Patent ID: 11449732

Abstract:
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.