Zobrazeno 1 - 10
of 53 948
pro vyhledávání: '"Stationary time series"'
Autor:
Bastian, Patrick
We consider the problem of detecting deviations from a white noise assumption in time series. Our approach differs from the numerous methods proposed for this purpose with respect to two aspects. First, we allow for non-stationary time series. Second
Externí odkaz:
http://arxiv.org/abs/2411.06909
Autor:
Chen, Junfeng1,2 (AUTHOR) jfchen@hhu.edu.cn, Guan, Azhu3 (AUTHOR) guanazhu@163.com, Cheng, Shi4 (AUTHOR) cheng@snnu.edu.cn
Publikováno v:
Sensors (14248220). Nov2024, Vol. 24 Issue 22, p7272. 15p.
Recent normalization-based methods have shown great success in tackling the distribution shift issue, facilitating non-stationary time series forecasting. Since these methods operate in the time domain, they may fail to fully capture the dynamic patt
Externí odkaz:
http://arxiv.org/abs/2410.01860
Statistical inference for time series such as curve estimation for time-varying models or testing for existence of change-point have garnered significant attention. However, these works are generally restricted to the assumption of independence and/o
Externí odkaz:
http://arxiv.org/abs/2408.02913
Publikováno v:
Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM '24), October 21--25, 2024, Boise, ID, USA
Despite their popularity, deep neural networks (DNNs) applied to time series forecasting often fail to beat simpler statistical models. One of the main causes of this suboptimal performance is the data non-stationarity present in many processes. In p
Externí odkaz:
http://arxiv.org/abs/2410.03935
Time series forecasting typically needs to address non-stationary data with evolving trend and seasonal patterns. To address the non-stationarity, reversible instance normalization has been recently proposed to alleviate impacts from the trend with c
Externí odkaz:
http://arxiv.org/abs/2409.20371
Time series analysis is crucial in fields like finance, economics, environmental science, and biomedical engineering, aiding in forecasting, pattern identification, and understanding underlying mechanisms. While traditional time-domain methods focus
Externí odkaz:
http://arxiv.org/abs/2408.11012
Discovering causal relations from observational time series without making the stationary assumption is a significant challenge. In practice, this challenge is common in many areas, such as retail sales, transportation systems, and medical science. H
Externí odkaz:
http://arxiv.org/abs/2407.07291
Autor:
Cai, Xiaoxuan, Zeng, Li, Fowler, Charlotte, Dixon, Lisa, Ongur, Dost, Baker, Justin T., Onnela, Jukka-Pekka, Valeri, Linda
Mobile technology (mobile phones and wearable devices) generates continuous data streams encompassing outcomes, exposures and covariates, presented as intensive longitudinal or multivariate time series data. The high frequency of measurements enables
Externí odkaz:
http://arxiv.org/abs/2407.17666