# Time-Series Anomaly Detection: Overview and New Trends

Qinghua Liu, Paul Boniol, Themis Palpanas, John Paparrizos

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Qinghua Liu, Paul Boniol, Themis Palpanas, John Paparrizos. Time-Series Anomaly Detection: Overview and New Trends. Proceedings of the VLDB Endowment (PVLDB), 2024, 17 (12), pp.4229-4232. 10.14778/3685800.3685842. hal-04708918

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# Time-Series Anomaly Detection: Overview and New Trends

Qinghua Liu*

Paul Boniol*

Themis Palpanas

John Paparrizos

The Ohio State University

Inria, DI ENS, PSL, CNRS

Université Paris Cité; IUF

The Ohio State University

liu.11085@osu.edu

paul.boniol@inria.fr

themis@mi.parisdescartes.fr

paparrizos.1@osu.edu

## ABSTRACT

Anomaly detection is a fundamental data analytics task across scientific fields and industries. In recent years, an increasing interest has been shown in the application of anomaly detection techniques to time series. In this tutorial, we take a holistic view of anomaly detection in time series and comprehensively cover detection algorithms ranging from the 1980s to the most current state-of-the-art techniques. Importantly, the scope of this tutorial extends beyond algorithmic discussion, delving into the latest advancements in benchmarking and evaluation measures for this area. In particular, our interactive systems enable the exploration of methods and benchmarking results, thereby promoting user comprehension. Furthermore, this tutorial extensively explores automated solutions for unsupervised model selection, introduces a new taxonomy, and engages with the challenges and recent findings, particularly the difficulty for these solutions to outperform simple random choice. Driven by the limited generalizability of current detection algorithms, we review recent applications of Foundation Models for anomaly detection to motivate further research in the area.

## PVLDB Reference Format:

Qinghua Liu, Paul Boniol, Themis Palpanas, and John Paparrizos.

Time-Series Anomaly Detection: Overview and New Trends. PVLDB, 17(12): 4229 - 4232, 2024.

doi:10.14778/3685800.3685842

## 1 INTRODUCTION

A wide range of sensing, networking, and processing solutions enable the collection of large amounts of data over time [25, 26, 28–30, 32, 38, 39, 41, 46]. Analytical tasks over such ordered sequences of real-valued data, known as *time series*, are becoming increasingly important in virtually every domain [3, 16–18, 35, 42–44, 47–52].

Anomaly detection has received ample academic and industrial attention over the past decades. *Anomalies* refer to data points that do not conform to some notion of normality or an expected behavior based on previously observed data. In practice, anomalies can correspond to erroneous data (e.g., broken sensors), or data of interest (e.g., anomalous behavior of the measured system) [1]. Detecting such cases is crucial for many applications [23]. Moreover, as illustrated in Figure 1, anomaly detection applied to time series (compared to other data types) is attracting more interest lately. Multiple surveys, benchmarks, and experimental studies summarize

<figure><img src="image_3.png" /><figcaption>Figure 1: Evolution of the Google interest score for anomaly detection over time series, images, text, and video.</figcaption></figure>

and analyze the state-of-the-art methods [4, 10, 11, 45, 55], exploring different aspects of the problem.

[Tutorial Overview] As the interest in Time-Series Anomaly Detection (TSAD) expands across scientific fields and industries, this tutorial provides a comprehensive view of this task. We start with definitions for time series and anomalies. Our goal is four-fold: (i) introduce the motivation related to the anomaly detection task in time series by describing different types of time-series anomaly and the taxonomy of anomaly detection algorithms proposed by different communities; (ii) describe recently proposed benchmarks and experimental evaluations along with the discussion on the challenges and problems inherent in the evaluation process; (iii) explore the latest trends in automated anomaly detection solutions for time series, providing a taxonomy and insights for each methodology alongside experimental investigations; (iv) discuss the challenges and opportunities for time-series anomaly detection including Foundation Model-empowered TSAD.

