Nec Corporation

Takehiko Mizoguchi

Haifeng Chen

Cristian Lumezanu

Dongjin Song

Yuncong Chen

Wei Cheng

Zhengzhang Chen

Masanao Natsumeda

Xiao Yu

Xuchao Zhang

Hao Yuan

Xiaoyuan Liang

An information processing deviceof the present invention includes an analysis unitand an encoding unit. The analysis unitextracts a partial time-series data set obtained by dividing a time-series data set that is a set of time-series data including a plurality of elements at given time intervals, and calculates correlation data representing a correlation between elements of time-series data included in the partial time-series data set. The encoding unitgenerates coded data based on the time-series data of the partial time-series data set and the correlation data.

Time-series data processing method

A method for system metric prediction and influential events identification by concurrently employing metric logs and event logs is presented. The method includes concurrently modeling multivariate metric series and individual events in event series by a multi-stream recurrent neural network (RNN) to improve prediction of future metrics, where the multi-stream RNN includes a series of RNNs, one RNN for each metric and one RNN for each event sequence and modeling causality relations between the multivariate metric series and the individual events in the event series by employing an attention mechanism to identify target events most responsible for fluctuations of one or more target metrics.

Causal attention-based multi-stream RNN for computer system metric prediction and influential events identification based on metric and event logs

Methods and systems of training and using a neural network model include training a time series embedding model and a text embedding model with unsupervised clustering to translate time series and text, respectively, to a shared latent space. The time series embedding model and the text embedding model are further trained using semi-supervised clustering that samples training data pairs of time series information and associated text for annotation.

Embedding multi-modal time series and text data

A first anomaly detection unit detects anomalous first monitored data from among a plurality of first monitored data obtained from a monitored system. A second anomaly detection unit operates in parallel with the first anomaly detection unit and detects anomalous second monitored data from among a plurality of second monitored data obtained from the monitored system. In a first storage unit, the anomalous first monitored data and the anomalous second monitored data detected before lapse of a given time from detection time of the anomalous first monitored data are stored in association with each other. A first determination unit, when the anomalous first monitored data is detected, retrieves the anomalous second monitored data associated with the detected anomalous first monitored data from the first storage unit and outputs a first anomaly detection result including the retrieved anomalous second monitored data and the detected anomalous first monitored data.

Abnormality detection device

Systems and methods for updating a classification model of a neural network. The methods include selecting, as a set of landmarks, a limited number of data from a set of historical data used to train a classification model. Additionally, the methods generate new training data from recently collected data. Further, the methods update the classification model with the new training data and the set of landmarks to obtain an updated classification model having a loss function configured to capture similarities in the new training data and remember similarities in the historical data represented by the set of landmarks within a predefined tolerance.

Landmark-based classification model updating

A factor, other than an external factor, having an influence on a state change of a system can be correctly identified even when an external factor having a strong correlation with the state change of the system exists. In an analysis system, an external factor identification unitidentifies a first explanatory time series among a plurality of explanatory time series. A differential time series generation unitgenerates a difference time series between a value of an objective time series and a prediction value of the objective time series calculated based on a value of the first explanatory time series. An effect degree calculation unitcalculates, based on second explanatory time series among the plurality of explanatory time series and the difference time series, an influence degree of each of the second explanatory time series on a value change of the difference time series.

Analysis system, analysis method, and recording medium

Provided is a factor analysis device capable of obtaining more useful knowledge relating to the degree of influence of pieces of data. A factor analysis device according to one embodiment of the present invention is provided with: a classification unit for classifying a type of data into a first group or a second group; and an influence degree calculation unit for calculating, as the degree of influence on target data, the degree of influence of the data of the type classified into the second group on the data of the first group type.

Factor analysis device, factor analysis method, and storage medium on which program is stored

Methods and systems for detecting and correcting anomalies includes generating historical binary codes from historical time series segments. The historical time series segments are each made up of measurements from respective sensors. A latest binary code is generated from a latest time series segment. It is determined that the latest time series segment represents anomalous behavior, based on a comparison of the latest binary code to the historical binary codes. The sensors are ranked, based on a comparison of time series data of the sensors in the latest time series segment to respective time series data of the historical time series, to generate a sensor ranking. A corrective action is performed responsive to the detected anomaly, prioritized according to the sensor ranking.

Sensor attribution for anomaly detection

A system for cross-modal data retrieval is provided that includes a neural network having a time series encoder and text encoder which are jointly trained using an unsupervised training method which is based on a loss function. The loss function jointly evaluates a similarity of feature vectors of training sets of two different modalities of time series and free-form text comments and a compatibility of the time series and the free-form text comments with a word-overlap-based spectral clustering method configured to compute pseudo labels for the unsupervised training method. The computer processing system further includes a database for storing the training sets with feature vectors extracted from encodings of the training sets. The encodings are obtained by encoding a training set of the time series using the time series encoder and encoding a training set of the free-form text comments using the text encoder.

Word-overlap-based clustering cross-modal retrieval

Change points of a system represented by a plurality of time series are detected more appropriately. An information processing system includes means for learning, with respect to each of a plurality of time series, models that approximate partial time series respectively and are defined by parameters of the partial time series respectively, the partial time series being obtained by dividing a corresponding time series into a plurality of segments at change point candidates; and means for detecting, with respect to each of the change point candidates for the plurality of time series, a global change point that is a change point for the plurality of time series based on a difference between a parameter of a first partial time series starting from a time point of a corresponding change point candidate and a parameter of a second partial time series before the corresponding change point candidate, and outputting the global change point.

Information processing system, change point detection method, and recording medium

A system monitoring apparatus has: a determination unit for determining whether the relatedness indicating a relationship that is true for first time-series data of multiple sets measured during a first time period is true for second time-series data of the multiple sets measured during a second time period; an irregularity degree calculation unit for calculating the degree of irregularity indicating the extent to which the second time-series data is irregular; a first extent calculation unit for calculating a first extent indicating the extent to which the irregularity is at a specified value when the second time-series data are normal or irregular; a second extent calculation unit for calculating a second extent indicating the extent to which the second time-series data of the multiple sets are related; and a state calculation unit for finding whether the second time-series data are normal or irregular, based on the first and second extents.

Abnormality detection apparatus, abnormality detection method, and non-transitory computer-readable medium

This factor analysis device is provided with a feature extraction unit () that extracts feature quantities from an explanatory time series, a feature conversion unit () that converts said feature quantities to a feature time series, a feature-time-series influence-degree computation unit () that uses said feature time series and a response time series to compute an influence degree indicating the degree to which the feature time series influences the change over time represented by the response time series, and an explanatory-time-series influence-degree computation unit () that uses said influence degree to compute an influence degree indicating the degree to which the explanatory time series influences the change over time represented by the response time series.

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Tokyo, Japan

Takehiko Mizoguchi