Results 101 to 110 of about 18,244,857 (290)

Outlier detection in multivariate time series via projection pursuit [PDF]

, 2004
This article uses Projection Pursuit methods to develop a procedure for detecting outliers in a multivariate time series. We show that testing for outliers in some projection directions could be more powerful than testing the multivariate series directly.
Peña, Daniel, Tsay, Ruey S., Galeano, Pedro   +2 more
core   +1 more source

Rainfall Shapes the Diversity of Soil Nitrogen‐Fixing Microorganisms Worldwide

Advanced Science, EarlyView.
This study reveals the distinctive pattern and mechanism of rainfall driving the global biodiversity and biogeography of soil potential N‐fixing microorganisms, and constructs the theoretical framework. The results guide us on how to maintain ecosystem productivity under future climate changes (e.g., whether a specific region needs more N fertilizers ...
Bin Hua, Shuang Pang, Ang Li, Zonghao Hu, Honghui Wu, Shuhan Zhang, Yi Fan, Yi Wu, Wei Yang, Yachao Zhao, Yupeng Guan, Baoming Ji, Deliang Kong, Yong Zhao, Anton A. Goncharov, Anastasia Yu. Korotkevich, Rong Mao, Yang Zhang, Ximei Zhang   +18 more
wiley   +1 more source

DiffTST: Diff Transformer for Multivariate Time Series Forecast

IEEE Access
Deep learning models employing the Transformer architecture have demonstrated exceptional performance in the field of multivariate time series forecasting research. However, these models often incorporate irrelevant or weakly relevant information during the processing of time series, leading to noise.
Song Yang, Wenyong Han, Yaping Wan, Tao Zhu 0001, Zhiming Liu, Shuangjian Li   +5 more
openaire   +3 more sources

Universal Battery Capacity Degradation Forecasting Driven by Foundation Models Across Diverse Chemistries and Conditions

Advanced Science, EarlyView.
A unified time‐series forecasting framework learns transferable battery capacity‐degradation patterns from 20 heterogeneous datasets spanning chemistries, formats, temperatures, and cycling conditions. A single model delivers competitive predictions on both known and previously unseen datasets, while physics‐guided representation learning improves ...
Joey Chan, Huan Wang, Zhen Chen, Haoyu Pan, Wei Wu, Zirong Wang, Ershun Pan, Min Xie, Lifeng Xi   +8 more
wiley   +1 more source

IBformer: Inductive Bias is Necessary for Multivariate Time Series Forecasting

Data Science and Engineering
Transformers use a powerful self-attention mechanism to model remote dependency capabilities, showing great potential in various long-term time series prediction tasks.
Haoyuan Shi, Xiangfu Meng, Yongqin Zhang, Jianxu Lu, Jia Wang   +4 more
doaj   +1 more source

Nonparametric modeling and forecasting electricity demand: an empirical study [PDF]

This paper uses half-hourly electricity demand data in South Australia as an empirical study of nonparametric modeling and forecasting methods for prediction from half-hour ahead to one year ahead.
Han Lin Shang
core  

Low Latency Global Carbon Budget Reveals Late 2024 Carbon Losses and Contrasting Early 2025 Land Sink Recovery Signals

Advanced Science, EarlyView.
Low latency carbon budget estimates for July 2024–June 2025 combine atmospheric CO2 growth rates, fossil emissions, ocean uptake, DGVM land fluxes, and OCO‐2 inversions. The budget shows that late‐2024 land carbon losses dominate the annual anomaly, while early‐2025 recovery differs between bottom‐up models and top‐down inversions, especially in ...
Piyu Ke, Philippe Ciais, Yitong Yao, Stephen Sitch, Wei Li, Xiaomeng Du, Xiaofan Gui, Ben Poulter, Thomas Colligan, Auke M. van der Woude, Joram Hooghiem, Wouter Peters, Zhu Liu, Zhu Deng, Zhe Jin, Xiangjun Tian, Yilong Wang, Junjie Liu, Sudhanshu Pandey, Chris O'Dell, Jiang Bian, John Miller, Xin Lan, Jefferson Goncalves De Souza, Michael O'Sullivan, Pierre Friedlingstein, Julien Alléon, Yi Xi, Daniel S. Goll, Lei Zhu, Guido R. van der Werf, Shilong Piao, Frédéric Chevallier   +32 more
wiley   +1 more source

Automatic time series forecasting: the forecast package for R. [PDF]

Automatic forecasts of large numbers of univariate time series are often needed in business and other contexts. We describe two automatic forecasting algorithms that have been implemented in the forecast package for R.
Rob J. Hyndman, Yeasmin Khandakar
core   +2 more sources

Forecasting Enrollment Model Based on First-Order Fuzzy Time Series

, 2005
This paper proposes a novel improvement of forecasting approach based on using time-invariant fuzzy time series. In contrast to traditional forecasting methods, fuzzy time series can be also applied to problems, in which historical data are linguistic ...
Konstantin Y., Degtiarev, Sah, Melike
core   +1 more source

Multivariate time series energy forecasting using a hybrid neural-SVM ensemble model: A data-driven approach for energy management in Bangladesh

Energy Nexus
Energy forecasting of generation, demand, sources, and prices over short-time horizons is necessary for optimization of energy management. Given the increased use of developing technologies and reliance on renewable energy sources, strategic planning ...
Mohammad Mynul Islam Mahin, Md Jawad Bin Rouf, Shah Murtoza Morshed, Sheak Salman, Md Shihab Shakur, Mohammad Morshed, Md. Parvez   +6 more
doaj   +1 more source