The field of general time series analysis has recently begun to explore
unified modeling, where a common architectural backbone can be retrained on a
specific task for a specific dataset. In this work, we approach unification
from a complementary vantage point: unification across tasks and domains. To
this end, we explore the impact of discrete, learnt, time series data
representations that enable generalist, cross-domain training. Our method,
TOTEM, or TOkenized Time Series EMbeddings, proposes a simple tokenizer
architecture that embeds time series data from varying domains using a discrete
vectorized representation learned in a self-supervised manner. TOTEM works
across multiple tasks and domains with minimal to no tuning. We study the
efficacy of TOTEM with an extensive evaluation on 17 real world time series
datasets across 3 tasks. We evaluate both the specialist (i.e., training a
model on each domain) and generalist (i.e., training a single model on many
domains) settings, and show that TOTEM matches or outperforms previous best
methods on several popular benchmarks. The code can be found at:
this https URL

本研究探索了跨任务和领域的统一建模，通过使用离散的、学到的时间序列数据表示方法来实现通用、跨领域训练。我们提出了一种名为 TOTEM 的简单的分词架构，它使用自监督学习的方式来嵌入来自不同领域的时间序列数据。通过在 17 个真实世界的时间序列数据集上进行广泛评估，我们研究了 TOTEM 方法的有效性，涵盖了 3 个任务。我们对专家模式（在每个领域上训练模型）和通用模式（在多个领域上训练单个模型）进行了评估，并展示 TOTEM 在几个常见基准测试中能够达到或超过先前最佳方法的效果。