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 the distributions of imputed data. There is a gap of knowledge on how different imputation methods for univariate time series affect the forecasting performance of time series models. We evaluated the prediction performance of autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) network models on imputed time series data using ten different imputation techniques.

Methods

Missing values were generated under missing completely at random (MCAR) mechanism at 10%,

SUBMITTER: Niako N 

PROVIDER: S-EPMC11670515 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature