对于在线时间序列搜索问题，在假设对未来信息有一定的预期下，提出了在线时间序列搜索的风险补偿模型，进一步研究了模型的求解，给出了模型的一个最优策略，并通过数值计算讨论了最优策略的补偿函数随参数变化规律.数值实验结果表明，随着风险容忍度的增大与预期区间下限的增大，补偿函数均增大且趋于收敛；随着预期概率的增大与预期区间上限的减少，补偿函数分别增大.研究结果丰富了在线时间序列搜索的理论且具有实际应用价值.
The risk-reward model of the time series search problem is promoted under the assumption that the future can be partially forecasted, where an optimal strategy is presented. Moreover, the variations of the reward function with the parameters are studied by numerical computation, which show that the reward first increases and then is convergent as the risk tolerance and the lower limit of the expectation interval rise, respectively, and increase as the expectation probability rises and the upper limit of the expectation interval declines, respectively. The results enrich the theory of online time series search and are valuable in application.
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