25 papers · ranked by Valyu relevance
Julian Q. Kosciessa, Niels A. Kloosterman, Douglas D. Garrett
Multiscale Entropy (MSE) is used to characterize the temporal irregularity of neural time series patterns. Due to its’ presumed sensitivity to non-linear signal characteristics, MSE is typically considered a complementary measure of brain dynamics to signal variance and spectral power. However, the divergence between…
Shengjie Xia, Wu Sun, Xiaofeng Zou, Panfeng Chen + 5 more
'Huarong Xu' 'Mei Chen' 'Hui Li' 'Xiangjie Kong'] Multivariate time series anomaly detection has garnered significant attention in fields such as IT operations, finance, medicine, and industry. However, a key challenge lies in the fact that anomaly patterns often exhibit multi-scale temporal variations, which existing…
Chao Ma, Yikai Hou, Li Xiang, Yinggang Sun + 3 more
'Jiaxing Qu'] Long-term time-series forecasting is essential for planning and decision-making in economics, energy, and transportation, where long foresight is required. To obtain such long foresight, models must be both efficient and effective in processing long sequence. Recent advancements have enhanced the…
Jean Gaudart, Stanislas Rebaudet, Gaetan Texier, Robert Barrais + 2 more
The aim of the present study was to develop a method for multiscale analysis of non-stationary and non-periodic epidemic time series. Indeed, the epidemiologists may need to know the features, at different resolutions, of short duration outbreaks that did not exhibit periodic cycles. Among of the large number of…
Zoltan Nagy, Peter Mukli, Peter Herman, Andras Eke
Physiological processes-such as, the brain's resting-state electrical activity or hemodynamic fluctuations-exhibit scale-free temporal structuring. However, impacts common in biological systems such as, noise, multiple signal generators, or filtering by transport function, result in multimodal scaling that cannot be…
Junyu Zhu, Enguang Zuo, Xinyu Bi, Chen Chen + 4 more
'Xiaoyi Lv' 'Richard J. Povinelli'] Multivariate time series forecasting is crucial for numerous practical applications ranging from financial markets to climate monitoring. Traditional multivariate time series forecasting methods primarily adopt a time-centric modeling paradigm, applying attention mechanisms to the…
Yitian Zhang, Liheng Ma, Soumyasundar Pal, Yingxue Zhang + 1 more
'Mark Coates'] The performance of transformers for timeseries forecasting has improved significantly. Recent architectures learn complex temporal patterns by segmenting a time-series into patches and using the patches as tokens. The patch size controls the ability of transformers to learn the temporal patterns at…
Hao Si, Xiao Wang, Fan Zhang, Xiaoya Zhou + 4 more
—Multivariate time series analysis has long been one of the key research topics in the field of artificial intelligence. However, analyzing complex time series data remains a challenging and unresolved problem due to its high dimensionality, dynamic nature, and complex interactions among variables. Inspired by the…
Iwona Komorska, Andrzej Puchalski, Minvydas Ragulskis
Diagnosing the condition of rotating machines by non-invasive methods is based on the analysis of dynamic signals from sensors mounted on the machine-such as vibration, velocity, or acceleration sensors; torque meters; force sensors; pressure sensors; etc. The article presents a new method combining the empirical mode…
Fernanda Dantas Bueno, Vanessa C. Morita, Raphael Y. de Camargo, Marcelo B. Reyes + 2 more
The ability to process time on the scale of milliseconds and seconds is essential for behaviour. A growing number of studies have started to focus on brain dynamics as a mechanism for temporal encoding. Although there is growing evidence in favour of this view from computational and in vitro studies, there is still a…
Authors not listed
This work establishes theoretical foundations for hierarchical quantum-classical algorithm design, where complex problems are decomposed across multiple spatial, temporal, or organizational scales with quantum and classical computation assigned to appropriate levels. We develop a mathematical framework that…
Sebastian Wallot, Giuseppe Leonardi
This paper provides a practical, hands-on introduction to cross-recurrence quantification analysis (CRQA), diagonal cross-recurrence profiles (DCRP), and multidimensional recurrence quantification analysis (MdRQA) in R. These methods have enjoyed increasing popularity in the cognitive and social sciences since a…
Nuray Vakitbilir, Amanjyot Singh Sainbhi, Abrar Islam, Alwyn Gomez + 6 more
