@article{MAKHILLIJSC20138421150,
    title = {Enhanced Symbolic Aggregate Approximation (EN-SAX) as an Improved Representation Method for Financial Time Series Data},
    journal = {International Journal of Soft Computing},
    volume = {8},
    number = {4},
    pages = {261-268},
    year = {2013},
    issn = {1816-9503},
    doi = {ijscomp.2013.261.268},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2013.261.268},
    author = {Peiman Mamani,Azuraliza Abu and},
    keywords = {Financial time series data,dimensionality reduction,Symbolic Aggregate Approximation (SAX),(EN-SAX),pre-processing,Malaysia},
    abstract = {Data representation is one of the most important tasks in 
  time series data pre-processing. Time series data representation is required 
  to make the data more suitable for data mining specifically for prediction. 
  Time series data is characterized by its numerical and continuous values. One 
  of the data representation methods for time series is the Symbolic Aggregate 
  Approximation (SAX) which uses mean values as the basis of representation of 
  the data. However, representing the time series financial data with the mean 
  value often causes the loss of patterns that can describes important pieces 
  of information. The aim of this study is to propose an enhancement of SAX representation 
  purposely for the financial time series data. The Enhanced SAX (EN-SAX) adds 
  two new values to the original mean value for each segment in SAX. These values 
  enable better representation for each segment in a lower dimension and keep 
  some of the important patterns that are meaningful in financial time series 
  data. The experimental results show that the EN-SAX representation manages to 
  give lower error rates compared to SAX and improves the prediction accuracy.}
    }