Foundation Models for Time Series Analysis: A Tutorial and Survey
arxiv(2024)
摘要
Time series analysis stands as a focal point within the data mining
community, serving as a cornerstone for extracting valuable insights crucial to
a myriad of real-world applications. Recent advancements in Foundation Models
(FMs) have fundamentally reshaped the paradigm of model design for time series
analysis, boosting various downstream tasks in practice. These innovative
approaches often leverage pre-trained or fine-tuned FMs to harness generalized
knowledge tailored specifically for time series analysis. In this survey, we
aim to furnish a comprehensive and up-to-date overview of FMs for time series
analysis. While prior surveys have predominantly focused on either the
application or the pipeline aspects of FMs in time series analysis, they have
often lacked an in-depth understanding of the underlying mechanisms that
elucidate why and how FMs benefit time series analysis. To address this gap,
our survey adopts a model-centric classification, delineating various pivotal
elements of time-series FMs, including model architectures, pre-training
techniques, adaptation methods, and data modalities. Overall, this survey
serves to consolidate the latest advancements in FMs pertinent to time series
analysis, accentuating their theoretical underpinnings, recent strides in
development, and avenues for future research exploration.
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