  Frequency domain representation of time series feature offers a concise
representation for handling real-world time series data with inherent
complexity and dynamic nature. However, current frequency-based methods with
complex operations still fall short of state-of-the-art time domain methods for
general time series analysis. In this work, we present Omni-Dimensional
Frequency Learner (ODFL) model based on a in depth analysis among all the three
aspects of the spectrum feature: channel redundancy property among the
frequency dimension, the sparse and un-salient frequency energy distribution
among the frequency dimension, and the semantic diversity among the variable
dimension. Technically, our method is composed of a semantic-adaptive global
filter with attention to the un-salient frequency bands and partial operation
among the channel dimension. Empirical results show that ODFL achieves
consistent state-of-the-art in five mainstream time series analysis tasks,
including short- and long-term forecasting, imputation, classification, and
anomaly detection, offering a promising foundation for time series analysis.
