Time series signal recovery methods: Comparative study
Institute of Electrical and Electronics Engineers Inc.
Signal data often contains missing values. Effective replacement (imputation) of the missing values can have significant positive effects on processing the signal. In this paper, we compare three commonly employed methods for estimating missing values in time series data: forward fill, backward fill, and mean fill. We carry out a large scale experimental analysis using 3, 600 AR(1)-based simulated time series to determine the optimal method for estimating missing values. The results of the numerical experiments show that the forward and backward fill methods are better suited for times series with large positive correlations, while the mean fill method is better suited for times series with low or negative correlations. The extensive and exhaustive nature of the numerical experiments provides a definitive answer to the comparison of the three imputation methods. © 2021 IEEE.
This conference paper is not available at CUD collection. The version of scholarly record of this paper is published in 2021 International Symposium on Networks, Computers and Communications (ISNCC) (2021), available online at: https://doi.org/10.1109/ISNCC52172.2021.9615669
AR, Autoregression, Filling methods, Imputation, PACF, Time series
Kamalov, F., & Sulieman, H. (2021). Time series signal recovery methods: Comparative study. 2021 International Symposium on Networks, Computers and Communications (ISNCC). https://doi.org/10.1109/ISNCC52172.2021.9615669