Anomaly Detection in Control Systems With Interval Dissimilarity

Kemmerling, Marco; Combrzynski-Nogala, Maciej; Hasler, Marc; Enslin, Chrismarie; Lütticke, Daniel; Schmitt, Robert

doi:10.1109/ki55792.2022.9925972

Cited by 2 publications

(3 citation statements)

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“…For the experiments examining the synthetic data, three datasets with different sizes and complexities are generated based on predefined equations, similarly to [44]. In the generated datasets, X i t denotes the i-th variable at time t, and is the Gaussian-distributed random noise following N (0, 1) with distinct random seeds.…”

Section: Description Of the Datasetsmentioning

confidence: 99%

A Data-Driven Two-Phase Multi-Split Causal Ensemble Model for Time Series

Kemmerling

Buschmann

et al. 2023

Symmetry

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Causal inference is a fundamental research topic for discovering the cause–effect relationships in many disciplines. Inferring causality means identifying asymmetric relations between two variables. In real-world systems, e.g., finance, healthcare, and industrial processes, time series data from sensors and other data sources offer an especially good basis to infer causal relationships. Therefore, many different time series causal inference algorithms have been proposed in recent years. However, not all algorithms are equally well-suited for a given dataset. For instance, some approaches may only be able to identify linear relationships, while others are applicable for non-linearities. Algorithms further vary in their sensitivity to noise and their ability to infer causal information from coupled vs. non-coupled time series. As a consequence, different algorithms often generate different causal relationships for the same input. In order to achieve a more robust causal inference result, this publication proposes a novel data-driven two-phase multi-split causal ensemble model to combine the strengths of different causality base algorithms. In comparison to existing approaches, the proposed ensemble method reduces the influence of noise through a data partitioning scheme in a first phase. To achieve this, the data are initially divided into several partitions and the base causal inference algorithms are applied to each partition. Subsequently, Gaussian mixture models are used to identify the causal relationships derived from the different partitions that are likely to be valid. In the second phase, the identified relationships from each base algorithm are then merged based on three combination rules. The proposed ensemble approach is evaluated using multiple metrics, among them a newly developed evaluation index for causal ensemble approaches. We perform experiments using three synthetic datasets with different volumes and complexity, which have been specifically designed to test causality detection methods under different circumstances while knowing the ground truth causal relationships. In these experiments, our causality ensemble outperforms each of its base algorithms. In practical applications, the use of the proposed method could hence lead to more robust and reliable causality results.

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Section: Description Of the Datasetsmentioning

confidence: 99%

A Data-Driven Two-Phase Multi-Split Causal Ensemble Model for Time Series

Kemmerling

Buschmann

et al. 2023

Symmetry

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“…An iteration is completed by updating a search tree statistic, which guides the selection of a promising node in the next iteration of the algorithm. In MCTS, for instance, the node for expansion is selected based on an upper confidence bound statistic, and an iteration is completed by backpropagating a reward obtained from a so-called roll-out process, which estimates the cost of the synthetic route [17,18].…”

Section: Introductionmentioning

confidence: 99%

“…MCTS are used in a variety of fields and hyperparameters have been explored for a variety of MCTS implementations. The computational budget expressed as the number of iterations and the balance between exploitation and exploration have been most commonly been investigated [17,18], but it is unlikely that the learnings from previous studies on other fields can be transferred to retrosynthesis, as the implementation of the MCTS algorithm is significantly different. Although hyperparameters are likely to influence the output of the multi-step retrosynthesis experiments, the process of selecting (or optimizing) them is largely unexplored in this field.…”

Section: Introductionmentioning

confidence: 99%

Data‐driven approaches for identifying hyperparameters in multi‐step retrosynthesis

Westerlund,

Barge,

Mervin

et al. 2023

Molecular Informatics

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Multi‐step retrosynthesis problem can be solved by a search algorithm, such as Monte Carlo tree search (MCTS). The performance of multistep retrosynthesis, as measured by a trade‐off in search time and route solvability, therefore depends on the hyperparameters of the search algorithm. In this paper, we demonstrated the effect of three MCTS hyperparameters (number of iterations, tree depth, and tree width) on metrics such as Linear integrated speed‐accuracy score (LISAS) and Inverse efficiency score which consider both route solvability and search time. This exploration was conducted by employing three data‐driven approaches, namely a systematic grid search, Bayesian optimization over an ensemble of molecules to obtain static MCTS hyperparameters, and a machine learning approach to dynamically predict optimal MCTS hyperparameters given an input target molecule. With the obtained results on the internal dataset, we demonstrated that it is possible to identify a hyperparameter sets which outperform the current AiZynthFinder default setting and appeared optimal across a variety of target input molecules, both on proprietary and public datasets. The settings identified with the in‐house dataset reached a solvability of 93% and median search time of 151s for the in‐house dataset, and a 74% solvability and 114s for the ChEMBL dataset. These numbers can be compared to the current default settings which solved 85% and 73% during a median time of 110s and 84s, for in‐house and ChEMBL, respectively.

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Anomaly Detection in Control Systems With Interval Dissimilarity

Cited by 2 publications

References 0 publications

A Data-Driven Two-Phase Multi-Split Causal Ensemble Model for Time Series

A Data-Driven Two-Phase Multi-Split Causal Ensemble Model for Time Series

Data‐driven approaches for identifying hyperparameters in multi‐step retrosynthesis

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