Abstract: A method of predicting a product lifetime total sales volume through a hybrid model based on machine learning includes: constructing a first model configured to predict a product lifespan on the basis of modeling data in a machine learning model by means of a first model construction unit; constructing a second model configured to predict a product total sales volume on the basis of product lifespan information predicted by the first model and the modeling data in the machine learning model by means of a second model construction unit; predicting a lifetime lifespan of test data and creating lifetime lifespan prediction data by inputting the test data into the first model by means of a lifespan prediction unit; and predicting a lifetime total sales volume by inputting the lifetime lifespan prediction data and the test data into the second model by means of a total sales volume prediction unit.

Abstract: The present disclosure relates to a predictive new product development method. The predictive new product development method includes: calculating a new product differentiation index (PDI) by calculating a consumer satisfaction coefficient for each feature of a new product and a degree of differentiation of each feature through a differentiation index calculation unit; calculating a demand creation index (DCI) through a relative ratio of sales volume generated by the new product compared to a predecessor product by a demand creation index calculation unit; building a demand forecasting model through Gaussian process regression of the calculated new product differentiation index (PDI) and the calculated demand creation index (DCI) by a demand forecasting model building unit; and deriving an optimal profile for the new product using the demand forecasting model through an optimal profile deriving unit.

Abstract: The present disclosure retates to a method and device for prediction with a machine learning-based hybrid model. The prediction method includes: creating a first model for predicting a demand pattern for a combination of product features through K-means and ANN based on historical data; creating a second model for predicting a total demand for a period to be predicted using QRNN; predicting the demand pattern in the first model and the total demand in the second model by using features of the new product as input variables in the first model and the second model; creating a third model for calculating a specific demand for each time slot by reflecting the total demand predicted through the second model in the demand pattern calculated through the first model; and predicting a result using the third model.