# MSAD: A Deep Dive into Model Selection for Time series Anomaly Detection

arXiv: https://arxiv.org/abs/2510.26643 · HTML: https://mlsanity.com/paper/2510.26643/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 106 references that are in the map.
- First posted on arXiv 30 Oct 2025.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [Early Convolutions Help Transformers See Better](https://mlsanity.com/paper/2106.14881/) (2021)
- [InceptionTime: Finding AlexNet for Time Series Classification](https://mlsanity.com/paper/1909.04939/) (2019)
- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)
- [MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification](https://mlsanity.com/paper/2012.08791/) (2020)
- [A review on outlier/anomaly detection in time series data](https://mlsanity.com/paper/2002.04236/) (2020)
- [Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress](https://mlsanity.com/paper/2009.13807/) (2020)
- [sktime: A Unified Interface for Machine Learning with Time Series](https://mlsanity.com/paper/1909.07872/) (2019)
- [Bake off redux: a review and experimental evaluation of recent time series classification algorithms](https://mlsanity.com/paper/2304.13029/) (2023)
- [Towards a Rigorous Evaluation of Time-series Anomaly Detection](https://mlsanity.com/paper/2109.05257/) (2021)
- [Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark](https://mlsanity.com/paper/1510.03336/) (2015)
- [Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series](https://mlsanity.com/paper/2010.05073/) (2020)
- [Multivariate Industrial Time Series with Cyber-Attack Simulation: Fault Detection Using an LSTM-based Predictive Data Model](https://mlsanity.com/paper/1612.06676/) (2016)
- [Distributed and parallel time series feature extraction for industrial big data applications](https://mlsanity.com/paper/1610.07717/) (2016)
- [Scaled ReLU Matters for Training Vision Transformers](https://mlsanity.com/paper/2109.03810/) (2021)
- [Unsupervised Model Selection for Time-series Anomaly Detection](https://mlsanity.com/paper/2210.01078/) (2022)
- [Dive into Time-Series Anomaly Detection: A Decade Review](https://mlsanity.com/paper/2412.20512/) (2024)
- [Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series](https://mlsanity.com/paper/2207.12208/) (2022)
- [dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series Classification](https://mlsanity.com/paper/2207.12165/) (2022)
- [Matrix Profile Goes MAD: Variable-Length Motif And Discord Discovery in Data Series](https://mlsanity.com/paper/2008.13447/) (2020)
- [Automated Model Selection for Time-Series Anomaly Detection](https://mlsanity.com/paper/2009.04395/) (2020)
- [Isolation Mondrian Forest for Batch and Online Anomaly Detection](https://mlsanity.com/paper/2003.03692/) (2020)
- [Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases](https://mlsanity.com/paper/1509.07823/) (2015)

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

- [In a Streaming World, Should You Stand Still? A Comprehensive Benchmark of Anomaly Detection in Streams](https://mlsanity.com/paper/2609.39215/) (2026)
- [Retrieval Over Training: Similarity search-based Model Selection for Time Series Anomaly Detection](https://mlsanity.com/paper/2610.20243/) (2026)

## Abstract (arXiv)

Anomaly detection is a fundamental task for time series analytics with important implications for the downstream performance of many applications. Despite increasing academic interest and the large number of methods proposed in the literature, recent benchmarks and evaluation studies demonstrated that no overall best anomaly detection methods exist when applied to very heterogeneous time series datasets. Therefore, the only scalable and viable solution to solve anomaly detection over very different time series collected from diverse domains is to propose a model selection method that will select, based on time series characteristics, the best anomaly detection methods to run. Existing AutoML solutions are, unfortunately, not directly applicable to time series anomaly detection, and no evaluation of time series-based approaches for model selection exists. Towards that direction, this paper studies the performance of time series classification methods used as model selection for anomaly detection. In total, we evaluate 234 model configurations derived from 16 base classifiers across more than 1980 time series, and we propose the first extensive experimental evaluation of time series classification as model selection for anomaly detection. Our results demonstrate that model selection methods outperform every single anomaly detection method while being in the same order of magnitude regarding execution time. This evaluation is the first step to demonstrate the accuracy and efficiency of time series classification algorithms for anomaly detection, and represents a strong baseline that can then be used to guide the model selection step in general AutoML pipelines. Preprint version of an article accepted at the VLDB Journal.