【摘要】 带输入项的线性自回归模型是一种综合性预测模型，较之常用的树木物候预测模型更为优越。模型结构属动态随机差分模型范畴；集中了线性自回归和多元线性回归模型两者的优点；模型有时滞，使预测值不但和现时刻输入（长期天气预报结果）有关，还受历史输入及自身滞后量的影响，即削弱了长期天气预报结果对物候预测的影响，提高了精度；模型参数的修正采用递推最小二乘估计法，参数随预测期数的增加而不断修正，使预测值更靠近真值（观测值）。从日本樱花、绯红晚樱、刺槐３树种预测误差对比可明显看出，新法预测误差总是稳定在１～２ｄ内，而不致于如其他方法预测误差那样在１～１１ｄ不定。说明新预测方法更加符合树木物候动态随机变化之实际。更多还原

【Abstract】 The linear autoregression model with input variables is a comprehensive forecasting model which is superior to the conventional model for phenological forecast.It belongs to the dynamic random difference equation in structure and combines the merits of both the linear autoregression model and linear multivariant regression model.As the new model has its lag,the prediction value is not only related to current input(long term weather forecast results) but also is affected by historical input and hysteresis.This means that the long term weather forecast results do not exert much influence on the prediction value,hence increase the prediction precision.The revision of model paramaters is made by means of recursion least squares and the parematers are constantly revised along with the increase in the number of predictions,approximating the prediction value to the actual value.Results from error contrast on Prunus yedoensis,P.serrulata and Robinia pseudoacacia  show that the error is kept within one or two days if the new forecast method is used,while the error could vary from one to eleven days in other methods applied.This means that the new method could better comform to dynamic variation in forecasting tree phenology.更多还原