Existing time series tokenization methods predominantly encode a constant number of samples into individual tokens. This inflexible approach can generate excessive tokens for even simple patterns like extended constant values, resulting in substantial computational overhead. Inspired by the success of byte pair encoding, we propose the first pattern-centric tokenization scheme for time series analysis. Based on a discrete vocabulary of frequent motifs, our method merges samples with underlying patterns into tokens, compressing time series adaptively. Exploiting our finite set of motifs and the continuous properties of time series, we further introduce conditional decoding as a lightweight yet powerful post-hoc optimization method, which requires no gradient computation and adds no computational overhead. On recent time series foundation models, our motif-based tokenization improves forecasting performance by 36% and boosts efficiency by 1990% on average. Conditional decoding further reduces MSE by up to 44%. In an extensive analysis, we demonstrate the adaptiveness of our tokenization to diverse temporal patterns, its generalization to unseen data, and its meaningful token representations capturing distinct time series properties, including statistical moments and trends.

翻译：现有时间序列标记化方法主要将固定数量的样本编码为单个标记。这种僵化的方法即使对简单模式（如持续恒定值）也会生成过多标记，导致显著的计算开销。受字节对编码成功应用的启发，我们提出了首个面向时间序列分析的以模式为中心的标记化方案。基于频繁基元的离散词汇表，我们的方法将具有底层模式的样本合并为标记，从而自适应地压缩时间序列。利用有限的基元集合和时间序列的连续特性，我们进一步引入了条件解码作为轻量级但强大的事后优化方法，该方法无需梯度计算且不增加计算开销。在近期的时间序列基础模型上，我们基于基元的标记化方法将预测性能平均提升36%，效率平均提升1990%。条件解码进一步将均方误差降低高达44%。通过广泛分析，我们证明了我们的标记化方法对多样化时序模式的自适应性、对未见数据的泛化能力，以及其能够捕获包括统计矩和趋势在内的不同时间序列特性的有意义的标记表示。