Periodic patterns are a fundamental characteristic of time series in natural world, with
significant implications for a range of disciplines, from economics to cloud systems …

Time series analysis is a fundamental task in various application domains, and deep
learning approaches have demonstrated remarkable performance in this area. However …

Modeling and detection of seasonality in time series is essential for accurate analysis,
prediction and anomaly detection. Examples of seasonal effects at different scales abound …

Detection of periodic patterns of interest within noisy time series data plays a critical role in
various tasks, spanning from health monitoring to behavior analysis. Existing learning …

Periodicity detection is a crucial step in time series tasks, including monitoring and
forecasting of metrics in many areas, such as IoT applications and self-driving database …

Periodicity detection is an important pre-processing step for many time series algorithms. It
provides important information about the structural properties of a time series. Feature …

Time series prediction presents a significant challenge across various domains, such as
transportation systems, environmental science, and multiple industrial sectors. Real-world …

Abstract Recently, Transformers and MLPs based models have dominated and made
significant progress in time series analysis. However, these methods struggle to capture the …

Time series analysis finds wide applications in fields such as weather forecasting, anomaly
detection, and behavior recognition. Previous methods attempted to model temporal …

Natural and human-engineered systems often exhibit periodic behavior. Examples include
the climate system, migration of animals in the wild, consumption of electricity in the power …