# A Bi-LSTM Autoencoder Framework for Anomaly Detection -- A Case Study of a Wind Power Dataset

arXiv: https://arxiv.org/abs/2303.09703 · HTML: https://mlsanity.com/paper/2303.09703/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 17 Mar 2023.

## Builds on (most cited first, 8 of 8)

- [A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencoder](https://mlsanity.com/paper/1711.00614/) (2017)
- [Unsupervised and Semi-supervised Anomaly Detection with LSTM Neural Networks](https://mlsanity.com/paper/1710.09207/) (2017)
- [Collective Anomaly Detection based on Long Short Term Memory Recurrent Neural Network](https://mlsanity.com/paper/1703.09752/) (2017)
- [A Survey on Anomaly Detection for Technical Systems using LSTM Networks](https://mlsanity.com/paper/2105.13810/) (2021)
- [LSTM-Autoencoder based Anomaly Detection for Indoor Air Quality Time Series Data](https://mlsanity.com/paper/2204.06701/) (2022)
- [Smart Metering System Capable of Anomaly Detection by Bi-directional LSTM Autoencoder](https://mlsanity.com/paper/2112.03275/) (2021)
- [Guiding the Sequential Experiments in Autonomous Experimentation Platforms through EI-based Bayesian Optimization and Bayesian Model Averaging](https://mlsanity.com/paper/2302.13360/) (2023)
- [Applications of Federated Learning in Manufacturing: Identifying the Challenges and Exploring the Future Directions with Industry 4.0 and 5.0 Visions](https://mlsanity.com/paper/2302.13514/) (2023)

## Built on by (most cited first, 2 of 2)

- [Application of Attention Mechanism with Bidirectional Long Short-Term Memory (BiLSTM) and CNN for Human Conflict Detection using Computer Vision](https://mlsanity.com/paper/2502.18555/) (2025)
- [Process mining-driven modeling and simulation to enhance fault diagnosis in cyber-physical systems](https://mlsanity.com/paper/2506.21502/) (2025)

## Abstract (arXiv)

Anomalies refer to data points or events that deviate from normal and homogeneous events, which can include fraudulent activities, network infiltrations, equipment malfunctions, process changes, or other significant but infrequent events. Prompt detection of such events can prevent potential losses in terms of finances, information, and human resources. With the advancement of computational capabilities and the availability of large datasets, anomaly detection has become a major area of research. Among these, anomaly detection in time series has gained more attention recently due to the added complexity imposed by the time dimension. This study presents a novel framework for time series anomaly detection using a combination of Bidirectional Long Short Term Memory (Bi-LSTM) architecture and Autoencoder. The Bi-LSTM network, which comprises two unidirectional LSTM networks, can analyze the time series data from both directions and thus effectively discover the long-term dependencies hidden in the sequential data. Meanwhile, the Autoencoder mechanism helps to establish the optimal threshold beyond which an event can be classified as an anomaly. To demonstrate the effectiveness of the proposed framework, it is applied to a real-world multivariate time series dataset collected from a wind farm. The Bi-LSTM Autoencoder model achieved a classification accuracy of 96.79% and outperformed more commonly used LSTM Autoencoder models.