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    • 1. 发明专利
      AU2019201857B2 Sparse neural network based anomaly detection in multi-dimensional time series 未知
    • Sparse neural network based anomaly detection in multi-dimensional time series
    • AU2019201857B2
    • 2020-10-15
    • AU2019201857
    • 2019-03-18
    • TATA CONSULTANCY SERVICES LTD
    • MALHOTRA PANKAJ , GUGULOTHU NARENDHAR , VIG LOVEKESH , SHROFF GAUTAM
    • G06F11/07
    • SPARSE NEURAL NETWORK BASED ANOMALY DETECTION IN MULTI DIMENSIONAL TIME SERIES Anomaly detection from time series is one of the key components in automated monitoring of one or more entities. Domain-driven sensor selection for anomaly detection is restricted by knowledge of important sensors to capture only a certain set of anomalies from the entire set of possible anomalies. Hence, existing anomaly detection approaches are not very effective for multi-dimensional time series. Embodiments of the present disclosure depict sparse neural network for anomaly detection in multi-dimensional time series (MDTS) corresponding to a plurality of parameters of entities. A reduced-dimensional time series is obtained from the MDTS via an at least one feedforward layer by using a dimensionality reduction model. The dimensionality reduction model and recurrent neural network (RNN) encoder-decoder model are simultaneously learned to obtain a multi-layered sparse neural network. A plurality of error vectors corresponding to at least one time instance of the MDTS is computed to obtain. An anomaly score. [To be published with FIG. 2] receiving, at an input layer, a multi-dimensional time series corresponding to a plurality of parameters of an entity 202 obtaining, using a dimensionality reduction model, a reduced-dimensional time series from the multi-dimensional time series via an at least one feedforward layer, wherein 204 connections between the input layer and the feedforward layer are sparse to access at least a portion of the plurality of parameters estimating, by using a recurrent neural network (RNN) encoder-decoder model, the multi-dimensional time 206 series using the reduced-dimensional time series obtained by the dimensionality reduction model simultaneously learning, by using the estimated multi-dimensional time series, the dimensionality reduction model and the RNN encoder-decoder model to obtain a multi-layered sparse neural network computing, by using the multi-layered sparse neural network, a plurality of error vectors corresponding to at least onetime instance of the multi-dimensional time series by 210 performing a comparison of the multi-dimensional time series and the estimated multi-dimensional time series generating at least one anomaly score based on the plurality of the error vectors 212
    • 基本信息 添加关注 加入工作空间 PDF全文 PDF下载 全文下载 相似专利 双屏查看 权利要求书 说明书全文 Espacenet 法律信息 同族专利 专利引用
    • 4. 发明专利
      AU2019201857A1 Sparse neural network based anomaly detection in multi-dimensional time series 未知
    • Sparse neural network based anomaly detection in multi-dimensional time series
    • AU2019201857A1
    • 2020-01-23
    • AU2019201857
    • 2019-03-18
    • TATA CONSULTANCY SERVICES LTD
    • MALHOTRA PANKAJ , GUGULOTHU NARENDHAR , VIG LOVEKESH , SHROFF GAUTAM
    • G06F11/07
    • SPARSE NEURAL NETWORK BASED ANOMALY DETECTION IN MULTI DIMENSIONAL TIME SERIES Anomaly detection from time series is one of the key components in automated monitoring of one or more entities. Domain-driven sensor selection for anomaly detection is restricted by knowledge of important sensors to capture only a certain set of anomalies from the entire set of possible anomalies. Hence, existing anomaly detection approaches are not very effective for multi-dimensional time series. Embodiments of the present disclosure depict sparse neural network for anomaly detection in multi-dimensional