## 2 Related Works
Pretrained Transformers have also been studied in classification tasks. [PERSON] and [PERSON] [19] investigated Transformers for raw optical satellite image time series classification. Due to the scarcity of labeled data, the authors used a self-supervised pretraining scheme. [PERSON] et al. [20] introduced an unsupervised pretraining framework, where the model was pretrained using proportionally masked data, followed by fine-tuning on downstream tasks such as classification. [PERSON] et al. [21] proposed using large-scale pretrained speech processing models to address downstream time series classification tasks and achieved competitive results on 30 popular time series classification datasets. Although the aforementioned work has achieved some success in capturing the dynamic features of time series data and addressing the issue of data scarcity, it generally overlooks the interaction between static and dynamic features. Static features, such as the elevator model, component model, etc., are crucial for elevator fault prediction. However, existing models often focus solely on the dynamic features of the time series data, leading to insufficient feature extraction capabilities and making it difficult to fully uncover the potential risks of elevator faults. Recent research indicates that Transformers have powerful capabilities in handling time series data, making them highly practical in various domains such as fault detection, prediction, and time series classification. Therefore, for the processing and fault prediction of elevator time series data using Transformers would be more efficient. Time series compression is an important research area aimed at reducing storage and transmission requirements by projecting high-dimensional data into low-dimensional spaces through dimensionality reduction techniques. Functional Principal Component Analysis (FPCA), proposed by [PERSON] and [PERSON], uses linear transformations to project high-dimensional time series into a small number of principal components while preserving most of the information. However, FPCA cannot simultaneously reduce the number of features and time points, which may limit its performance in high-dimensional scenarios [22]. To address this issue, the Functional Autoencoder (FAE), introduced by [PERSON] et al., replaces the scalar weights in traditional autoencoders with functional weights and processes time series data through functional neurons. Although the FAE demonstrates certain advantages in time series dimensionality reduction, the low-dimensional representations it generates are typically in the scalar form, limiting its ability to capture complex functional relationships [23]. Moreover, [PERSON] et al. proposed a Bi-Functional Autoencoder (BFAE) for time series dimensionality reduction. The BFAE combines a functional encoder and a functional decoder, enabling it to reduce both the number of functional features and the number of observed time points. Its key innovation lies in using continuous neurons and functional weights to retain the functional nature of the data while generating low-dimensional representations in a nonlinear latent space, thereby significantly enhancing the performance of time series compression [24]. It has been proven that time series compression plays a significant role in elevator time series fault detection and holds great potential for broader applications. By employing effective time series compression techniques, it is possible to significantly reduce the storage and computational demands of high-dimensional data, thereby enhancing the processing efficiency and deployment flexibility of the model.