A Hybrid LSTM-Attention Approach for Missing Data Imputation in IoT Time Series

IoT systems generate large volumes of time-series data, but sensor malfunctions often lead to missing values that reduce the effectiveness of machine learning models. We propose a novel hybrid architecture that interleaves Long Short-Term Memory (LSTM) layers with a multihead attention mechanism, where the first LSTM layer captures local temporal dependencies, the attention layer highlights long-range relationships, and the second LSTM layer integrates these features into a coherent sequence. This structured design, unlike conventional orderings, enhances robustness against irregular missingness. Evaluated on six months of soil surface temperature data with simulated missing rates from 10% to 90%, in terms of mean absolute error (MAE), R² score (R²) and root mean squared error (RMSE). Performance is also compared to a statistical technique k-Nearest Neighbour (KNN) and a deep learning technique Bidirectional Recurrent Imputation for Time Series (BRITS) baselines. Importantly, training with simulated missingness further improved generalization, underscoring the novelty and practical relevance of the proposed approach for real-world IoT scenarios.