Nippon Telegraph and Telephone Corporation

Hisashi Kurasawa

Hiroshi Nakashima

Shingo Tsukada

Takayuki Ogasawara

Akinori Fujino

Masumi Yamaguchi

Kazuhiro Yoshida

Ippei Shake

Manabu Yoshida

Takahiro Hata

Kana Eguchi

Tsutomu Yabuuchi

Katsuyoshi Hayashi

Akihiro Chiba

Nobuhiko Matsuura

Naoki Asanoma

Miyuki Imada

Shozo Azuma

A monetary risk prediction apparatus according to an embodiment includes: a predictive model recording unit that records a predictive model to predict time-series data showing a future asset amount of a user; and prediction means for receiving evaluation data that is time-series data including an asset amount and a numeric value showing a health condition of the user, inputting the evaluation data to the predictive model recorded on the predictive model recording unit, and outputting the time-series data showing the future asset amount of the user predicted by the predictive model according to the input.

Forecasting device, method, and program for predicting user financial assets and health

A data processing method according to an embodiment acquires data including a plurality of records, divides the data based on external condition identification information such as a user ID to generate data sets Di for respective external conditions, divides each of the data sets Di based on label information indicating whether the record corresponds to a positive label indicating that a predetermined event has occurred or a negative label indicating that the predetermined event has not occurred to generate two data sets Di+ and Di− for the respective label information, generates difference data for a combination of a record included in one data set of the two data sets and a record included in the other data set, combines the generated difference data to generate integrated data Dnew, performs statistical analysis using Dnew, and outputs a result of performing the statistical analysis.

Sales data processing apparatus, method, and medium storing program for sales prediction

A learning-type signal separation method performed using a model formulation unit, which performs learning processing based on a training-use signal including a specific component, and a training-use signal not including the specific component, the training-use signals including a common characteristic. The learning-type signal separation method includes: generating learned data by causing the model formulation unit to perform learning processing based on the training-use signal and information indicating whether or not the specific component is included in the training-use signal, to generate a data series signal in which the specific component has been separated and removed from a data series of the training-use signal; acquiring an arbitrary signal including the common characteristic; and generating, based on the acquired arbitrary signal and the generated learned data, a data series signal in which the specific component has been separated and removed from a data series of the arbitrary signal.

Learning-type signal separation method and learning-type signal separation device

A time-series-data feature extraction device includes: a data processing unit that processes a received unevenly spaced time-series-data group into an evenly spaced time-series-data group including omissions and an omission information group indicating presence or absence of omissions, based on a received input time-series data length and a received minimum observation interval; a model learning unit that learns a weight vector of each layer of a model with a difference between an element not missing in a matrix of the evenly spaced time-series-data group including omissions and an element of an output result of an output layer of the model being taken as an error, and stores the weight vector as a model parameter in a storage unit, the difference being; and a feature extraction unit that receives time-series data of a feature extraction target, calculates a value of the intermediate layer of the model with use of the model parameter stored in the storage unit by inputting the received time-series data of the feature extraction target into the model, and outputs the calculated value of the intermediate layer as a feature that represents temporal changes in data.

Time-series-data feature extraction device, time-series-data feature extraction method and time-series-data feature extraction program

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Tokyo, Japan

Hisashi Kurasawa