Mood forecasting method, mood forecasting apparatus and program
The present invention can forecast a future-mood and to present the forecasting and a behavior that is the basis of the forecasting by causing a computer to execute: a first-training procedure for training a first-neural network in accordance with behavior time-series data and mood time-series data in each time interval, the first-neural network using behavior time-series data and mood time-series data in a first-time interval as input to output a forecasted-value of behavior time-series data in a time interval following the first-time interval; and a second-training procedure for training a second-neural network in accordance with behavior time-series and mood time-series data per time interval, the second-neural network using the behavior time-series data in the following time interval and the mood time-series data in the first-time interval as input to output a forecasted-value of mood time-series data in the following time interval.
1 . A mood forecasting method executed by a computer,
the method comprising:
training a first neural network in accordance with behavior time-series data and mood time-series data per time interval where a certain period is divided into a plurality of periods, the first neural network using behavior time-series data and mood time-series data in a first time interval as input to output a forecasted value of behavior time-series data in a time interval following the first time interval; and
training a second neural network in accordance with behavior time-series data and mood time-series data per time interval where the certain period is divided into a plurality of periods, the second neural network using the behavior time-series data in the following time interval and the mood time-series data in the first time interval as input to output a forecasted value of mood time-series data in the following time interval.
2 . The mood forecasting method according to claim 1 executed by a computer, the method further comprising:
inputting behavior time-series data and mood time-series data in a second time interval to the trained first neural network, and calculating a forecasted value of behavior time-series data in a time interval following the second time interval; and
inputting, to the trained second neural network, the forecasted value of the behavior time-series data in the time interval following the second time interval that has been calculated in the first forecasting procedure and the mood time-series data in the second time interval, and calculating a forecasted value of mood time-series data in the time interval following the second time interval.
3 . A mood forecasting apparatus comprising:
a memory and a processor, wherein the processor is configured to:
train a first neural network in accordance with behavior time-series data and mood time-series data per time interval where a certain period is divided into a plurality of periods, the first neural network using behavior time-series data and mood time-series data in a first time interval as input to output a forecasted value of behavior time-series data in a time interval following the first time interval; and
train a second neural network in accordance with behavior time-series and mood time-series data per time interval where the certain period is divided into a plurality of periods, the second neural network using the behavior time-series data in the following time interval and the mood time-series data in the first time interval as input to output a forecasted value of mood time-series data in the following time interval.
4 . The mood forecasting apparatus according to claim 3 , wherein the processor is further configured to:
input behavior time-series data and mood time-series data in a second time interval to the trained first neural network to calculate a forecasted value of behavior time-series data in a time interval following the second time interval; and
input, to the trained second neural network, the forecasted value of the behavior time-series data in the time interval following the second time interval that has been calculated by the first forecasting unit and the mood time-series data in the second time interval to calculate a forecasted value of mood time-series data in the time interval following the second time interval.
5 . A non-transitory computer-readable recording medium having computer readable instructions stored thereon to operate as the mood forecasting apparatus according to claim 3 .