This tutorial features (i) an extensive review of literature spanning several decades, from early methods of the 1980s to the latest state-of-the-art approaches, and developed in different communities, from data management to machine learning; (ii) insights into the recent advancements in automated solutions, enriching the research landscape with novel perspectives; (iii) interactive systems for exploring a method’s computation steps and experimental evaluation, making complex concepts engaging. We hope this tutorial will equip the audience with a fundamental comprehension of TSAD and inspire them to work in this dynamic and evolving area.
[Relation to Previous Tutorials] Recent tutorials related to anomaly detection have focused (i) on specific types of methods (such as deep-learning methods [15]) ignoring several of the state-of-the-art methods, (ii) on specific types of temporal data (such as spatiotemporal data [59]), or (iii) on a much more general topic (only briefly discussing anomaly detection as a subpart of time series analysis [57]). Earlier versions of this tutorial [8, 9] focused on describing the state-of-the-art time-series anomaly detection methods and their operation.

In contrast, this tutorial undergoes significant expansion, offering a detailed explanation of recently proposed methodologies with interactive exploration for the audience. Beyond the introduction of anomaly detection techniques, this tutorial adopts a

<footnote>*These authors contributed equally to this work.
This work is licensed under the Creative Commons BY-NC-ND 4.0 International License. Visit https://creativecommons.org/licenses/by-nc-nd/4.0/ to view a copy of this license. For any use beyond those covered by this license, obtain permission by emailing info@vldb.org. Copyright is held by the owner/author(s). Publication rights licensed to the VLDB Endowment.
Proceedings of the VLDB Endowment, Vol. 17, No. 12 ISSN 2150-8097.
doi:10.14778/3685800.3685842</footnote>
<figure><img src="image_4.png" /><figcaption>Figure 2: Process-centric anomaly detection taxonomy.</figcaption></figure>

comprehensive perspective on the field, with a focus on evaluation
measures and benchmark analysis. A brand new section has been
introduced, outlining automated solutions with a taxonomy and
evaluation results. Furthermore, the tutorial explores the latest ad-
vancements in applying Foundation Models to time series analysis
and anomaly detection, opening up new research opportunities. To
our knowledge, this is the first tutorial that (i) extensively covers
the time-series anomaly detection landscape; (ii) incorporates the
latest developments in automated solutions and Foundation Models
for TSAD; and (iii) elaborates on concepts through experimental
studies and use of interactive demonstration systems.

[Audience and Expected Background] This tutorial is for re-
searchers and data analysts and will focus on recent advances in
TSAD. The tutorial aims to initiate new collaborations between
members of the data management community and data science prac-
titioners in various domains and increase the interest in anomaly
detection for time series. We will present material that includes the
necessary background to follow the entire presentation and tech-
nical details of the current state-of-the-art solutions for anomaly
detection. We will also discuss the drawbacks of existing solutions
and the open problems. Both experts and newcomers in the area
will be able to follow the material and benefit from the tutorial.

2 TUTORIAL SCOPE

In this **3 hour lecture-style tutorial**, we will go through the prob-
lem of anomaly detection in time series, starting from fundamental
definitions to open problems and opportunities.

## 2.1 Introduction, Motivation and Foundations

We will start by discussing examples of scientific and industrial
applications that rely on TSAD.

[Type of Time Series] We first introduce the different types of time series. Specifically, we define the time series as an ordered sequence of real values on one (for *univariate* time series) or multiple dimensions (for *multivariate* time series). Moreover, we define *static* and *streaming* time series as sequences with a fixed length or continuously arriving subsequences. Finally, the normal behavior (i.e., subsequences representing the normal and recurrent behavior) might change over time. In this case, we differentiate *single* and *multiple normalities* time series.

[Type of Anomalies] We then introduce the different types of anomalies. The first two categories, *point* and *contextual* anomalies, refer to data points deviating remarkably from the rest of the data globally or given a specific context, respectively. The third category, *Collective* anomalies, corresponds to sequences of points that do not repeat a typical (previously observed) pattern. Moreover, their

<figure><img src="image_5.png" /><figcaption>(a) GraphAn system</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>(b) Theseus system</figcaption></figure>

<caption>Figure 3: Example of interactive systems that (a) allow the user to dive into computational steps [6], and (b) depicts experimental results [7].</caption>

combination also matters. For instance, we need to differentiate
time series containing *single* anomalies from time series contain-
ing *multiple* anomalies (either *similar* or *different*). For all these
definitions and classes, we will provide explicit examples.

## 2.2 Taxonomy of Anomaly Detection Methods

We will dive into the different anomaly detection methods proposed
in the literature. As many papers appear every year proposing new
methods for anomaly detection in time series based on different
applications [4, 22], it is beyond our scope to cover all proposed
methods extensively here. In this tutorial, we will summarize the
popular categories of methods.

