Effectively modeling time information and incorporating it into applications or models involving chronologically occurring events is crucial. Real-world scenarios often involve diverse and complex time patterns, which pose significant challenges for time encoding methods. While previous methods focus on capturing time patterns, many rely on specific inductive biases, such as using trigonometric functions to model periodicity. This narrow focus on single-pattern modeling makes them less effective in handling the diversity and complexities of real-world time patterns. In this paper, we investigate to improve the existing commonly used time encoding methods and introduce **Learnable Transformation-based Generalized Time Encoding (LeTE)**. We propose using deep function learning techniques to parameterize nonlinear transformations in time encoding, making them learnable and capable of modeling generalized time patterns, including diverse and complex temporal dynamics. By enabling learnable transformations, LeTE encompasses previous methods as specific cases and allows seamless integration into a wide range of tasks. Through extensive experiments across diverse domains, we demonstrate the versatility and effectiveness of LeTE.

Time series forecasting is a vital task with widespread applications.While recent advancements have adopted patching to enrich shortterm context, existing encoding methods often struggle to capture the diverse semantics within patches, resulting in semantic information loss and limited model performance.Moreover, most long-term dynamics modeling approaches rely on homogeneous architectures with fixed receptive fields, inevitably sacrificing either performance or efficiency.To address these challenges, we propose Mixture of Universals (MoU), a novel framework designed to prevent semantic loss during patch encoding and efficiently enhance long-term dynamics through a hybrid approach.Specifically, MoU is consist of two novel designs: Mixture of Feature Extractors (MoF) and Mixture of Architectures (MoA).MoF introduces a semantics-aware encoding mechanism that selectively activates the corresponding subextractor based on the semantic context of input patches, preserving diverse temporal patterns and mitigating information loss.MoA, on the other hand, hierarchically captures long-term dependency with progressively expanded receptive field, improving model performance while maintaining relatively low computational costs.We conducted extensive experiments on seven real-world datasets, and the results demonstrate the superiority of our model.Our Code is available at https://github.com/lunaaa95/mou/.