Domain-Adaptive Mamba for Efficient Urban Spatio-Temporal Prediction

View the PDF file from the paper entitled DAMBA-ST: MAMBA adaptive for the field for effective urban spatial prediction, by RUI AN and 6 other authors

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a summary:The training of the spatial and urban basis models, which are well generalized across the various regions and cities, is very important to spread urban services in invisible areas or in domain areas. Recent studies have usually focused on combining spatial spatial data across the crossing field to train standardized models. However, these models suffer from the rhetoric complicate complexity and high memory expenses, which limits the ability to expand and spread their practical. Inspired by MAMBA’s efficiency, state space model with the complexity of linear time, we explore its potential for effective urban spatial prediction. However, the MAMBA application is directly as spatial and spinal spine leads to a negative transfer and deterioration of severe performance. This is primarily due to spatial, temporal incense and linguistic mechanism for the updates of the hidden situation in Mampa, which limits the generalization of the crossing field. To overcome these challenges, we suggest the MAMBA-based new model for an effective urban spatial prediction. DAMBA-ST maintains the linear complexity of MAMBA while enhancing its ability to adapt significantly to heterogeneous areas. Specifically, we offer basic innovations: (1) A space model for the condition adapting to the field that divides the inherent representation space into a common sub -space to learn the common denominators between the field and independent sub -spaces for specifications to capture discriminatory features within the field; (2) Three distinct field transformers, which act as a perceived agent for a field of different field distributions bridge and facilitate the alignment of common denominators across the field. Wide experiences show the generalization and efficiency of DAMBA-ST. It has a recent performance on prediction tasks and shows a strong horse, allowing smooth spreading in new urban environments without widespread or refining.