Forecasting Data Published at Irregular Time Intervals Using an Extension of Holt's Method
扩展了单指数平滑和霍尔特方法，使其适用于不规则时间间隔的数据，并通过六个公开序列验证了其计算高效性和预测性能。
In practice many data series contain observations at irregular times whereas most forecasting methods are restricted to the case of equal time intervals between data points. This paper provides extensions of Single Exponential Smoothing and Holt's Method to the case of irregularly spaced data and shows them to be highly efficient computationally. The new methods are applied to six published series, and their performance is analyzed via four error measures with respect to changes in the smoothing parameters.