'Kevin Yuwa Stein' 'Logan Froese' 'Tobias Bergmann' 'Davis McClarty' 'Rahul Raj' 'Frederick Adam Zeiler'] Cerebral physiological signals embody complex neural, vascular, and metabolic processes that provide valuable insight into the brain’s dynamic nature. Profound comprehension and analysis of these signals are…
Xuefei Cao, Jun Ke, Björn Sandstede, Xi Luo
Canonical Correlation Analysis is a technique in multivariate data analysis for finding linear projections that maximize the correlation between two groups of variables. The correlations are typically defined without accounting for the serial correlations between observations, a typical setting for time series data. To…
Authors not listed
This conceptual paper introduces the Adaptive Multi-Resolution Modeling Framework (AMRMF), a novel technique designed to revolutionize chemical engineering by integrating multi-scale simulations, quantum-inspired algorithms, advanced uncertainty quantification, and Bayesian inference. The framework bridges theoretical…
Xinhan Wang, Bowen Zhao, Jinran Wu
Time series forecasting is a critical task with widespread applications in industrial domains and daily life, including weather prediction, long-term energy consumption planning, and marketing analysis. Nevertheless, effectively extracting salient temporal patterns and exploring dependencies within multivariate time…
Ethan R. Deyle, Gerald Pao, George Sugihara
The foundation of Empirical dynamic modeling (EDM) is in representing time-series data as the trajectory of a dynamic system in a multidimensional state space rather than as a collection of traces of individual variables changing through time. Takens’s theorem provides a rigorous basis for adopting this state-space…
Yulong Wang, Yushuo Liu, Xiaoyi Duan, Kai Wang
Multivariate time series forecasting is crucial across various industries, where accurate extraction of complex periodic and trend components can significantly enhance prediction performance. However, existing models often struggle to capture these intricate patterns. To address these challenges, we propose FilterTS, a…
Vanessa Freitas Silva, Maria Eduarda Silva, Pedro Ribeiro, Fernando Silva
'Fernando Silva'] In recent years, there has been a surge in the prevalence of high- and multi-dimensional temporal data across various scientific disciplines. These datasets are characterized by their vast size and challenging potential for analysis. Such data typically exhibit serial and crossdependency and possess…
Authors not listed
Nonlinear monotonically increasing bounded functions help to visualize and analyze data on various scales. However, many monotonic functions such as logarithm or power laws have either function values or derivatives that become unbounded at some regions of the $x-$ axis. On the other hand, sigmoid or hyperbolic…
Michael X Cohen
Morlet wavelets are frequently used for time-frequency analysis of non-stationary time series data, such as neuroelectrical signals recorded from the brain. The crucial parameter of Morlet wavelets is the width of the Gaussian that tapers the sine wave. This width parameter controls the trade-off between temporal…
Authors not listed
As demand rises for scalable and sustainable energy storage, fast and low-cost diagnostics capable of identifying cell-to-cell variations are urgently needed, particularly for factory-produced sorting and second-life assessment. Electrochemical impedance spectroscopy (EIS) is widely used but remains slow, expensive and…
Sachin Heerah, Roberto Molinari, Stéphane Guerrier, Amy Marshall-Colon
Identification of system-wide causal relationships can contribute to our understanding of long-distance, intercellular signaling in biological organisms. Dynamic transcriptome analysis holds great potential to uncover coordinated biological processes between organs. However, many existing dynamic transcriptome studies…
Joshua Reed, Cory J D Matthews, Jennifer Jelincic, C Loren Buck + 2 more
Title: Synopsis The study of endocrinology provides insights into the upstream drivers of behavior and physiology of wild and captive populations to both natural and anthropogenic stressors. Most studies of wildlife endocrinology rely on single samples from multiple individuals to understand differences between…
Authors not listed
Computational simulations of biomolecules provide a wealth of information about the thermodynamic landscape of biologically important systems, kinetics of important cellular processes, and the biophysical basis of life. Despite the ubiquity of molecular simulations in biophysical literature, major challenges persist…