洛杉矶代写Assignment 酿酒时间模型
ASSIGNMENT代写

洛杉矶代写Assignment 酿酒时间模型

2016-12-12 06:50

洛杉矶代写Assignment,酿酒时间模型

基于时间序列数据表现出显着的季节性因素,我们采用的冬天指数平滑模型,以适应原始数据。过去时期和残差的预测值的计算和存储和模型的乘法型的季节性表现优于加性型。法官从MAPE(平均绝对百分误差),该模型适合葡萄酒和白兰地12.42 6。其他重要的结果显示在表1。

然而,通过仔细检查残差,我们会发现它不是那么好,如表2所示。这些残差的自相关系数如表2和图2所示。显著的自相关系数表明残余和LBQ统计一些协会或模式也拒绝假设,残差是从一个随机序列提取。因此,我们可以判断该模型是不够的,在拟合的数据。

洛杉矶代写Assignment,酿酒时间模型

Based on the fact that the time series data exhibit significant seasonal components, we employ the Winters Exponential Smoothing model to fit the original data. The predicted value of past periods and the residuals are compute and stored and model with the Multiplicative type of Seasonality performs well than with Additive type. Judge from the MAPE (Mean Absolute Percent Error), the model fits well with wine 6 and brandy 12.42. Other important results are showed in the Table1. Table1 Wine Brandy MAPE MAD MSE MAPE MAD MSE 6 4911 46911807 12.42 32.2 2110.2 However, by examining the residuals carefully, we will find it not so well as shown in the Table 2. The autocorrelation coefficients for these residuals are shown in Table 2 and Figure 2. Significant autocorrelation coefficients indicate some association or pattern in the residual and the LBQ statistic also rejects the hypothesis that the residuals are drawn from a random series. So, we can judge the model to be inadequate in fitting the data.