Exogenous variables provide complementary information that enhances endogenous representations, thereby facilitating more accurate multivariate time series forecasting (MTSF). However, existing methods typically overlook the synergistic interplay between exogenous and endogenous variables by adopting shallow fusion strategies such as simple concatenation or separate encoding, which fail to capture the dynamic dependencies essential for modeling complex temporal patterns. To address this issue, we propose Endexformer, a novel hierarchical Endogenous-Exogenous modeling framework built upon the Transformer architecture. Specifically, Endexformer adopts a hierarchical architecture to jointly model temporal embeddings of endogenous variables and structural embeddings of exogenous variables, enabling a unified representation of cross-variable dependencies. To capture the fine-grained temporal patterns of endogenous variables, we present a multilevel temporal attention mechanism that leverages variable-level embeddings to adaptively incorporate exogenous information. Furthermore, we design a dynamic interactive attention mechanism that selectively emphasizes informative endogenous and exogenous patterns, mitigating redundancy and preserving semantic integrity in variable representations. Extensive experiments on eight real-world datasets show that Endexformer achieves outstanding performance against competing benchmark approaches in MTSF tasks across various temporal scenarios.

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Due to the complex environment of hyperspectral image (HSI) gathering area, it is difficult to obtain a large number of labeled samples for HSI. Therefore, how to effectively achieve the HSI few-shot classification is a hot spot of current research. Prototypical network (PN) is one of the most classical few-shot learning algorithms, which has been widely employed for few-shot image classification and few-shot object detection. However, existing PN-based algorithms for HSI only utilize the single-scale spatial-spectral feature extracted from the last layer, ignoring the semantic information with different scales contained in the other layers. To solve this problem, a novel multi-scale spatial-spectral prototypical network (MSSPN) is proposed in this letter. The contribution of this letter is threefold. Firstly, a multi-scale spatial-spectral feature extraction algorithm based on ladder structure is proposed to effectively achieve the integration of spatial-spectral features with different scales. Secondly, with the theory of ladder-structure-based extraction algorithm, we design a multi-scale spatial-spectral prototype representation, which is suggested to be more robust and effective in the multi-scale spatial-spectral metric space. Finally, our proposed MSSPN has the advantage of expandability, and can be easily applied for the other PN-based few-shot learning methods. The experimental results on HSI few-shot classification indicate that our proposed MSSPN algorithm can achieve higher accuracy than the representative HSI classifiers and the existing PN-based algorithms.