scholarly journals A systematic review of data pre-processing methods and unsupervised mining methods used in profiling smart meter data

2021 ◽  
Vol 5 (4) ◽  
pp. 284-314
Author(s):  
Folasade M. Dahunsi ◽  
◽  
Abayomi E. Olawumi ◽  
Daniel T. Ale ◽  
Oluwafemi A. Sarumi ◽  
...  
Keyword(s):  
Systematic Review ◽  
Data Mining ◽  
Time Series Data ◽  
Series Data ◽  
Smart Meters ◽  
Data Mining Technique ◽  
Intrinsic Nature ◽  
Hidden Knowledge ◽  
Mining Methods ◽  
Processing Techniques

<abstract> <p>The evolution of smart meters has led to the generation of high-resolution time-series data - a stream of data capable of unveiling valuable knowledge from consumption behaviours for different applications. The ability to extract hidden knowledge from such massive amounts of data requires that it be analysed intelligently. Hence, for a clear representation of the various consumption behaviours of consumers, a good number of data mining technologies are usually employed. This paper presents a systematic review of the various data mining techniques and methodologies employed while profiling energy data streams. The review identifies the strengths and shortcomings of existing data mining methods as applied in research, focusing more on data processing techniques and load clustering. Also discussed are data mining methods used to profile consumption data, their pros and cons. It was inferred during the research that the choice of data mining technique employed is highly dependent on the application it is intended for and the intrinsic nature of the dataset.</p> </abstract>

Time Series Data Mining Implemented on Football Match

2010 ◽  
Vol 26-28 ◽  
pp. 98-103 ◽  
Author(s):  
Ben Cheng Chai
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Data Mining Technique ◽  
Time Series Data Mining ◽  
Proposed Model ◽  
Interesting Pattern ◽  
Football Match ◽  
The One

This study utilizes time series data mining to find the interesting pattern and cooperation custom. Meanwhile, data mining technique and some special football skills such as ball possession are employed to build a novel decision model in football match. The proposed model is expatiated through real football match. In short, on the one hand, the model provides a feasible route to guide the decision makers including football coach to establish effective mechanism in football match. On the other hand, it extends the application scope of time series data mining.

scholarly journals Study on Visual Techniques of Potential Pattern Discovery for Time Series Data

2018 ◽  
Vol 232 ◽  
pp. 02049
Author(s):  
Dalin Xu ◽  
Yingmei Wei
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Pattern Mining ◽  
Time Series Data ◽  
Development Trend ◽  
Series Data ◽  
Mining Methods ◽  
Visualization Technology ◽  
Important Branch ◽  
Development And Evolution

Sequential pattern mining is always a very important branch of time series data mining. The pattern mining with visual means can be used to extract the knowledge of time series data more intuitively. Based on the research content, this paper analyzes the sequence pattern mining methods in different aspects and their combination with visualization technology. We further discuss and summarize the advantages of different visualization methods in discovering the potential patterns in time series data. Different systems and models have their unique information to show the focus. Compared with the characteristics of the model, the development and evolution of visualization technology for the discovery of potential patterns of time series data can be summarized. Finally, this paper discusses its development trend and how to play a greater role in the era of big data.

scholarly journals IMPLEMENTASI DATA MINING UNTUK MERAMALKAN PENJUALAN DI MINIMARKET IDOLA JL PATI-TAMBAKROMO KM 2 DESA KARANGMULYO RT 08 RW 1 DENGAN METODE TIME SERIES

Compiler ◽  
2012 ◽  
Vol 1 (2) ◽  
Author(s):  
Hamdani Widyatmoko ◽  
Anton Setiawan Honggowibowo ◽  
NurCahyani Dewi Retnowati
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Series Data ◽  
Market Demand ◽  
Data Mining Technique ◽  
Absolute Deviation ◽  
The Future

Minimarket idola on a daily basis there are many sales transactions, so that the data stored in the database is very large. The data can be used as much useful information for the owner of a minimarket in policy making. To explore the data that is used a lot of data mining technique. Data mining uses data analysis to discover patterns and relationships in data that may be used to make accurate predictions.In this research, data mining is used to forecast the sales of goods in Minimarket Idoal. Forecasting the future based on measuring the value of the patterns in the data collection. To perform sales forecasting in the future to use the method of time series. Forecasting time series data to predict what will happen based on past historical data.Time series methods for forecasting sales in the calculation Minimarket Idola using exponential smoothing and moving average. Of the count sought the MAD (Mean Absolute Deviation) or forecasting errors. Where MAD is the smallest value of the calculation of exponential smoothing and moving average is the result of forecasting with a small error. Forecasting results will not always be appropriate because the market demand influenced by several factors. But it does not mean that the forecast is made useless.

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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