Effects of missing data imputation methods on univariate blood pressure time series data analysis and forecasting with ARIMA and LSTM

Abstract Background Missing observations within the univariate time series are common in real-life and cause analytical problems in the flow of the analysis. Imputation of missing values is an inevitable step in every incomplete univariate time series. Most of the existing studies focus on comparing...

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Автори:	Nicholas Niako, Jesus D. Melgarejo, Gladys E. Maestre, Kristina P. Vatcheva
Формат:	Artigo
Мова:	Inglês
Опубліковано:	BMC 2024-12-01
Серія:	BMC Medical Research Methodology
Предмети:	Univariate time series Missing data imputation ARIMA LSTM Forecasting Ambulatory blood pressure
Онлайн доступ:	https://doi.org/10.1186/s12874-024-02448-3
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Effects of missing data imputation methods on univariate blood pressure time series data analysis and forecasting with ARIMA and LSTM

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