Mohamad Forouzanfar

Time series forecasting: a comparative analysis of ARIMA, LSTM, and TFT models with missing data handling

This study compares ARIMA, LSTM, and temporal fusion transformer (TFT) models across three applications and shows that TFT consistently achieved superior forecasting performance and demonstrated greater robustness to increasing missingness, while k-NN generally provided the most effective imputation performance across datasets.