[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Guo et al., 2015 - Google Patents

The Financial Data of Anomaly Detection Research Based on Time Series

Guo et al., 2015

Document ID
2672582144713379390
Author
Guo C
Xu L
Liu H
Wang L
Yu X
Han B
Publication year
Publication venue
2015 International Conference on Computer Science and Applications (CSA)

External Links

Snippet

The rapid development of financial markets make the financial data variability and unpredictability, the abnormal fluctuations in financial data often contain important information. Financial data is generated over time, so the time series mining method widely …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30943—Information retrieval; Database structures therefor; File system structures therefor details of database functions independent of the retrieved data type
    • G06F17/30946—Information retrieval; Database structures therefor; File system structures therefor details of database functions independent of the retrieved data type indexing structures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F17/30613—Indexing
    • G06F17/30619—Indexing indexing structures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F17/30705—Clustering or classification
    • G06F17/3071—Clustering or classification including class or cluster creation or modification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30386—Retrieval requests
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6279—Classification techniques relating to the number of classes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity

Similar Documents

Publication Publication Date Title
Zhong et al. HELAD: A novel network anomaly detection model based on heterogeneous ensemble learning
Javaid et al. A deep learning approach for network intrusion detection system
Neto et al. Bivariate symbolic regression models for interval-valued variables
Shi et al. Effective decoding in graph auto-encoder using triadic closure
Karthick Semi supervised hierarchy forest clustering and KNN based metric learning technique for machine learning system
Li et al. Weighted outlier detection of high-dimensional categorical data using feature grouping
Xie et al. Accurate recovery of missing network measurement data with localized tensor completion
Zhuang et al. Representation learning via semi-supervised autoencoder for multi-task learning
Aziz et al. Multi-layer hybrid machine learning techniques for anomalies detection and classification approach
He et al. Probabilistic skyline queries on uncertain time series
CN111143838A (en) Database user abnormal behavior detection method
Qiao et al. Adapting stochastic block models to power-law degree distributions
Bhattacharjee et al. Context-aware graph-based analysis for detecting anomalous activities
Zhu et al. Change point detection in dynamic networks based on community identification
Chen et al. An efficient network intrusion detection model based on temporal convolutional networks
Liu et al. Network log anomaly detection based on gru and svdd
Ravipati et al. A survey on different machine learning algorithms and weak classifiers based on KDD and NSL-KDD datasets
Guo et al. The Financial Data of Anomaly Detection Research Based on Time Series
Zhang et al. A high performance intrusion detection system using lightgbm based on oversampling and undersampling
Feng et al. Subnormal distribution derived from evolving networks with variable elements
Luo et al. MLaD 2: A Semi-supervised Money Laundering Detection Framework Based on Decoupling Training
Hao et al. An ensemble detection method for shilling attacks based on features of automatic extraction
Ruijuan et al. User abnormal behavior analysis based on neural network clustering
Ahmed et al. A harmony search algorithm with multi-pitch adjustment rate for symbolic time series data representation
Cheng et al. Community Detection Based on Density Peak Clustering Model and Multiple Attribute Decision‐Making Strategy TOPSIS