## 2 Related Works  
Elevator data generated in real-time during operation are closely related to measurements over time and are collectively referred to as time series data. In recent years, there have been numerous deep learning models for time series in the field of deep learning, such as LSTM, as well as the Transformer model, which is the focus of this study [7]. The Transformer model can be used for quantitative prediction of time series data, specifically for long-sequence time series forecasting (LSTF) [8]. LSTF requires the model to have a high predictive capability [9], meaning it can effectively capture precise long-term dependencies between the input and output. Recent research has shown the potential of Transformers to have increased prediction capacity. However, due to the quadratic time complexity of its attention mechanism, the Transformer model becomes computationally expensive and difficult to scale when handling very long sequences. When modeling time series in real-world scenarios, timestamp information is often available, including calendar timestamps (e.g., seconds, minutes, hours, weeks, months, and years) and special timestamps (e.g., holidays and events) [10]. These timestamps are highly useful in practical applications but are rarely utilized in ordinary Transformers. To address this issue, [PERSON] [11] proposed encoding timestamps as positional encodings, using learnable embedding layers to embed timestamp features. Autoformer [12] and FEDformer [13] followed this timestamp encoding scheme, as it allows for leveraging global time information. However, this positional encoding information is only added at the initial stage of the model, and as the number of layers increases, the positional information gradually fades away. In addition to modifying individual modules in Transformers to model time series, there are also many works that seek to transform Transformers at the architectural level. Considering the multiresolution nature of time series, such as cyclic patterns and trends, recent studies have adopted hierarchical Transformer architectures. Informer inserted max pooling layers with a stride of 2 between attention blocks, which downsamples the sequence to half its length. Pyraformer [14] designed a binary tree-based attention mechanism where the finest-scale nodes correspond to time points in the original time series, and coarser-scale nodes represent lower-resolution sequences. Pyraformer performs intra-scale and inter-scale attention, capturing time dependencies within a single resolution and constructing a multiresolution representation of the original series. [PERSON] [15] designed a multilevel prediction model that includes a static covariate encoder, a gate-based feature selection module, and a time self-attention decoder to encode and select useful information from various covariate sources for prediction. SSDNet [16] and ProTran [17] combines the Transformer architecture with state space models to provide probabilistic predictions. Although these modifications help address the issue of multiscale dependencies, they often introduce additional complexity and may still face overfitting issues, especially when the data are limited or noisy. Effectively balancing multiresolution dependencies without introducing excessive model complexity remains an unsolved problem. Transformers have proven to be effective in various time series classification tasks due to their ability to capture long-term dependencies. Classification Transformers typically adopt a simple encoder structure, where self-attention layers perform representation learning, and feedforward layers generate probabilities for each class. Transformer proposals have achieved state-of-the-art results on 13 multivariate time series classification tasks. [PERSON]\(\breve{s}\)[PERSON] and [PERSON] [18] studied self-attention-based Transformers for raw optical satellite time series classification and achieved the best results compared to recurrent and convolutional models.