Abstract
Effective model compression plays a pivotal role in mitigating the
computational and interpretational challenges inherent in the domain of
time series forecasting. In this study, we introduce an innovative
data-centric methodology tailored to identify a representative data
subset from the entirety of the dataset. This chosen representative
segment forms the cornerstone for the training of proficient time series
forecasting models. Furthermore, our investigation unveils a compelling
outcome of this approachâ\euro”a substantial reduction in the size of
time series forecasting models when trained with this selected
representative data segment. This model compression strategy results in
a remarkable 56.31% decrease in storage consumption, a discovery of
considerable significance for optimizing resources and enhancing
scalability in time series forecasting. By distilling the dataset to its
fundamental components through our data-centric approach, we aim to
enhance both computational efficiency and the interpretability of the
resultant models. This paper introduces a pioneering technique to tackle
the challenges associated with data volume and model complexity in the
field of time series forecasting, offering potential pathways for more
efficient and insightful modelling in this domain.