# LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Time Series Anomaly Detection

arXiv: https://arxiv.org/abs/2310.05668 · HTML: https://mlsanity.com/paper/2310.05668/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 53 references that are in the map.
- First posted on arXiv 9 Oct 2023.

## Builds on (most cited first, 17 of 17)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks](https://mlsanity.com/paper/1807.03888/) (2018)
- [Maximum Classifier Discrepancy for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1712.02560/) (2017)
- [Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding](https://mlsanity.com/paper/1802.04431/) (2018)
- [Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One](https://mlsanity.com/paper/1912.03263/) (2019)
- [Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy](https://mlsanity.com/paper/2110.02642/) (2021)
- [Unsupervised Domain Adaptation with Residual Transfer Networks](https://mlsanity.com/paper/1602.04433/) (2016)
- [Unified Deep Supervised Domain Adaptation and Generalization](https://mlsanity.com/paper/1709.10190/) (2017)
- [Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications](https://mlsanity.com/paper/1802.03903/) (2018)
- [A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data](https://mlsanity.com/paper/1811.08055/) (2018)
- [Expectation Propagation for approximate Bayesian inference](https://mlsanity.com/paper/1301.2294/) (2013)
- [RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks](https://mlsanity.com/paper/2002.09545/) (2020)
- [Rethinking Assumptions in Deep Anomaly Detection](https://mlsanity.com/paper/2006.00339/) (2020)
- [CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms](https://mlsanity.com/paper/2111.03753/) (2021)
- [Learning Competitive and Discriminative Reconstructions for Anomaly Detection](https://mlsanity.com/paper/1903.07058/) (2019)

## Built on by (most cited first, 9 of 9)

- [See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers](https://mlsanity.com/paper/2411.02465/) (2024)
- [Learning Multi-Pattern Normalities in the Frequency Domain for Efficient Time Series Anomaly Detection](https://mlsanity.com/paper/2311.16191/) (2023)
- [Enhancing Web Service Anomaly Detection via Fine-grained Multi-modal Association and Frequency Domain Analysis](https://mlsanity.com/paper/2501.16875/) (2025)
- [Cluster-Wide Task Slowdown Detection in Cloud System](https://mlsanity.com/paper/2408.04236/) (2024)
- [Self-Supervised Spatial-Temporal Normality Learning for Time Series Anomaly Detection](https://mlsanity.com/paper/2406.19770/) (2024)
- [Synergizing Large Language Models and Task-specific Models for Time Series Anomaly Detection](https://mlsanity.com/paper/2501.05675/) (2025)
- [Pi-transformer: A prior-informed dual-attention model for multivariate time-series anomaly detection](https://mlsanity.com/paper/2509.19985/) (2025)
- [Drift-Based Dataset Stability Benchmark](https://mlsanity.com/paper/2512.23762/) (2025)
- [DeMa: Dual-Path Delay-Aware Mamba for Efficient Multivariate Time Series Analysis](https://mlsanity.com/paper/2601.05527/) (2026)

## Abstract (arXiv)

Most of current anomaly detection models assume that the normal pattern remains same all the time. However, the normal patterns of Web services change dramatically and frequently. The model trained on old-distribution data is outdated after such changes. Retraining the whole model every time is expensive. Besides, at the beginning of normal pattern changes, there is not enough observation data from the new distribution. Retraining a large neural network model with limited data is vulnerable to overfitting. Thus, we propose a Light and Anti-overfitting Retraining Approach (LARA) for deep variational auto-encoder based time series anomaly detection methods (VAEs). This work aims to make three novel contributions: 1) the retraining process is formulated as a convex problem and can converge at a fast rate as well as prevent overfitting; 2) designing a ruminate block, which leverages the historical data without the need to store them; 3) mathematically proving that when fine-tuning the latent vector and reconstructed data, the linear formations can achieve the least adjusting errors between the ground truths and the fine-tuned ones. Moreover, we have performed many experiments to verify that retraining LARA with even 43 time slots of data from new distribution can result in its competitive F1 Score in comparison with the state-of-the-art anomaly detection models trained with sufficient data. Besides, we verify its light overhead.