Abstract: An information processing device includes a hardware processor. The processor functions to generate first data to be input to a model used for deducing an effect of a case where a measure is executed with input of observation data. The first data represents the observation data obtained without execution of the measure. The processor functions to receive first parameters affecting the effect. Each first parameter is used in an estimation process of estimating the observation data when the measure is executed. The processor functions to execute the estimation process and generate second data representing the observation data. The processor functions to estimate the effect by using the first/second data. The processor functions to learn the model by using the first/second data. The processor functions to evaluate performance of the learned model by comparing the effect estimated by using the first/second data and the effect estimated by the learned model.

Abstract: An information processing apparatus includes processors. The processors construct a predictive model serving to receive respective pieces of first time-series data of input variables in a first period of time and predicts output variables to be obtained at a time point after the first period of time. The processors calculate third time-series data being time-series data of an index representing an error or a goodness of fit between (i) pieces of second time-series data representing the output variables predicted by the predictive model at time points included in the first period of time and (ii) correct time-series data representing correct answers to the output variables at the time points in the first period of time. The processors construct a time-series causal graph representing causality between the input variables and the index by using the pieces of first time-series data and the third time-series data.

Abstract: According to an embodiment, an information processing apparatus includes one or more hardware processors configured to: obtain a first feature including a feature indicating a temporal order and a second feature different from the first feature of input time-series data by using an encoder that extracts the first feature and the second feature from the input time-series data; obtain output time-series data generated based on the first feature and the second feature obtained from the input time-series data by using a decoder that generates the output time-series data based on the first feature and the second feature that are input; and train the encoder and the decoder such that a difference between the input time-series data and the output time-series data becomes small.

Abstract: According to an embodiment, an information processing device includes one or more hardware processors configured to: generate a plurality of exogenous noise estimation values corresponding to a plurality of variables for each of one or more pieces of record data, based on a pre-update model being a structural causal model representing a causal relationship of the plurality of variables; determine whether a causal relationship of the plurality of variables represented by the one or more pieces of record data is different from the causal relationship represented by the pre-update model, based on independence between any two or more variables in the plurality of exogenous noise estimation values with respect to each of the one or more pieces of record data; and generate a post-update model being the structural causal model based on the one or more pieces of record data when determining that the causal relationships are different.

Abstract: According to one embodiment, an information processing apparatus includes one or more processors. The processors determine weights by m determination methods (m is an integer more than or equal to 2). Each determination method is a method using training data sets, each set including an explanatory variable and a visibility value as an objective variable, to determine the weights for the visibility values. The m determination methods includes a first determination method of determining to assign a larger weight to the training data set including a first visibility value than to other visibility values. The first visibility value occurs with lower frequency than the other visibility values. The processors train m forecast models configured to forecast the visibility values by inputting the at least one explanatory variable. The m forecast models are trained by using the training data sets assigned with the weights determined by the m determination methods.

Abstract: An information processing device according to one embodiment includes one or more hardware processors. The hardware processors executes update processing on a designated number of first sections on the basis of a first estimation result and a second estimation result. The first estimation result is obtained by inputting first time-series data to an estimation model. The second estimation result is obtained by inputting second time-series data to the estimation model. The second time-series data is obtained by applying mask processing to partial time-series data of a second section in the first time-series data. The second section is other than the designated number of the first sections.

Abstract: According to one embodiment, an information processing apparatus, comprises a processing circuitry configured to estimate, based on histories of charge and discharge of storage batteries of one or more consumers in a first period, remaining capacities of the storage batteries in a second period different from the first period, and calculate a power adjustable capacity that is a total of rechargeable capacities or dischargeable capacities of the storage batteries of the one or more consumers in the second period, based on estimates of the remaining capacities of the storage batteries.

Abstract: According to one embodiment, an information processing device includes one or more processors. The one or more processors are configured to: detect whether input waveform data is in a first state by using a detection model; acquire a plurality of pieces of second state waveform data in a second state detected in advance by using the detection model when detected to be in the first state; learn a classification model for classifying whether waveform data is in the first state or the second state, by using first state waveform data detected to be in the first state and the plurality of pieces of second state waveform data as learning data, to generate one or more partial waveform patterns serving as a basis for indicating that the first state waveform data is in the first state; and output the generated partial waveform pattern.

Abstract: According to one embodiment, an information processing apparatus includes a processing circuitry which acquires information of a first connection time period during which a mobile object is connected to a charge connector among a first period, the charge connector configured to become capable of control of charge and discharge and measurement of a remaining charge amount of a storage battery included in the mobile object when the mobile object is connected to the charge connector, and acquires information on a remaining charge amount of the storage battery measured during the first connection time period, and which estimates a second connection time period during which the mobile object is connected to the charge connector among the second period and a remaining charge amount of the storage battery during the second connection time period, based on the information of the first connection time period and the information on the remaining charge amount.

Abstract: An information processing device according to an embodiment estimates a probability distribution of a maximum value or a minimum value of time-series target sensor data in a designated estimation period after a base time. The information processing device includes one or more hardware processors that: calculate a reference feature amount representing a feature of related sensor data at a reference time before the base time based on a preset feature amount calculation model; estimate a reference distribution parameter in a reference period based on a preset parameter estimation model; update the feature amount calculation model and the parameter estimation model; calculate an estimation feature amount representing the feature of related sensor data at the base time based on the updated feature amount calculation model; and estimate an estimation distribution parameter based on the updated parameter estimation model.

