  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:
https://github.com/SaberaTalukder/TOTEM.
