Results 31 to 40 of about 751,571 (292)

Deep learning for time series classification: a review [PDF]

, 2019
Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed.
Fawaz, Hassan Ismail, Forestier, Germain, Idoumghar, Lhassane, Muller, Pierre-Alain, Weber, Jonathan   +4 more
core   +4 more sources

Data-driven pattern identification and outlier detection in time series [PDF]

, 2018
We address the problem of data-driven pattern identification and outlier detection in time series. To this end, we use singular value decomposition (SVD) which is a well-known technique to compute a low-rank approximation for an arbitrary matrix.
D Paul, G Strang, HH Nguyen, JW Kantelhardt, LHLJZ Ying, PP Kanjilal, PP Kanjilal, Z. Wu   +7 more
core   +3 more sources

Automatically estimating iSAX parameters [PDF]

, 2015
The Symbolic Aggregate Approximation (iSAX) is widely used in time series data mining. Its popularity arises from the fact that it largely reduces time series size, it is symbolic, allows lower bounding and is space efficient.
Azevedo, Paulo J., Castro, Nuno Constantino   +1 more
core   +1 more source

A Recent-Pattern Biased Dimension-Reduction Framework for Time Series Data

Journal of Advances in Information Technology, 2010
High-dimensional time series data need dimension-reduction strategies to improve the efficiency of computation and indexing. In this paper, we present a dimension-reduction framework for time series.
Santi Phithakkitnukoon, Carlo Ratti
doaj   +1 more source

Power system transient security assessment based on multi-channel time series data mining

Energy Reports, 2022
In the context of the clean energy revolution and the high penetration of renewables and power electronics, data-driven Transient Security Assessment (TSA) models can significantly reduce the computational burden of power system TSA and adapt to the ...
Kangkang Wang, Han Diao, Wei Wei, Tannan Xiao, Ying Chen, Bo Zhou   +5 more
doaj   +1 more source

PRESEE: An MDL/MML Algorithm to Time-Series Stream Segmenting [PDF]

, 2013
Time-series stream is one of the most common data types in data mining field. It is prevalent in fields such as stock market, ecology, and medical care. Segmentation is a key step to accelerate the processing speed of time-series stream mining.
Jiang, Yexi, Tang, Changjie, Tang, Mingjie, Xu, Kaikuo, Yuan, Changan   +4 more
core   +2 more sources

Time Series Optimization on Data Mining

Journal of Physics: Conference Series, 2019
Abstract Forecasting is one of the important topics in the data mining field, such as, predictions, weather forecasting, predictions of academic achievement. Another topic associated with forecasting through a series of data that depends on the time period is called time series.
Relita Buaton, Herman Mawengkang, Muhammad Zarlis, Syahril Effendi, Akim Manaor Hara Pardede, Yani Maulita, Achmad Fauzi, N Novriyenni   +7 more
openaire   +1 more source

Combining Three Peripheral Blood Biomarkers to Stratify Rheumatoid Arthritis‐Associated Interstitial Lung Disease Risk

Arthritis Care &Research, Accepted Article.
Objective The purpose was to evaluate a biomarker score consisting of MUC5B rs35705950 promoter variant, plasma matrix metalloproteinase (MMP)‐7, and serum anti‐malondialdehyde‐acetaldehyde (anti‐MAA) antibody for RA‐associated interstitial lung disease risk stratification. Methods Using a multicenter cohort of US veterans with RA, we performed a cross‐
Kelsey Coziahr, Austin M. Wheeler, Brent A. Luedders, Michael Duryee, Halie Frideres, Katherine D. Wysham, Grant W. Cannon, Gary Kunkel, Dana P. Ascherman, Paul A. Monach, Gail S. Kerr, Andreas M. Reimold, Scott M. Matson, Joshua F. Baker, Geoffrey M. Thiele, Ted R. Mikuls, Bryant R. England   +16 more
wiley   +1 more source

Flood prediction with time series data mining: Systematic review

Natural Hazards Research
The global community is continuously working to minimize the impact of disasters through various actions, including earth surveying. For example, flood-prone areas must be identified appropriately, predicted, understood, and socialized.
Dimara Kusuma Hakim, Rahmat Gernowo, Anang Widhi Nirwansyah   +2 more
doaj   +1 more source

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source