Neuroplasticity-inspired dynamic ANNs for multi-task demand forecasting

• URL: http://arxiv.org/abs/2509.24495v1
• Date: Mon, 29 Sep 2025 09:08:08 GMT
• Title: Neuroplasticity-inspired dynamic ANNs for multi-task demand forecasting
• Authors: Mateusz Żarski, Sławomir Nowaczyk,
• Abstract summary: 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)<n>Our proposed method enables structural adaptability of the computational graph during training, inspired by neuroplasticity as seen in biological systems.<n>NMT-Net offers a scalable, adaptable solution for multi-task and continual learning in time series prediction.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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