Time series forecasting: a comparative analysis of ARIMA, LSTM, and TFT models with missing data handling
TL;DR
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.
Abstract
Time series forecasting plays a critical role in finance, healthcare, and energy applications, where accurate predictions support decision-making and operational efficiency. Traditional approaches such as autoregressive integrated moving average (ARIMA) perform well for linear patterns but often struggle with nonlinear and complex temporal dependencies found in real-world data. Although deep learning methods such as long short-term memory (LSTM) networks and Transformer-based models have shown promise, comprehensive evaluations across multiple domains and under missing-data conditions remain limited. This study compares ARIMA, LSTM, and temporal fusion transformer (TFT) models across three applications: stock price forecasting using SP 500 data, heart-rate prediction from electrocardiogram (ECG)-derived signals, and electricity demand forecasting using Pennsylvania–New Jersey–Maryland (PJM) power grid data. To evaluate robustness under realistic conditions, varying levels of missingness were introduced using missing completely at random (MCAR) and missing at random (MAR) mechanisms. Missing values were handled using forward fill, linear interpolation, and k-nearest neighbors (k-NN) imputation. Results show 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.