  Alongside the continuous process of improving AI performance through the
development of more sophisticated models, researchers have also focused their
attention to the emerging concept of data-centric AI, which emphasizes the
important role of data in a systematic machine learning training process.
Nonetheless, the development of models has also continued apace. One result of
this progress is the development of the Transformer Architecture, which
possesses a high level of capability in multiple domains such as Natural
Language Processing (NLP), Computer Vision (CV) and Time Series Forecasting
(TSF). Its performance is, however, heavily dependent on input data
preprocessing and output data evaluation, justifying a data-centric approach to
future research. We argue that data-centric AI is essential for training AI
models, particularly for transformer-based TSF models efficiently. However,
there is a gap regarding the integration of transformer-based TSF and
data-centric AI. This survey aims to pin down this gap via the extensive
literature review based on the proposed taxonomy. We review the previous
research works from a data-centric AI perspective and we intend to lay the
foundation work for the future development of transformer-based architecture
and data-centric AI.
