  In this study, we present aLLM4TS, an innovative framework that adapts Large
Language Models (LLMs) for time-series representation learning. Central to our
approach is that we reconceive time-series forecasting as a self-supervised,
multi-patch prediction task, which, compared to traditional contrastive
learning or mask-and-reconstruction methods, captures temporal dynamics in
patch representations more effectively. Our strategy encompasses two-stage
training: (i). a causal continual pre-training phase on various time-series
datasets, anchored on next patch prediction, effectively syncing LLM
capabilities with the intricacies of time-series data; (ii). fine-tuning for
multi-patch prediction in the targeted time-series context. A distinctive
element of our framework is the patch-wise decoding layer, which departs from
previous methods reliant on sequence-level decoding. Such a design directly
transposes individual patches into temporal sequences, thereby significantly
bolstering the model's proficiency in mastering temporal patch-based
representations. aLLM4TS demonstrates superior performance in several
downstream tasks, proving its effectiveness in deriving temporal
representations with enhanced transferability and marking a pivotal advancement
in the adaptation of LLMs for time-series analysis.
