# Real-time Drift Detection on Time-series Data

arXiv: https://arxiv.org/abs/2110.06383 · HTML: https://mlsanity.com/paper/2110.06383/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 9 references that are in the map.
- First posted on arXiv 12 Oct 2021.

## Builds on (most cited first, 2 of 2)

- [Concept Drift Detection via Equal Intensity k-means Space Partitioning](https://mlsanity.com/paper/2004.11587/) (2020)
- [Boosted Embeddings for Time Series Forecasting](https://mlsanity.com/paper/2104.04781/) (2021)

## Built on by (most cited first, 1 of 1)

- [CROP: Towards Distributional-Shift Robust Reinforcement Learning using Compact Reshaped Observation Processing](https://mlsanity.com/paper/2304.13616/) (2023)

## Abstract (arXiv)

Practical machine learning applications involving time series data, such as firewall log analysis to proactively detect anomalous behavior, are concerned with real time analysis of streaming data. Consequently, we need to update the ML models as the statistical characteristics of such data may shift frequently with time. One alternative explored in the literature is to retrain models with updated data whenever the models accuracy is observed to degrade. However, these methods rely on near real time availability of ground truth, which is rarely fulfilled. Further, in applications with seasonal data, temporal concept drift is confounded by seasonal variation. In this work, we propose an approach called Unsupervised Temporal Drift Detector or UTDD to flexibly account for seasonal variation, efficiently detect temporal concept drift in time series data in the absence of ground truth, and subsequently adapt our ML models to concept drift for better generalization.