Imputation strategies for missing data in environmental time series for an unlucky situation

被引：1
|
作者：
Mendola, D [1 ]
机构：
[1] Univ Palermo, Dipartimento Sci Stat & Matemat Silvio Vianelli, I-90128 Palermo, Italy
来源：
INNOVATIONS IN CLASSIFICATION, DATA SCIENCE, AND INFORMATION SYSTEMS | 2005年
关键词：
D O I：
10.1007/3-540-26981-9_32
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
After a detailed review of the main specific solutions for treatment of missing data in environmental time series, this paper deals with the unlucky situation in which, in an hourly series, missing data immediately follow an absolutely anomalous period, for which we do not have any similar period to use for imputation. A tentative multivariate and multiple imputation is put forward and evaluated; it is based on the possibility, typical of environmental time series, to resort to correlations or physical laws that characterize relationships between air pollutants.
引用
收藏
页码：275 / 282
页数：8
相关论文
共 50 条
  • [1] Method of missing data imputation for multivariate time series
    Li Z.
    Zhang F.
    Wang Y.
    Tao Q.
    Li C.
    2018, Chinese Institute of Electronics (40): : 225 - 230
  • [2] Missing Data Imputation in Time Series of Air Pollution
    Junger, Washington
    de Leon, Antonio Ponce
    EPIDEMIOLOGY, 2009, 20 (06) : S87 - S87
  • [3] Imputation of missing data in time series for air pollutants
    Junger, W. L.
    de Leon, A. Ponce
    ATMOSPHERIC ENVIRONMENT, 2015, 102 : 96 - 104
  • [4] Missing Data Imputation in Time Series by Evolutionary Algorithms
    Figueroa Garcia, Juan C.
    Kalenatic, Dusko
    Lopez Bello, Cesar Amilcar
    ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS, PROCEEDINGS: WITH ASPECTS OF ARTIFICIAL INTELLIGENCE, 2008, 5227 : 275 - +
  • [5] Time Series Data and Recent Imputation Techniques for Missing Data: A Review
    Zainuddin, Aznilinda
    Hairuddin, Muhammad Asraf
    Yassin, Ahmad Ihsan Mohd
    Abd Latiff, Zatul Iffah
    Azhar, Aziemah
    2022 INTERNATIONAL CONFERENCE ON GREEN ENERGY, COMPUTING AND SUSTAINABLE TECHNOLOGY (GECOST), 2022, : 346 - 350
  • [6] Combining Convolution and Transformer for Missing Time Series Data Imputation
    Wang, Yi-Fan
    Bu, Shuai-Yu
    Yan, Jing-Hua
    Hou, Zhi-Wen
    Bu, Ling-Bin
    Meng, Fan-Xu
    Journal of Network Intelligence, 2023, 8 (03): : 823 - 838
  • [7] Missing values imputation in ocean buoy time series data
    Chakraborty, Samarpan
    Ide, Kayo
    Balachandran, Balakumar
    OCEAN ENGINEERING, 2025, 318
  • [8] Comparison of Missing Data Imputation Methods in Time Series Forecasting
    Ahn, Hyun
    Sun, Kyunghee
    Kim, Kwanghoon Pio
    CMC-COMPUTERS MATERIALS & CONTINUA, 2022, 70 (01): : 767 - 779
  • [9] Data Imputation for Multivariate Time Series Sensor Data With Large Gaps of Missing Data
    Wu, Rui
    Hamshaw, Scott D.
    Yang, Lei
    Kincaid, Dustin W.
    Etheridge, Randall
    Ghasemkhani, Amir
    IEEE SENSORS JOURNAL, 2022, 22 (11) : 10671 - 10683
  • [10] Imputation of missing values in environmental time series by D-vine copulas
    Chapon, Antoine
    Ouarda, Taha B. M. J.
    Hamdi, Yasser
    WEATHER AND CLIMATE EXTREMES, 2023, 41
12345