Environmental air anomaly detection is crucial for ensuring the healthy growth of livestock in smart pig farming systems. This study focuses on four key environmental variables within pig housing: temperature, relative humidity, carbon dioxide concentration, and ammonia concentration. Based on these variables, it proposes a novel encoder–decoder architecture for anomaly detection based on continuous-time models. The proposed framework consists of two embedding layers: an encoder module built around a continuous-time neural network, and a decoder composed of multilayer perceptrons. The model is trained in a self-supervised manner and optimized using a reconstruction-based loss function. Extensive experiments are conducted on a multivariate multi-sequence dataset collected from real-world pig farming environments. Experimental results show that the proposed architecture significantly outperforms existing transformer-based methods, achieving 92.39% accuracy, 92.08% precision, 85.84% recall, and an F₁ score of 88.19%. These findings highlight the practical value of accurate anomaly detection in smart farming systems; timely identification of environmental irregularities enables proactive intervention, reduces animal stress, minimizes disease risk, and ultimately improves the sustainability and productivity of livestock operations.