International Conference on Computer and Knowledge Engineering

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Attentional Bi-LSTM for Multivariate Time Series Forecasting on Edge Devices: A Case Study on NanoPi Neo Plus2

Authors :

Navid Hajizadeh¹ Saeed Yazdani² Sara Ershadi-Nasab³

1- Ferdowsi university of mashhad 2- Ferdowsi university of mashhad 3- Ferdowsi university of mashhad

Keywords :

Attention Mechanism،Attentional Bi-LSTM،Bidirectional LSTM،Edge AI،Edge Deployment،Embedded Deep Learning،Multivariate Time Series Forecasting،NanoPi Neo Plus2،Real-Time Environmental Monitoring

Abstract :

This study presents a resource-aware deep learning pipeline for next-step multivariate time series forecasting, featuring an attentional bidirectional long short-term memory architecture. The proposed model is designed to capture both forward and backward temporal dependencies while dynamically focusing on the most salient time steps using a Bahdanau-style attention mechanism. We apply this method to the Jena Climate dataset, demonstrating its superior predictive accuracy compared to state-of-the-art models, including CNN-RNN, CNN-LSTM, and Stacked-LSTM baselines. To ensure practical applicability in real-world, resource-constrained environments, the entire model is optimized and deployed on a NanoPi Neo Plus2 board—an ARM-based 64-bit single-board computer with limited computational resources. Our implementation leverages lightweight inference techniques and efficient model quantization to enable on-device prediction without cloud connectivity. The resulting system achieves competitive forecasting performance with minimal latency and power consumption, showcasing the feasibility of edge-AI solutions for environmental monitoring and smart sensing applications. Both quantitative and qualitative analyses confirm the effectiveness and interpretability of the proposed approach.The source code for this project is publicly available at: https://github.com/NavidH95/attentional-bilstm-for-edge-forecasting/tree/main.