time series (MDTS) corresponding to a plurality of parameters of entities. A reduced-dimensional time series is obtained from the MDTS via an at least one feedforward layer by using a dimensionality reduction model. The dimensionality reduction model and recurrent neural network (RNN) encoder-decoder model are simultaneously learned to obtain a multi-layered sparse neural network. A plurality of error vectors corresponding to at least one time instance of the MDTS is computed to obtain. An anomaly score. [To be published with FIG. 2] receiving, at an input layer, a multi-dimensional time series corresponding to a plurality of parameters of an entity 202 obtaining, using a dimensionality reduction model, a reduced-dimensional time series from the multi-dimensional time series via an at least one feedforward layer, wherein 204 connections between the input layer and the feedforward layer are sparse to access at least a portion of the plurality of parameters estimating, by using a recurrent neural network (RNN) encoder-decoder model, the multi-dimensional time 206 series using the reduced-dimensional time series obtained by the dimensionality reduction model simultaneously learning, by using the estimated multi-dimensional time series, the dimensionality reduction model and the RNN encoder-decoder model to obtain a multi-layered sparse neural network computing, by using the multi-layered sparse neural network, a plurality of error vectors corresponding to at least onetime instance of the multi-dimensional time series by 210 performing a comparison of the multi-dimensional time series and the estimated multi-dimensional time series generating at least one anomaly score based on the plurality of the error vectors 212
    • 基本信息 添加关注 加入工作空间 PDF全文 PDF下载 全文下载 相似专利 双屏查看 权利要求书 说明书全文 Espacenet 法律信息 同族专利 专利引用
    • 5. 发明专利
      AU2016201088A1 ANOMALY DETECTION SYSTEM AND METHOD 未知
    • ANOMALY DETECTION SYSTEM AND METHOD
    • AU2016201088A1
    • 2016-10-27
    • AU2016201088
    • 2016-02-22
    • TATA CONSULTANCY SERVICES LTD
    • MALHOTRA PANKAJ , VIG LOVEKESH , SHROFF GAUTAM , AGARWAL PUNEET
    • G06F11/00
    • ANOMALY DETECTION SYSTEM AND METHOD An anomaly detection system and method is provided. The system comprising. a hardware processor; and a memory storing instructions to configure the hardware processor, wherein the hardware processor receives a first time-series data comprising a first set of points and a second time-series data comprising a second set of points, computes a first set of error vectors for each point of the first set, and a second set of error vectors for each point of the second set, each set of error vectors comprising one or more prediction errors; estimates parameters based on the first set of error vectors comprising; applies (or uses) the parameters on the second set of error vectors; and detects an anomaly in the second time-series data when the parameters are applied on the second set of error vectors. [To be published with Figure 1] RECE$NNGA RRStT ME-SERRES DATA CG FIRST SET OF POINTS C U A VECTOR OR EACH PONT FROM 5" T ETOF PO'' NI FF.S7 TME-E a f 4 DATA TO OSTA!N A F RST SET OF EPOR VECTORS ES TMATAT ON00E OR M0OR E PA RAMtET E RS RA SED O :N TMr . A 1 UPSET OF ERR VECT01RS TO 0%TA-N A S E T IF ESThATED PARAYM ETERS RECENAGA SECOND TMERES DAA COPR S 1 NG A SECOND SET 0F POI' T S COMPLING 3AN ERROR VECTOR FOR. EACH PO' NT., FRO M THE SECONDDW SET OF PO, NTS1 1N THE SECOND T NE SERUES DATA TO 0O6T A,,N A S SECOND SET OF ERROR $10 APPLY1NG3 TE SET OF ESTIMATED PARAMETERS ON T.4E SECOND 3-T ERR0R VECTORS DETECTIN ANANAMY IN THE SECOND Th*ME-xERISx DATA WHEN EME7 a eEIATED PARAMETER$ ARE$ APPUtED ON THE SECM-OD SET OF ERRDOR 'VECTORS,
    • 基本信息 添加关注 加入工作空间 PDF全文 PDF下载 全文下载 相似专利 双屏查看 权利要求书 说明书全文 Espacenet 法律信息 同族专利 专利引用
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