Abstract: An information processing device includes a hardware processor. The processor functions to generate first data to be input to a model used for deducing an effect of a case where a measure is executed with input of observation data. The first data represents the observation data obtained without execution of the measure. The processor functions to receive first parameters affecting the effect. Each first parameter is used in an estimation process of estimating the observation data when the measure is executed. The processor functions to execute the estimation process and generate second data representing the observation data. The processor functions to estimate the effect by using the first/second data. The processor functions to learn the model by using the first/second data. The processor functions to evaluate performance of the learned model by comparing the effect estimated by using the first/second data and the effect estimated by the learned model.

Abstract: An information processing apparatus according to one embodiment includes one or more hardware processors connected to a memory. The hardware processors functions to store, in the memory, history information including identification information of a model and a history of updating the model. The model receives input data including variables and outputs output data. The variables are each a variable for which a rate of influence on the output data is calculated. The model has been updated by using first input data. The hardware processors functions to select a target model to be updated by using second input data. The target model is selected from among models identified by their respective identification information. The hardware processors functions to update the target model by performing transfer learning in which updated parameters are estimated by using the second input data.

Abstract: According to an embodiment, an information processing apparatus is configured to set a candidate for a time lag until analysis target data including at least one of a measurement item and a setting item for use in control of a process controller affects an objective variable, and a time-lag number allowed in a regression model; select, as a candidate for an explanatory variable, at least one of the measurement item measured at a time corresponding to the candidate for the time lag and the setting item set at the time; and determine a regularization parameter of the regression model such that a number of the time lag is equal to or less than the time-lag number, based on a regularization path indicating transition of a regression coefficient for the candidate for the explanatory variable, the regression coefficient varying in accordance with a value of the regularization parameter.

Abstract: According to an embodiment, an information processing apparatus is configured to set a candidate for a time lag until analysis target data including at least one of a measurement item and a setting item for use in control of a process controller affects an objective variable, and a time-lag number allowed in a regression model; select, as a candidate for an explanatory variable, at least one of the measurement item measured at a time corresponding to the candidate for the time lag and the setting item set at the time; and determine a regularization parameter of the regression model such that a number of the time lag is equal to or less than the time-lag number, based on a regularization path indicating transition of a regression coefficient for the candidate for the explanatory variable, the regression coefficient varying in accordance with a value of the regularization parameter.

Abstract: An information processing device according to an embodiment estimates a probability distribution of a maximum value or a minimum value of time-series target sensor data in a designated estimation period after a base time. The information processing device includes one or more hardware processors that: calculate a reference feature amount representing a feature of related sensor data at a reference time before the base time based on a preset feature amount calculation model; estimate a reference distribution parameter in a reference period based on a preset parameter estimation model; update the feature amount calculation model and the parameter estimation model; calculate an estimation feature amount representing the feature of related sensor data at the base time based on the updated feature amount calculation model; and estimate an estimation distribution parameter based on the updated parameter estimation model.

Abstract: According to one embodiment, an information providing device includes; an evaluation value calculator, a profile setter and an information transmission processing unit. The evaluation value calculator calculate an evaluation value on each of a plurality of time periods based on behavior history data which indicates a history of days and times of occurrence of a specific behavior taken by a user, the evaluation value indicating likelihood of the specific behavior being taken by the user. The profile setter sets a profile on the user based on the evaluation values of the time periods, the profile indicating a tendency of time periods at which the specific behavior is taken by the user. The information transmission processing unit determines offer information for providing to the user according to the profile of the user and transmits the offer information to an output device related to the user.

Abstract: An electric power demand prediction system has an extractor to select electric power consumption value data included in a certain period out of a consumer's past electric power consumption value data and to extract an outdoor temperature-electric power relation that is a relation between an outdoor temperature and outdoor-temperature-depend electric power varying in accordance with the outdoor temperature out of electric power consumption, a model generator to generate an electric power consumption prediction model which predicts electric power consumption by the consumer in accordance with the outdoor temperature based on the outdoor temperature-electric power relation extracted by the extractor, and a predictor to predict electric power consumption by the consumer at a time subject to prediction based on the electric power consumption prediction model generated by the model generator and the outdoor temperature at the time subject to prediction.

Abstract: According to one embodiment, a measurement unit periodically measures a usage status of a measurement target, and measures each test item of the measurement target at an arbitrary timing according to a test schedule. A first storage unit stores the usage status and a test result in time series. A second storage unit stores statistic information representing a relationship between the usage status and the test result. A probability calculation unit calculates a first evaluation value representing a load of the measurement target at a recent timing and a second evaluation value representing a load of the measurement target at the arbitrary timing, based on the usage status, and calculates a specific probability of each test item, based on the statistic information, the first evaluation value and the second evaluation value. A test schedule creation unit creates a new test schedule having selected test items, based on the specific probability.

Abstract: According to one embodiment, a behavior estimation apparatus includes a storage unit, a first calculation unit, a second calculation unit, and an estimation unit. The storage unit stores first data collecting power values consumed by a consumer in a period. The first calculation unit calculates second data representing a frequency of each power value by using the first data. The second calculation unit calculates a first threshold to divide a first power value from a second power value which is larger than and next to the first power value, among power values corresponding to maximal values of frequencies included in the second data. The estimation unit obtains a power value consumed by the consumer in an estimating period, and estimates the consumer's behavior status in the estimating period by using the power value and the first threshold.

Abstract: According to one embodiment, a behavior estimation apparatus includes a storage unit, a first calculation unit, a second calculation unit, and an estimation unit. The storage unit stores first data collecting power values consumed by a consumer in a period. The first calculation unit calculates second data representing a frequency of each power value by using the first data. The second calculation unit calculates a first threshold to divide a first power value from a second power value which is larger than and next to the first power value, among power values corresponding to maximal values of frequencies included in the second data. The estimation unit obtains a power value consumed by the consumer in an estimating period, and estimates the consumer's behavior status in the estimating period by using the power value and the first threshold.