Graph-based approach for outlier detection in sequential data and its application on stock market and weather data

Rahmani, Ali Reza; Afra, Salim; Zarour, Omar; Addam, Omar; Koochakzadeh, Negar; Kianmehr, Keivan; Alhajj, Reda; Rokne, Jon G.

doi:10.1016/j.knosys.2014.02.008

Cited by 23 publications

(6 citation statements)

References 22 publications

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“…Stock market are dynamic and accompanied by highly sensitive, non-linear and chaotic characteristics. Available methods have limited ability to deal with the direct relationship between daily social texts, so the results of these models in predicting stock market movements are still far from satisfactory [6], [7]. Wu et al proposed a novel hybrid recurrent neural network (CH-RNN) based on cross-modal attention, using a recurrent neural network to model daily aggregated social texts, and experiments show that the model is effective in predicting stock market volatility improvement is significant [8].…”

Section: Literature Reviewmentioning

confidence: 99%

“…m i,j = { 0，s i ∉g j 1，s i ∈g j or i=j (5) To capture the influence of each stock node in the subgraph, we operate the attention mechanism on the Stock-Subgraph matrix M t . Attention coefficient based on the Softmax function is defined according to the fundamental time-aware vector v i t and time-aware vector v j t ∈g N i t in each subgraph, and the attention factor as a single-layer feed-forward network based on the Softmax function a → is shown in (6).…”

Section: Relationship Updatingmentioning

confidence: 99%

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MDKI: a multi-relational dynamic stock forecasting model based on knowledge inference

Chen

Tan²,

Chen³

et al. 2022

6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022)

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The prediction of the stock price has been a complex issue. Here, we introduce a stock forecasting model based on a dynamic relationship that combines historical transaction data and knowledge extracted from news, which is mainly divided into subgraph and global relation learning two modules. The subgraph relationship is extracted from the existing industry classification and news information in the market, and the global learning module is based on the shared features between subgraphs and combines the attention mechanism to complete the prediction task. Experiments on the Chinese CSI100 stock and CSMAR news datasets show that our proposed model has certain effectiveness.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Relationship Updatingmentioning

confidence: 99%

MDKI: a multi-relational dynamic stock forecasting model based on knowledge inference

Chen

Tan²,

Chen³

et al. 2022

6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022)

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“…Different graph-based abnormality detection (GBAD) algorithms have been proposed [156], where abnormal observations of structural data are identified in the information representing entities, actions and relationships. In [157], the authors propose a graph-based method to discover contextual anomalies in sequential data. Explicitly, the nodes of the graph are clustered into different categories, where each class includes only similar nodes.…”

Section: Feature Extraction (F)mentioning

confidence: 99%

Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives

Himeur,

Ghanem,

Alsalemi

et al. 2020

Preprint

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Enormous amounts of data are being produced everyday by submeters and smart sensors installed in different kinds of buildings. If leveraged properly, that data could assist end-users, energy producers and utility companies in detecting anomalous power consumption and understanding the causes of each anomaly. Therefore, anomaly detection could stop a minor problem to become widespread, costly and time-consuming issue. Moreover, this will help in better decision-making to reduce wasted energy and promote sustainable and energy efficiency behavior. In this regard, this paper is proposed to indepthly review existing frameworks of anomaly detection in power consumption and provide a critical analysis of existing solutions. Specifically, a comprehensive survey is introduced, in which a novel taxonomy is introduced to classify existing algorithms based on different factors adopted in their implementation, such as the machine learning algorithm, feature extraction approach, detection level, computing platform, application scenario and privacy preservation. To the best of the authors' knowledge, this is the first review article that discusses the anomaly detection in building energy consumption. Moving forward, important findings along with domain-specific problems, difficulties and challenges that remain unresolved are thoroughly discussed, including the absence of: (i) precise definitions of anomalous power consumptions, (ii) annotated datasets, (iii) unified metrics to assess the performance of existing solutions, and (iv) platforms for reproducibility. Following, insights about current research trends that anomaly detection technology needs to target for widespreading its application and facilitate its implementation are described before deriving a set of challenging future directions attracting significant research and development attention.

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“…Numerous algorithms have been proposed in the literature that use manifold embedding, or more in general, graph embedding, either explicitly or implicitly, to detect anomalies in data [36][37][38][39][40][41]. A comprehensive review of the literature is out of the scope of the present work, but here we discuss a few relevant examples.…”

Section: Introductionmentioning

confidence: 99%

Outlier Mining Methods Based on Graph Structure Analysis

2019

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Outlier detection in high-dimensional datasets is a fundamental and challenging problem across disciplines that has also practical implications, as removing outliers from the training set improves the performance of machine learning algorithms. While many outlier mining algorithms have been proposed in the literature, they tend to be valid or efficient for specific types of datasets (time series, images, videos, etc.). Here we propose two methods that can be applied to generic datasets, as long as there is a meaningful measure of distance between pairs of elements of the dataset. Both methods start by defining a graph, where the nodes are the elements of the dataset, and the links have associated weights that are the distances between the nodes. Then, the first method assigns an outlier score based on the percolation (i.e., the fragmentation) of the graph. The second method uses the popular IsoMap non-linear dimensionality reduction algorithm, and assigns an outlier score by comparing the geodesic distances with the distances in the reduced space. We test these algorithms on real and synthetic datasets and show that they either outperform, or perform on par with other popular outlier detection methods. A main advantage of the percolation method is that is parameter free and therefore, it does not require any training; on the other hand, the IsoMap method has two integer number parameters, and when they are appropriately selected, the method performs similar to or better than all the other methods tested.

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Graph-based approach for outlier detection in sequential data and its application on stock market and weather data

Cited by 23 publications

References 22 publications

MDKI: a multi-relational dynamic stock forecasting model based on knowledge inference

MDKI: a multi-relational dynamic stock forecasting model based on knowledge inference

Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives

Outlier Mining Methods Based on Graph Structure Analysis

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