cogcom 15(4): e5

Research Article

Time Series Forecasting with Missing Values

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  • @ARTICLE{10.4108/icst.iniscom.2015.258269,
        author={Shin-Fu Wu and Chia-Yung Chang and Shie-Jue Lee},
        title={Time Series Forecasting with Missing Values},
        journal={EAI Endorsed Transactions on Cognitive Communications},
        volume={1},
        number={4},
        publisher={EAI},
        journal_a={COGCOM},
        year={2015},
        month={4},
        keywords={time series prediction, missing values, local time index, least squares support vector machine (lssvm)},
        doi={10.4108/icst.iniscom.2015.258269}
    }
    
  • Shin-Fu Wu
    Chia-Yung Chang
    Shie-Jue Lee
    Year: 2015
    Time Series Forecasting with Missing Values
    COGCOM
    EAI
    DOI: 10.4108/icst.iniscom.2015.258269
Shin-Fu Wu1, Chia-Yung Chang1, Shie-Jue Lee1,*
  • 1: National Sun Yat-Sen University
*Contact email: leesj@mail.ee.nsysu.edu.tw

Abstract

Time series prediction has become more popular in various kinds of applications such as weather prediction, control engineering, financial analysis, industrial monitoring, etc. To deal with real-world problems, we are often faced with missing values in the data due to sensor malfunctions or human errors. Traditionally, the missing values are simply omitted or replaced by means of imputation methods. However, omitting those missing values may cause temporal discontinuity. Imputation methods, on the other hand, may alter the original time series. In this study, we propose a novel forecasting method based on least squares support vector machine (LSSVM). We employ the input patterns with the temporal information which is defined as local time index (LTI). Time series data as well as local time indexes are fed to LSSVM for doing forecasting without imputation. We compare the forecasting performance of our method with other imputation methods. Experimental results show that the proposed method is promising and is worth further investigations.