scholarly journals Identifying Major Hydrologic Change Drivers in a Transboundary Highly Managed Endorheic Basin: Integrating Hydro-ecological Models and Time Series Data Mining Techniques

2021 ◽  
Author(s):  
Juan Sebastian Acero Triana ◽  
Hoori Ajami
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Time Series Data ◽  
Ecological Models ◽  
Series Data ◽  
Data Mining Techniques ◽  
Time Series Data Mining ◽  
Hydrologic Change ◽  
Endorheic Basin

scholarly journals Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Shadi Aljawarneh ◽  
Aurea Anguera ◽  
John William Atwood ◽  
Juan A. Lara ◽  
David Lizcano
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Knowledge Discovery ◽  
Time Series Data ◽  
Medical Patient ◽  
Lessons Learned ◽  
Physiological Signals ◽  
Knowledge Discovery In Databases ◽  
Series Data ◽  
Data Mining Techniques

AbstractNowadays, large amounts of data are generated in the medical domain. Various physiological signals generated from different organs can be recorded to extract interesting information about patients’ health. The analysis of physiological signals is a hard task that requires the use of specific approaches such as the Knowledge Discovery in Databases process. The application of such process in the domain of medicine has a series of implications and difficulties, especially regarding the application of data mining techniques to data, mainly time series, gathered from medical examinations of patients. The goal of this paper is to describe the lessons learned and the experience gathered by the authors applying data mining techniques to real medical patient data including time series. In this research, we carried out an exhaustive case study working on data from two medical fields: stabilometry (15 professional basketball players, 18 elite ice skaters) and electroencephalography (100 healthy patients, 100 epileptic patients). We applied a previously proposed knowledge discovery framework for classification purpose obtaining good results in terms of classification accuracy (greater than 99% in both fields). The good results obtained in our research are the groundwork for the lessons learned and recommendations made in this position paper that intends to be a guide for experts who have to face similar medical data mining projects.

scholarly journals Discrete and Fuzzy Models of Time Series in the Tasks of Forecasting and Diagnostics

Axioms ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 49
Author(s):  
Anton Romanov ◽  
Valeria Voronina ◽  
Gleb Guskov ◽  
Irina Moshkina ◽  
Nadezhda Yarushkina
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Knowledge Base ◽  
Intelligent Systems ◽  
Time Series Data ◽  
Series Data ◽  
Quality Of Data ◽  
Dynamic Data ◽  
Production Program ◽  
Time Series Data Mining

The development of the economy and the transition to industry 4.0 creates new challenges for artificial intelligence methods. Such challenges include the processing of large volumes of data, the analysis of various dynamic indicators, the discovery of complex dependencies in the accumulated data, and the forecasting of the state of processes. The main point of this study is the development of a set of analytical and prognostic methods. The methods described in this article based on fuzzy logic, statistic, and time series data mining, because data extracted from dynamic systems are initially incomplete and have a high degree of uncertainty. The ultimate goal of the study is to improve the quality of data analysis in industrial and economic systems. The advantages of the proposed methods are flexibility and orientation to the high interpretability of dynamic data. The high level of the interpretability and interoperability of dynamic data is achieved due to a combination of time series data mining and knowledge base engineering methods. The merging of a set of rules extracted from the time series and knowledge base rules allow for making a forecast in case of insufficiency of the length and nature of the time series. The proposed methods are also based on the summarization of the results of processes modeling for diagnosing technical systems, forecasting of the economic condition of enterprises, and approaches to the technological preparation of production in a multi-productive production program with the application of type 2 fuzzy sets for time series modeling. Intelligent systems based on the proposed methods demonstrate an increase in the quality and stability of their functioning. This article contains a set of experiments to approve this statement.

scholarly journals Application of time series data mining for the prediction of transition times in production

Procedia CIRP ◽  
2020 ◽  
Vol 93 ◽  
pp. 897-902
Author(s):  
Günther Schuh ◽  
Andreas Gützlaff ◽  
Frederick Sauermann ◽  
Theresa Theunissen
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Time Series Data Mining ◽  
Transition Times

Short-term wind power forecasts by a synthetical similar time series data mining method

2018 ◽  
Vol 115 ◽  
pp. 575-584 ◽  
Author(s):  
Gaiping Sun ◽  
Chuanwen Jiang ◽  
Pan Cheng ◽  
Yangyang Liu ◽  
Xu Wang ◽  
...  
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Wind Power ◽  
Time Series Data ◽  
Series Data ◽  
Mining Method ◽  
Short Term ◽  
Similar Time ◽  
Time Series Data Mining ◽  
Data Mining Method
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