ITM Web Conf.
Volume 32, 2020International Conference on Automation, Computing and Communication 2020 (ICACC-2020)
|Number of page(s)||7|
|Published online||29 July 2020|
Imputation of missing data in time series by different computation methods in various data set applications
1 Ramrao Adik Institute of Technology, D.Y. Patil Group, Nerul, Navi Mumbai - 400 706, India
2 Ramrao Adik Institute of Technology, D.Y. Patil Group, Nerul, Navi Mumbai - 400 706, India
3 Ramrao Adik Institute of Technology, D.Y. Patil Group, Nerul, Navi Mumbai - 400 706, India
4 Ramrao Adik Institute of Technology, D.Y. Patil Group, Nerul, Navi Mumbai - 400 706, India
In a modern technology generation, big volumes of data are evolved under numerous operations compared to an earlier era. However, collection of data without missing single value, is a great challenge ahead. In practice, there are many solutions suggested to avoid the missing values in time series applications. The existing methods used in imputation and their prediction with time series, varies with applications. The existing methods mostly available for imputation are least squares support vector machine (LSSVM), autoregressive integrated moving average models (ARIMA), Artificial Neural Network (ANN), Artificial Intelligence (AI) techniques, state space models, Kalman filtering and fuzzy model. The extensive experimental application data is used to analyze these methods. In addition, a synthetic set of data can also be used to forecast missing value, which improves performance of imputation methods in time series. In this paper, predominantly used imputation methods have been listed with their fundamental computational information along with their verification on set of data mentioned.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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