[Classification by Inputs] We will first mention the three categories of methods based on the external knowledge provided to them. First, *unsupervised* methods take time series as input and are not provided by any other information. Then, *semi-supervised* methods take as input time series without any anomalies and are trained on normal data only. Finally, *supervised* methods take as input separately both normal and abnormal data. Thus, the model is trained to discriminate the anomalies from the normality.

[Classification by Methodologies] Then, we will describe the fol-
lowing categories of methods as depicted in Figure 2. First, *distance-
based* approaches analyze subsequences by utilizing distances to a
given model to detect anomalies. Second, *density-based* methods
focus on detecting recurring or isolated behaviors by evaluating
the density of the points or subsequences space. Third, *prediction-
based* methods consist of two main categories: forecasting-based
and reconstruction-based methods. The former, such as recurrent
or convolutional neural network-based [34, 36], use the past values
as input, predict the following, and use the forecasting error as
an anomaly score. The latter, such as the autoencoder-based ap-
proach [53], are trained to reconstruct the time series and use the
reconstruction error as an anomaly score.

## 2.3 Evaluating Anomaly Detection Methods

After describing various anomaly detectors, we will focus on how
to evaluate them. The choice of benchmarks and accuracy measures
may significantly bias the evaluation.

[Evaluation Measures] We will start by describing the evalu-
ation measures. Briefly, we will first discuss traditional measures,
such as Precision, Recall, and F-score, that assess the methods by
assuming each time-series point can be marked as an anomaly or
not (e.g., by a threshold on an anomaly score). We will then discuss
range-based variants [56] that aim to overcome shortcomings of
traditional measures when evaluating time series containing sub-
sequence anomalies. We will discuss Area Under the Curve (AUC)
measures that, contrary to the previous measures, eliminate the
need to define a threshold. We will finally discuss Volume Under
the Surface (VUS) [40] measures, which provide more robustness.

[Benchmark Study] Then, we will discuss recent benchmarks proposed for anomaly detection in time series task [24, 27, 45, 54]. Such benchmarks provide an extensive collection of time series from various domains and evaluate multiple methods belonging to the aforementioned categories. In this tutorial, we will discuss the results and conclusions of these recent benchmarks, as well as the criticisms that have been expressed about the characteristics and suitability of some datasets for this task [58].

[Interactive Exploration and Interpret Ability] In contrast to previous tutorials, this tutorial employs interactive exploration to assist in understanding the concepts. It is important for users to overcome the lack of interpretability that anomaly detection methods can have [34, 53]. This is becoming possible by recently proposed prototype systems [6, 7, 12], which enable users to interactively explore methods and their inner workings and thus, better understand the different computation steps. In this tutorial, we will discuss and demonstrate recently proposed systems for anomaly detection, along with systems that allow the user to explore large-scale experimental evaluation studies. As depicted in Figure 3, we will use such systems during the tutorial to explain interactively the anomaly detection methods and the experimental conclusions.

## 2.4 Automated Solutions for TSAD

Recent benchmarks and evaluation studies (described in the previous sections) have demonstrated that no overall best anomaly detection methods exist when applied on very heterogeneous time series (i.e., coming from very different domains). The primary question that arises is: How can we automatically identify the most accurate anomaly detector in the time series dataset? In this tutorial, we will present a taxonomy for the methods proposed in this field and provide recent experimental evaluations on them.

[AutoTSAD Taxonomy] We will begin by outlining the pipeline of automated solutions as illustrated in Figure ?? and then delve into different categories of methods. The works in this field can be classified into two main categories as depicted in Figure 4. Model Selection refers to identifying the optimal model and its corresponding hyperparameters from a predefined set. Subsequently, the selected model is utilized for anomaly detection. In this category, internal evaluation methods evaluate the effectiveness of a model by using surrogate metrics for anomaly detection, independent of external data such as ground truth labels for anomalies [19, 33] and meta-learning-based methods leverage the knowledge of the performance of various anomaly detectors on historical labeled datasets to enable the automatic model selection for new datasets [37, 55]. On the other hand, Model Generation entails the construction of a completely new model based on the predefined set. This newly generated model then can operate independently as an anomaly detector. Ensembling-based methods involve constructing models that combine ensembles from the predefined set [2, 62]. Pseudo-label-based

methods generate pseudo-labels to transform the unsupervised
 anomaly detection problem into a supervised framework [14, 60].

