Due to the existing volatility , stochastic and dynamic properties of cash flow , this paper employs exponential smoothing method and moving average method to eliminate the effects of the stochastic factors , use the seasonal exponent to eliminate the seasonal volatility of the cash flow , the exponent curve and polynomial fitting curve to estimate the overall cash flow and also provides the calculating methods and identifying principle of the overall cash flow 由于现金流量的波动性、随机性、动态性等特性的存在,依靠单一方法无法科学、准确的预测现金流量。论文提出利用指数平滑和移动平滑的方法来剔除随机因素影响,利用季节指数来消除现金流量的季节波动,之后再利用指数曲线或者多项式拟合曲线来预测整体现金流量的方法,并给出了整体现金流量的计算方法和确定原理。
The paper proves that with ar ( 1 ) process for the end demand , if retailer adopts moving average or exponential smoothing to forecast the non - zero lead time demand , then bullwhip effect in demand forecasting and processing will occur ; if retailer adopts the optimal forecasting method , then bullwhip effect will occur conditionally 证明当存在订货提前期时,零售商采用移动平均法及一次指数平滑法预测会导致在需求预测,信息处理及传递过程中产生牛鞭效应;而采用最优预测仅在需求相关性很强时存在有限值的牛鞭效应。
In this paper , some mathematical methods used to forecast the income of intangible assets are compared , in which we find some mathematical methods ( the forecasting model in time sequence , exponential smoothing estimation method , regressive model ) are not same with the valuing intangible assets , grey model and s - curve model are good to valuing intangible assets . in the base of this , combinatorial model is brought forward in order to make up the limitation of other mathematical me thods 本文将无形资产收益额的预测方法进行比较,发现常用的预测方法(平均数法、指数平滑法、移动平均法和回归预测模型)在预测无形资产收益额是存在很大局限性,而灰色预测模型和成长曲线模型能充分放映无形资产的收益曲线,在进行比较的基础上提出组合预测模型,以弥补各种方法的缺陷。
Exponential smoothing is a technique that can be applied to time series data, either to produce smoothed data for presentation, or to make forecasts. The time series data themselves are a sequence of observations.