To capture and forecast the volatility of customer needs, this paper proposes a forecast method within the framework of QFD (Quality Function Deployment), based on CTS (compositional time series) and VAR model (vector auto-regression model). The CTS formed by customer needs importance rating sampling within a period of time are treated as the basis to predict the future customer needs. Firstly, the CTS are transformed from the simplex space to the real domain. Then, the VAR model is established based on the time series obtained in the real domain. This model is used to accurately forecast beyond the sample and the predictive result is transformed back to the simplex space to obtain the predictive customer needs importance rating time series. Based on the predictive customer needs importance rating, the design attributes predictive priorities are calculated, which can guide the resources allocation in the development of personalized product, to provide better personalized product that is more in line with future customer needs. The case shows that the proposed method is effective.

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