An information processing device which generates a prediction model on time-series data by using neural networks in a short time is provided. A prediction model learning unit 121 learns a prediction model including a first neural network, a second neural network, and a third neural network. To the first neural network and a second neural network, subsets obtained by dividing a set that includes the time-series data values as elements are inputted respectively. To the third neural network, an inner product of outputs from the first neural network and the second neural network is inputted. The third neural network outputs a predicted data value for the prediction target type as of a prediction target time.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An information processing device comprising: a data acquisition unit which acquires time-series data values for at least one of a prediction target type and another type that potentially affects the prediction target type; and a prediction model learning unit which learns a prediction model including a first neural network and a second neural network to which subsets obtained by dividing a set that includes the time-series data values as elements are inputted respectively, and a third neural network to which an inner product of outputs from the first neural network and the second neural network is inputted and which outputs a predicted data value for the prediction target type as of a prediction target time. 2. The information processing device according to claim 1, further comprising: an analysis model learning unit which learns an analysis model including a fourth neural network and a fifth neural network each of which is composed of an input layer and an output layer and to which the subsets of the set are inputted respectively, and a sixth neural network to which an inner product of outputs from the fourth neural network and the fifth neural network is inputted and which outputs a predicted data value for the prediction target type as of the prediction target time; and a weight analysis unit which calculates and outputs weights of respective elements included in the set, based on the fourth neural network and fifth neural network. 3. The information processing device according to claim 2, wherein the weight analysis unit calculates weights of respective elements included in the set, based on a weight that are calculated between each of elements in an input layer and each of elements in an output layer in each of the fourth neural network and the fifth neural network through learning the analysis model. 4. The information processing device according to claim 3, wherein the weight analysis unit calculates weights of respective pairs of each of elements inputted to the fourth neural network and each of elements inputted to the fifth neural network from among elements included in the set, based on a weight that are calculated between each of elements in an input layer and each of elements in an output layer in each of the fourth neural network and the fifth neural network through learning the analysis model, and then calculates weights of respective elements included in the set based on the weights of the pairs. 5. The information processing device according to claim 1, wherein the set includes, as an element, a data value as of a predetermined time relative to the prediction target time. 6. A learning method comprising: acquiring time-series data values for at least one of a prediction target type and another type that potentially affects the prediction target type; and learning a prediction model including a first neural network and a second neural network to which subsets obtained by dividing a set that includes the time-series data values as elements are inputted respectively, and a third neural network to which an inner product of outputs from the first neural network and the second neural network is inputted and which outputs a predicted data value for the prediction target type as of a prediction target time. 7. The learning method according to claim 6, further comprising: learning an analysis model including a fourth neural network and a fifth neural network each of which is composed of an input layer and an output layer and to which the subsets of the set are inputted respectively, and a sixth neural network to which an inner product of outputs from the fourth neural network and the fifth neural network is inputted and which outputs a predicted data value for the prediction target type as of the prediction target time; and calculating and outputting weights of respective elements included in the set, based on the fourth neural network and fifth neural network. 8. The learning method according to claim 7, the calculating weights of respective elements included in the set calculates weights of respective elements included in the set, based on a weight that are calculated between each of elements in an input layer and each of elements in an output layer in each of the fourth neural network and the fifth neural network through learning the analysis model. 9. A non-transitory computer readable storage medium recording thereon a program, causing a computer to perform a method comprising: acquiring time-series data values for at least one of a prediction target type and another type that potentially affects the prediction target type; and learning a prediction model including a first neural network and a second neural network to which subsets obtained by dividing a set that includes the time-series data values as elements are inputted respectively, and a third neural network to which an inner product of outputs from the first neural network and the second neural network is inputted and which outputs a predicted data value for the prediction target type as of a prediction target time. 10. The non-transitory computer readable storage medium, according to claim 9, recording thereon the program, causing a computer to perform the method further comprising: learning an analysis model including a fourth neural network and a fifth neural network each of which is composed of an input layer and an output layer and to which the subsets of the set are inputted respectively, and a sixth neural network to which an inner product of outputs from the fourth neural network and the fifth neural network is inputted and which outputs a predicted data value for the prediction target type as of the prediction target time; and calculating and outputting weights of respective elements included in the set, based on the fourth neural network and fifth neural network. 11. An information processing device comprising: a data acquisition means for acquiring time-series data values for at least one of a prediction target type and another type that potentially affects the prediction target type; and a prediction model learning means for learning a prediction model including a first neural network and a second neural network to which subsets obtained by dividing a set that includes the time-series data values as elements are inputted respectively, and a third neural network to which an inner product of outputs from the first neural network and the second neural network is inputted and which outputs a predicted data value for the prediction target type as of a prediction target time.