Abstract: A learning method to be executed by a computer according to one embodiment includes of acquiring event history information representing a history of a predetermined event; and training, by using the acquired event history information, parameters of an intensity function in which a trigger function is set to be a function represented by a composite function of a first function and a predetermined second function; and a derivative of the first function, the first function being represented by a neural network that models a temporal change in influence of the event.

Abstract: A learning device includes a learning unit that learns parameters for determining an occurrence probability of an event at each time and each location on the basis of history information relating to the event, the history information including a time, a location, and an event type, and features of an area corresponding to the location, so that a likelihood expressing a combined effect of the event type and the features of the area on the event is optimized.

Abstract: An object is to make it possible to accurately detect abnormality of event data. A training unit (105) trains a parameter of a model based on a plurality of event series that are event data in a time series and labels that indicate abnormality or normality with respect to event data of each of the plurality of event series, the model outputting a degree of abnormality of a target event series when the target event series is input, the target event series being an event series of which the degree of abnormality is to be predicted, the parameter being trained to optimize an objective function that represents a relationship between a probability of occurrence of an event at each time point in the time series and a degree of abnormality of each of the plurality of event series.

Abstract: A hazard estimation unit 21 estimates a likelihood of an occurrence of an event according to a hazard function, with respect to each of a plurality of pieces of time-series data that are a series of multiple pieces of data to which an event occurrence time relevant to the data is given in advance and that include time-series data in which the event did not occur and time-series data in which the event occurred. A parameter estimation unit 22 estimates a parameter of the hazard function so as to optimize a likelihood function expressed by including the event occurrence time given with respect to each of the plurality of pieces of time-series data and the likelihood of the occurrence of the event estimated with respect to each of the plurality of pieces of time-series data.

Abstract: Event occurrence is estimated from time series information which is high-dimensional information such as an image. A hazard estimation unit 11 estimates a likelihood of occurrence of an event according to a hazard function for each of a plurality of time-series image groups including time-series image groups in which no events have occurred and time-series image groups in which events have occurred, each of the plurality of time-series image groups being given an occurrence time of an event in advance, and a parameter estimation unit 12 estimates a parameter of the hazard function such that a likelihood function that is represented by including the occurrence time of an event given for each of the plurality of time-series image groups and the likelihood of occurrence of an event estimated for each of the plurality of time-series image groups is optimized.

Abstract: An object is to predict an event with high accuracy by efficiently incorporating external information into a point process model of events. In an event prediction device 10 that predicts an event, an operation unit 3 extracts event history information from an event history storage device 1 and extracts external information from an external information storage device 2, the event history information including a point in time and a place at which an event has occurred, the external information including an external factor that affects the occurrence of an event. A parameter estimation unit 5 estimates an optimum parameter for prediction of an event by supplying the extracted history information and the extracted external information to learning of a relationship between the point process model of events and external factors.

Abstract: A parameter estimation unit (16) estimates a set of parameters so as to optimize a likelihood function of a strength function expressing the event occurrence probability of a type m space-time event at a time t and a geospatial location s when the strength function is modelled with use of the occurrence probability of the type m space-time event at the time t and the geospatial location s, the function expressing the degree of influence of the event occurrence history, the value of the strength function representing the event occurrence probability in an observation section that includes the time t and the geospatial location s, and the relationship between the type m and the type of the event occurrence history included in the observation section, and here, the estimated parameters include the value of the strength function expressing the event occurrence probability in the observation sections, the relationship between types, and the function expressing the degree of influence of the event occurrence histor

Abstract: It is possible to accurately predict data for a prediction target time. The operation unit 10 receives high-dimensional array data representing data at each time and external information data that is a tensor or matrix representing external information at each time. The parameter estimation unit 16 decomposes the high-dimensional array data into a weighted sum of products of a plurality of factor matrices for each rank and decomposes the external information data into a weighted sum of products of a plurality of factor matrices for each rank, under a sparse constraint of weighting parameters for each rank. The prediction unit 22 predicts the data for a prediction target time based on the weighting parameters for each rank and the plurality of factor matrices for each rank, obtained for the high-dimensional array data.