scholarly journals Using Data Mining with Time Series Data in Short-Term Stocks Prediction: A Literature Review

2012 ◽  
Vol 02 (04) ◽  
pp. 176-180 ◽  
Author(s):  
José Manuel Azevedo ◽  
Rui Almeida ◽  
Pedro Almeida
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Literature Review ◽  
Time Series Data ◽  
Series Data ◽  
Short Term ◽  
Using Data

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

scholarly journals Data Mining Techniques using Time Series Research

2019 ◽  
Vol 8 (2S11) ◽  
pp. 121-129
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Pattern Mining ◽  
Time Series Data ◽  
Research Field ◽  
Series Data ◽  
Primary Role ◽  
Useful Knowledge ◽  
Time Series Data Mining ◽  
Using Data

As time-series data are eventually large the discovery of knowledge from these massive data seems to be a challenge issue. The similarity measure plays a primary role in time series data mining, which improves the accuracy of data mining task. Time series data mining is used to mine all useful knowledge from the profile of data. Obviously, we have a potential to perform these works, but it leads to a vague crisis. This paper involves a survey regarding time series technique and its related issues like challenges, preprocessing methods, pattern mining and rule discovery using data mining. Streaming of data is one of the difficult tasks that should be managed over time. Thus, this paper can provide a basic and prominent knowledge about time series in data mining research field.

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.

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