Time Series Foundational Models: Their Role in Anomaly Detection and Prediction
Time series foundational models (TSFM) have gained prominence in time series forecasting,
promising state-of-the-art performance across various applications. However, their …
promising state-of-the-art performance across various applications. However, their …
Sundial: A Family of Highly Capable Time Series Foundation Models
We introduce Sundial, a family of native, flexible, and scalable time series foundation
models. To predict the next-patch's distribution, we propose a TimeFlow Loss based on flow …
models. To predict the next-patch's distribution, we propose a TimeFlow Loss based on flow …
[HTML][HTML] A Vegetable-Price Forecasting Method Based on Mixture of Experts
The accurate forecasting of vegetable prices is crucial for policy formulation, market
decisions, and agricultural market stability. Traditional time-series models often require …
decisions, and agricultural market stability. Traditional time-series models often require …
Towards Neural Scaling Laws for Time Series Foundation Models
Scaling laws offer valuable insights into the design of time series foundation models
(TSFMs). However, previous research has largely focused on the scaling laws of TSFMs for …
(TSFMs). However, previous research has largely focused on the scaling laws of TSFMs for …
XRF V2: A Dataset for Action Summarization with Wi-Fi Signals, and IMUs in Phones, Watches, Earbuds, and Glasses
B Lan, P Li, J Yin, Y Song, G Wang, H Ding… - arxiv preprint arxiv …, 2025 - arxiv.org
Human Action Recognition (HAR) plays a crucial role in applications such as health
monitoring, smart home automation, and human-computer interaction. While HAR has been …
monitoring, smart home automation, and human-computer interaction. While HAR has been …
General Time-series Model for Universal Knowledge Representation of Multivariate Time-Series data
Universal knowledge representation is a central problem for multivariate time series (MTS)
foundation models and yet remains open. This paper investigates this problem from the first …
foundation models and yet remains open. This paper investigates this problem from the first …
Bridging Smart Meter Gaps: A Benchmark of Statistical, Machine Learning and Time Series Foundation Models for Data Imputation
The integrity of time series data in smart grids is often compromised by missing values due
to sensor failures, transmission errors, or disruptions. Gaps in smart meter data can bias …
to sensor failures, transmission errors, or disruptions. Gaps in smart meter data can bias …
A Mamba Foundation Model for Time Series Forecasting
H Ma, Y Chen, W Zhao, J Yang, Y Ji, X Xu, X Liu… - arxiv preprint arxiv …, 2024 - arxiv.org
Time series foundation models have demonstrated strong performance in zero-shot
learning, making them well-suited for predicting rapidly evolving patterns in real-world …
learning, making them well-suited for predicting rapidly evolving patterns in real-world …