A Multi-Dimensional Time Series Data Mining Model for Weather Forecast

2012 ◽  
Vol 532-533 ◽  
pp. 1277-1281
Author(s):  
Kun Liu
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Time Series Data ◽  
Meteorological Data ◽  
Piecewise Linear ◽  
Series Data ◽  
Linear Fitting ◽  
Time Series Data Mining ◽  
Fitting Method ◽  
Mining Model

Based on the analysis and actual application under the background of the ground automatic weather stations research material, through selecting dimension, piecewise linear fitting method, the cluster symbol, this thesis proposes the dimensions redundant reduction algorithm, the extremum slope piecewise linear fitting method, a multi-dimensional time series data mining model for the meteorological data, and uses the model to preliminarily mine the rule of rain­ weather phenomena. Finally, the experimental results show that this model designed in this paper can predict the weather phenomenon great practicality.

Time Series Data Mining in Process Industry

2012 ◽  
Vol 532-533 ◽  
pp. 1069-1074
Author(s):  
Jia Ren ◽  
Jin Feng Gao
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Large Scale ◽  
Time Series Data ◽  
Piecewise Linear ◽  
Linear Representation ◽  
Series Data ◽  
Time Series Data Mining ◽  
Piecewise Linear Representation ◽  
Mining Procedure

time series; data mining; knowledge discovery; trend extremum representation. Abstract. In recent years, there has been an explosion of interest in mining time series databases. In this paper, we make some attempts to mining process industrial time series. As with most computer science problems, representation of the data is the key to efficient and effective solutions. We introduce a novel algorithm, Trend Extremum Representation, which is empirically proved to be superior to Piecewise Linear Representation and Important Points Representation in manipulating large-scale industrial data. Then, subsequent mining procedure is undertaken. Through clustering analysis and association rule discovery, several useful rules are derived for differentiating normal and abnormal events in everyday operations.

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
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