ESTIMATION METHOD, ESTIMATION APPARATUS AND PROGRAM
An estimation method according to an embodiment is characterized by a computer executing: inputting time series data into a first neural network model, estimating a label for the time series data, inputting an intermediate output from the first neural network model when estimating the label into a second neural network model, estimating a time series condition of the time series data, and updating a parameter of the first neural network model and a parameter of the second neural network model by using an error between the estimated label and a ground truth label for the time series data and an error between the estimated time series condition and a true time series condition of the time series data.
1 . An estimation method characterized by a computer executing:
inputting time series data into a first neural network model;
estimating a label for the time series data;
inputting an intermediate output from the first neural network model when estimating the label into a second neural network model;
estimating a time series condition of the time series data; and
updating a parameter of the first neural network model and a parameter of the second neural network model by using an error between the estimated label and a ground truth label for the time series data and an error between the estimated time series condition and a true time series condition of the time series data.
2 . The estimation method according to claim 1 , wherein
an output from a convolutional neural network layer or an output from a recurrent neural network layer included in the first neural network model when estimating the label is treated as the intermediate output to be inputted into the second neural network model.
3 . The estimation method according to claim 1 , wherein
the time series condition includes at least one of a frequency of the time series data or a duration before, after, or before and after a time point treated as a reference when collecting the time series data.
4 . The estimation method according to claim 1 , wherein
the second neural network model is a neural network model achieving domain adaptation or domain generalization.
5 . An estimation apparatus comprising:
a processor; and
a memory storing program instructions that cause the processor to:
accept time series data into a first neural network model
estimate a label for the time series data;
accept an intermediate output from the first neural network model when estimating the label into a second neural network model
estimate a time series condition of the time series data; and
update a parameter of the first neural network model and a parameter of the second neural network model by using an error between the estimated label and a ground truth label for the time series data and an error between the estimated time series condition and a true time series condition of the time series data.
6 . A non-transitory computer-readable storage medium that stores therein a program causing a computer to execute the estimation method of claim 1 .