Comparison of Imputation Methods for Univariate Time Series

نویسندگان

چکیده

In order to predict and forecast with greater accuracy, handling “missing values” in “time series” information is crucial. Complete accurate historical data are essential. There many research studies on multivariate time series imputation, however due the lack of associated factors, imputation univariate rarely taken into consideration. It natural that could arise because almost all scientific disciplines collect, store, monitor use "time series" observations. Therefore, characteristics must be considered develop an effective acceptable method for dealing missing data. This work uses statistical package R assess measure effectiveness methods context "univariate The “imputation algorithms” explored evaluated using “root mean square error”, “mean absolute error” percent error”. Four types According experimental findings, “seasonal decomposition” performs better having seasonality characteristic, followed by “linear interpolation”, “kalman smoothing” provides values more similar original set have lower error rates than other techniques.

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ژورنال

عنوان ژورنال: International Journal on Recent and Innovation Trends in Computing and Communication

سال: 2023

ISSN: ['2321-8169']

DOI: https://doi.org/10.17762/ijritcc.v11i2s.6148