Enhanced symbolic aggregate approximation (EN-SAX) as an improved representation method for financial time series data

Peiman Mamani Barnaghi, Azuraliza Abu Bakar, Zulaiha Ali Othman

Research output: Contribution to journalArticle

3 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)261-268
Number of pages8
JournalInternational Journal of Soft Computing
Volume8
Issue number4
DOIs
Publication statusPublished - 2013

Fingerprint

Financial Data
Financial Time Series
Time Series Data
Time series
Approximation
Mean Value
Data Preprocessing
Prediction
Error Rate
Data Mining
Enhancement
Data mining
Experimental Results
Processing

Keywords

  • (EN-SAX)
  • Dimensionality reduction
  • Financial time series data
  • Malaysia
  • Pre-processing
  • Symbolic aggregate approximation (SAX)

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Modelling and Simulation

Cite this

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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.",
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AU - Ali Othman, Zulaiha

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

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