Results 301 to 310 of about 8,095,148 (356)

Foundation Models for Time Series Analysis: A Tutorial and Survey

Knowledge Discovery and Data Mining
Time series analysis stands as a focal point within the data mining community, serving as a cornerstone for extracting valuable insights crucial to a myriad of real-world applications.
Yuxuan Liang, Haomin Wen, Yuqi Nie, Yushan Jiang, Ming Jin, Dongjin Song, Shirui Pan, Qingsong Wen   +7 more
semanticscholar   +1 more source

Kolmogorov-Arnold Networks (KANs) for Time Series Analysis

2024 IEEE Globecom Workshops (GC Wkshps)
This paper introduces a novel application of Kolmogorov-Arnold Networks (KANs) to time series forecasting, leveraging their adaptive activation functions for enhanced predictive modeling.
Cristian J. Vaca-Rubio, Luis Blanco, Roberto Pereira, Màrius Caus   +3 more
semanticscholar   +1 more source

Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysis

Neural Information Processing Systems
Time series data are ubiquitous across a wide range of real-world domains. While real-world time series analysis (TSA) requires human experts to integrate numerical series data with multimodal domain-specific knowledge, most existing TSA models rely ...
Haoxin Liu, Shangqing Xu, Zhiyuan Zhao, Lingkai Kong, Harshavardhan Kamarthi, Aditya B. Sasanur, Megha Sharma, Jiaming Cui, Qingsong Wen, Chao Zhang, B. Prakash   +10 more
semanticscholar   +1 more source

Empowering Time Series Analysis with Large Language Models: A Survey

International Joint Conference on Artificial Intelligence
Recently, remarkable progress has been made over large language models (LLMs), demonstrating their unprecedented capability in varieties of natural language tasks. However, completely training a large general-purpose model from the scratch is challenging
Yushan Jiang, Zijie Pan, Xikun Zhang, Sahil Garg, Anderson Schneider, Yuriy Nevmyvaka, Dongjin Song   +6 more
semanticscholar   +1 more source

Permutations and time series analysis

Chaos: An Interdisciplinary Journal of Nonlinear Science, 2009
The main aim of this paper is to show how the use of permutations can be useful in the study of time series analysis. In particular, we introduce a test for checking the independence of a time series which is based on the number of admissible permutations on it.
Cánovas, Jose S., Guillamón, Antonio
openaire   +3 more sources

TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysis

International Conference on Learning Representations
Time series analysis plays a critical role in numerous applications, supporting tasks such as forecasting, classification, anomaly detection, and imputation.
Shiyu Wang, Jiawei Li, X. Shi, Zhou Ye, Baichuan Mo, Wenze Lin, Shengtong Ju, Zhixuan Chu, Ming Jin   +8 more
semanticscholar   +1 more source

Complex network approaches to nonlinear time series analysis

Physics reports, 2019
In the last decade, there has been a growing body of literature addressing the utilization of complex network methods for the characterization of dynamical systems based on time series. While both nonlinear time series analysis and complex network theory
Y. Zou, R. Donner, N. Marwan, J. Donges, J. Kurths   +4 more
semanticscholar   +1 more source

Time series analysis of injuries

Statistics in Medicine, 1989
AbstractWe used time series models in the exploratory and confirmatory analysis of selected fatal injuries in the United States from 1972 to 1983. We built autoregressive integrated moving average (ARIMA) models for monthly, weekly, and daily series of deaths and used these models to generate hypotheses.
B, Martinez-Schnell, A, Zaidi
openaire   +2 more sources

MOMENT: A Family of Open Time-series Foundation Models

International Conference on Machine Learning
We introduce MOMENT, a family of open-source foundation models for general-purpose time series analysis. Pre-training large models on time series data is challenging due to (1) the absence of a large and cohesive public time series repository, and (2 ...
Mononito Goswami, Konrad Szafer, Arjun Choudhry, Yifu Cai, Shuo Li, Artur Dubrawski   +5 more
semanticscholar   +1 more source

TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting

International Conference on Learning Representations
Time series forecasting is widely used in extensive applications, such as traffic planning and weather forecasting. However, real-world time series usually present intricate temporal variations, making forecasting extremely challenging.
Shiyu Wang, Haixu Wu, X. Shi, Tengge Hu, Huakun Luo, Lintao Ma, James Y. Zhang, Jun Zhou   +7 more
semanticscholar   +1 more source