Title: DyWPE: Signal-Aware Dynamic Wavelet Positional Encoding for Time Series Transformers Show Chinese title

Title: DyWPE：时间序列Transformer的信号感知动态小波位置编码

Abstract: Existing positional encoding methods in transformers are fundamentally signal-agnostic, deriving positional information solely from sequence indices while ignoring the underlying signal characteristics. This limitation is particularly problematic for time series analysis, where signals exhibit complex, non-stationary dynamics across multiple temporal scales. We introduce Dynamic Wavelet Positional Encoding (DyWPE), a novel signal-aware framework that generates positional embeddings directly from input time series using the Discrete Wavelet Transform (DWT). Comprehensive experiments in ten diverse time series datasets demonstrate that DyWPE consistently outperforms eight existing state-of-the-art positional encoding methods, achieving average relative improvements of 9.1\% compared to baseline sinusoidal absolute position encoding in biomedical signals, while maintaining competitive computational efficiency. Show Chinese abstract

Abstract: 现有的变换器中的位置编码方法本质上是信号无关的，仅从序列索引中获取位置信息，而忽略了底层信号特征。 这一限制在时间序列分析中尤其成问题，因为信号在多个时间尺度上表现出复杂且非平稳的动力学特性。 我们引入了动态小波位置编码（DyWPE），这是一种新颖的信号感知框架，通过离散小波变换（DWT）直接从输入时间序列生成位置嵌入。 在十个多样化的时间序列数据集上的全面实验表明，DyWPE始终优于八种现有最先进的位置编码方法，在生物医学信号中相比基线正弦绝对位置编码平均相对改进了9.1%，同时保持了有竞争力的计算效率。