DocumentCode
3400843
Title
Automatic stationary detection of time series using auto-correlation coefficients and LVQ — Neural network
Author
Poulos, Marios ; Papavlasopoulos, Sozon
Author_Institution
Lab. of Inf. Technol., Ionian Univ., Corfu, Greece
fYear
2013
fDate
10-12 July 2013
Firstpage
1
Lastpage
4
Abstract
A data mining of Time Series using Autocorrelation Coefficients (ACC) and LVQ -Neural Network is addressed in this work-a problem that has not yet been seen in a signal processing framework, to the best of our knowledge. Neural network classification was performed on real Time series Data of real data, in an attempt to experimentally investigate the connection between Time Series data and hidden information about the properties of stationary Time Series. Finally, the ability of the ACC will be tested via a well fitted LVQ neural network which gives satisfactory results in predicting Time Series.
Keywords
data mining; neural nets; time series; LVQ neural network; autocorrelation coefficients; automatic stationary detection; data mining; neural network classification; signal processing framework; time series data; Electroencephalography; Equations; Neural networks; Neurons; Support vector machine classification; Time series analysis; Vectors; Auto Corellation Coefficients; Data Mining; LVQ Neural Network; Stationarity; Time Series;
fLanguage
English
Publisher
ieee
Conference_Titel
Information, Intelligence, Systems and Applications (IISA), 2013 Fourth International Conference on
Conference_Location
Piraeus
Print_ISBN
978-1-4799-0770-0
Type
conf
DOI
10.1109/IISA.2013.6623678
Filename
6623678
Link To Document