# Exploring the impact of Optimised Hyperparameters on Bi-LSTM-based Contextual Anomaly Detector

arXiv: https://arxiv.org/abs/2501.15053 · HTML: https://mlsanity.com/paper/2501.15053/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 30 references that are in the map.
- First posted on arXiv 25 Jan 2025.

## Builds on (most cited first, 6 of 6)

- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [SALAD: Self-Adaptive Lightweight Anomaly Detection for Real-time Recurrent Time Series](https://mlsanity.com/paper/2104.09968/) (2021)
- [Wisdom of the Contexts: Active Ensemble Learning for Contextual Anomaly Detection](https://mlsanity.com/paper/2101.11560/) (2021)
- [RePAD2: Real-Time, Lightweight, and Adaptive Anomaly Detection for Open-Ended Time Series](https://mlsanity.com/paper/2303.00409/) (2023)
- [RoLA: A Real-Time Online Lightweight Anomaly Detection System for Multivariate Time Series](https://mlsanity.com/paper/2305.16509/) (2023)
- [Impact of Recurrent Neural Networks and Deep Learning Frameworks on Real-time Lightweight Time Series Anomaly Detection](https://mlsanity.com/paper/2407.18439/) (2024)

## Abstract (arXiv)

The exponential growth in the usage of Internet of Things in daily life has caused immense increase in the generation of time series data. Smart homes is one such domain where bulk of data is being generated and anomaly detection is one of the many challenges addressed by researchers in recent years. Contextual anomaly is a kind of anomaly that may show deviation from the normal pattern like point or sequence anomalies, but it also requires prior knowledge about the data domain and the actions that caused the deviation. Recent studies based on Recurrent Neural Networks (RNN) have demonstrated strong performance in anomaly detection. This study explores the impact of automatically tuned hyperparamteres on Unsupervised Online Contextual Anomaly Detection (UoCAD) approach by proposing UoCAD with Optimised Hyperparamnters (UoCAD-OH). UoCAD-OH conducts hyperparameter optimisation on Bi-LSTM model in an offline phase and uses the fine-tuned hyperparameters to detect anomalies during the online phase. The experiments involve evaluating the proposed framework on two smart home air quality datasets containing contextual anomalies. The evaluation metrics used are Precision, Recall, and F1 score.