Forecasting Technological Substitutions with Concurrent Short Time Series
针对短时间序列数据，提出一种结合幂变换和重复测量的广义增长曲线模型，利用多个并发序列的序列相关性提高技术替代预测精度，并用电话交换数据验证。
Abstract It has been shown in the literature that the data of technological substitutions exhibit a strong correlation across different time periods, that is, a strong serial correlation. Significant improvement in predicting such substitutions has been achieved by incorporating serial correlation and power transformation parameters into four growth curve models. The modified models, or data-based transformed models, however, break down when the number of time points is small. This article proposes a generalized growth curve model for forecasting technological substitutions with concurrent short time series. The model combines the concepts of power transformations and repeated measurements with a common serial covariance structure. Concurrent time series for several cases provide the repeated measurement requirement of the model. Improvement in forecasting accuracy by using this model is demonstrated with a set of telephone switching data.