决方案。完整的NMT-Net代码可以从我们的GitHub仓库获取。
URL
https://arxiv.org/abs/2509.24495
