  Time series data are ubiquitous across various domains, making time series
analysis critically important. Traditional time series models are
task-specific, featuring singular functionality and limited generalization
capacity. Recently, large language foundation models have unveiled their
remarkable capabilities for cross-task transferability, zero-shot/few-shot
learning, and decision-making explainability. This success has sparked interest
in the exploration of foundation models to solve multiple time series
challenges simultaneously. There are two main research lines, namely
pre-training foundation models from scratch for time series and adapting large
language foundation models for time series. They both contribute to the
development of a unified model that is highly generalizable, versatile, and
comprehensible for time series analysis. This survey offers a 3E analytical
framework for comprehensive examination of related research. Specifically, we
examine existing works from three dimensions, namely Effectiveness, Efficiency
and Explainability. In each dimension, we focus on discussing how related works
devise tailored solution by considering unique challenges in the realm of time
series. Furthermore, we provide a domain taxonomy to help followers keep up
with the domain-specific advancements. In addition, we introduce extensive
resources to facilitate the field's development, including datasets,
open-source, time series libraries. A GitHub repository is also maintained for
resource updates (https://github.com/start2020/Awesome-TimeSeries-LLM-FM).
