Context-Aware Multi-Step Imputation for Univariate Temperature Time Series Using BiLSTM

Missing data imputation remains a persistent challenge in Internet of Things and sensor-based environments, particularly for applications related to smart cities and urban data analysis. In this study, we compare imputation methods for univariate urban temperature time series collected from three distinct weather stations with significant missing segments. To evaluate the performance of each method, artificial gaps of varying lengths are systematically introduced into the data. We assess the accuracy of eXtreme Gradient Boosting (XGBoost) regression in comparison with more conventional techniques such as spline interpolation. While XGBoost performs well for short and medium gaps, its effectiveness diminishes for longer gaps due to its limited capacity to capture the cyclic patterns inherent in temperature data. To address this, we propose a context-aware Bidirectional Long Short-Term Memory network that captures both past and future temporal dependencies. The model performs multi-step predictions using a one-day context window, avoiding the cumulative error typically propagated in recursive single-step approaches. We also compared the performance of our proposed model with Multi-layer Perceptron. Our results show that the Bidirectional Long Short-Term Memory model performs better than MLP overall and significantly outperforms XGBoost, especially for long missing blocks. These findings demonstrate the effectiveness of context-aware deep learning—particularly Bidirectional Long Short-Term Memory model for robust imputation of cyclic temperature time series in sensor networks.