**[Experimental Results]** We will then introduce a recent study [31] on evaluating automated solutions and provide insights into the design choices. The study reveals that a majority of these solutions fail to outperform the simple random choice, indicating both the necessity for further investigation and the substantial potential within this domain. Furthermore, we will delve into the various scenarios in which they are applicable.

## 2.5 Challenges and Opportunities

In the last part of the tutorial, we will discuss the challenges and
perspectives for next-generation time-series anomaly detection.

[Foundation Model Empowered TSAD] Foundation models are an emerging paradigm of self-supervised (or) unsupervised learning on large datasets [5]. They have demonstrated adaptability across modalities, extending beyond web data to scientific domains. Scaling the model, dataset size and data diversity have also been shown to result in remarkable transfer capabilities and excellent few-shot learning on novel datasets and tasks [13]. Their capacity to process and analyze a large amount of data with temporal dependencies [21] paves the way for more sophisticated and precise detection of anomalies. In this section, we will explore recent advances in applying foundation models for TSAD, categorizing the approaches into two types: (i) adapting foundation models from other modalities such as texts and images for TSAD tasks [61], and (ii) developing foundation models specifically for time-series to perform anomaly detection [20]. Subsequently, we will review the design choices and insights these methodologies offer.

[Towards Automated Anomaly Detection] Given the absence of labeled data and one-fits-all anomaly detectors in unsupervised anomaly detection, it is challenging to determine the most accurate detector for various applications. Moreover, achieving optimal performance requires in-depth knowledge of the myriad of methods. This necessity drives data analysts into an exhaustive and time-consuming trial-and-error procedure process, making selecting suitable anomaly detectors cumbersome. In this section, we will further explore automated solutions (Section 2.4) and outline recent efforts. Following this, we will discuss prospective research directions, encompassing domain generalization and the development of incremental automated solutions. This field represents a

<figure><img src="image_7.png" /><figcaption>Figure 4: A taxonomy of AutoTSAD.</figcaption></figure>
promising area of research, and we hope our tutorial can serve as a
catalyst to steer research efforts towards this direction.

## 2.6 Conclusions

We will conclude this tutorial by summarizing the main insights
obtained from recent benchmarks of various anomaly detectors and
automated solutions. Finally, we will summarize the new trends and
challenges with the advent of automated solutions and Foundation
Models and discuss the open research directions.

## 3 PRESENTERS

Qinghua Liu is a Ph.D. student at The Ohio State University and a member of The DATUM Lab. His research interests include time-series analysis and anomaly detection. He received the B.Eng. degree in Electronic Engineering from Tianjin University in 2022. Previously, he worked at SRIBD, CASIA and JD.com Inc.

Paul Boniol is a researcher at Inria, member of the VALDA project-team. Previously, he worked at ENS Paris-Saclay (Centre Borelli), Université Paris Cité, EDF Research lab, and Ecole Polytechnique. His research interests lie between data analytics, machine learning, and time-series analysis. His Ph.D. thesis focused on subsequence anomaly detection and time-series classification. His work has been published in the top data management and analytics venues.

Themis Palpanas is an elected Senior Member of the French University Institute (IUF), and Distinguished Professor of computer science at Universite Paris Cite (France). He has authored 14 patents, received 3 best paper awards and the IBM SUR award, has been Program Chair for VLDB 2025 and IEEE BigData 2023. General Chair for VLDB 2013, and has served Editor in Chief for BDR. He has been working in the fields of Data Series Management and Analytics for more than 15 years, and has developed several of the state of the art techniques. He has delivered 19 tutorials in top conferences.

John Paparrizos is an assistant professor at The Ohio State University, leading The DATUM Lab. His research focuses on adaptive solutions for data-intensive and machine-learning applications. His doctoral work was recognized at the 2019 ACM SIGKDD Doctoral Dissertation Award competition. He has also received the inaugural ACM SIGMOD Research Highlight Award, a NetApp Faculty Award, and the 2023 IEEE TCDE Rising Star Award. His ideas have been widely adopted across scientific areas, Fortune 100-500 companies (e.g., Exelon and Nokia), and organizations such as ESA.

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