Advancing Time Series Classification with Multimodal Language Modeling
Mingyue Cheng Yiheng Chen Qi Liu Zhiding Liu Yucong Luo
For the advancements of time series classification, scrutinizing previous
studies, most existing methods adopt a common learning-to-classify paradigm - a
time series classifier model tries to learn the relation between sequence
inputs and target label encoded by one-hot distribution. Although effective,
this paradigm conceals two inherent limitations: (1) encoding target categories
with one-hot distribution fails to reflect the comparability and similarity
between labels, and (2) it is very difficult to learn transferable model across
domains, which greatly hinder the development of universal serving paradigm. In
this work, we propose InstructTime, a novel attempt to reshape time series
classification as a learning-to-generate paradigm. Relying on the powerful
generative capacity of the pre-trained language model, the core idea is to
formulate the classification of time series as a multimodal understanding task,
in which both task-specific instructions and raw time series are treated as
multimodal inputs while the label information is represented by texts. To
accomplish this goal, three distinct designs are developed in the InstructTime.
Firstly, a time series discretization module is designed to convert continuous
time series into a sequence of hard tokens to solve the inconsistency issue
across modal inputs. To solve the modality representation gap issue, for one
thing, we introduce an alignment projected layer before feeding the transformed
token of time series into language models. For another, we highlight the
necessity of auto-regressive pre-training across domains, which can facilitate
the transferability of the language model and boost the generalization
performance. Extensive experiments are conducted over benchmark datasets, whose
results uncover the superior performance of InstructTime and the potential for
a universal foundation model in time series classification.