Anomaly detection within environmental time series data plays a crucial role in modern monitoring systems, yet it continues to pose challenges due to the inherently complex and nonlinear nature of sensor-generated signals. This study proposes a dual-path approach for time series anomaly detection that combines the expressive capabilities of deep learning with the transparency of classic machine learning techniques. The approach integrates a bidirectional Long Short-term memory (LSTM) autoencoder for extracting temporal features with density-based outlier detection algorithms, specifically Local Outlier Factor (LOF) and Isolation Forest. This methodology effectively models time dependent patterns while maintaining a balance between interpretability and computational cost. The proposed approach shows significant improvements compared to standalone deep learning and conventional statistical approaches, across various evaluation metrics through extensive testing on indoor environmental sensor datasets. The results analyze the different impacts of components on the anomaly detection process: Isolation Forest (49.08%), Reconstruction Error (39.27%), and LOF (11.65%). Using synthetic data with differing noise intensities improved the model’s resilience across diverse anomaly categories—point, contextual, and collective-achieving detection rates above 86%. These findings highlight the approach’s practical value in real-world environmental monitoring by balancing high accuracy and interpretability.