时间序列分解结果.doc

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1、在随机时间序列分析中，为简便起见，我们假定时间序列主要由趋势项（T）、季节项（S）和随机项（R）构成。#读入数据，画曲线图>sales<-read.csv(file="sales.csv",header=TRUE)>head(sales)>plot(sales$t,sales$Y,type="l")观察这幅图形，可以看出有明显的长期趋势和季节变动。利用分解法，假设这48个数据可表示为：，Yt代表实际销售额度。长期趋势的分解用时间回归法，在同一图中画出趋势项目、季节项和随机项的数据图，如下：decomp

2、ose()函数主要用来做季节指数分解，figure项即指季节指数。同时也返回原始数据，以及MA算法的结果；trend趋势项使用光滑移动平均法求得，它包含了长期趋势T和周期变动因素C，之前用回归法求得长期趋势T，利用此函数的返回值Trend即可求得周期变动因素C；Random即为不规则变动。此函数的基本结构：Additive: xt =Trend+Seasonal+RandomMultiplicative: xt =Trend*Seasonal*Random>sales1<-ts(sales[,2],s

3、tart=1,frequency=4)#季节变动趋势分解>m<-decompose(sales1,type="multiplicative")>plot(m)>m$xQtr1Qtr2Qtr3Qtr420033017.603043.542094.352809.8420043274.803163.282114.313024.5720053327.483493.482439.933490.7920063685.083661.232378.433459.5520073849.633701.182642.383

4、585.5220084078.663907.062828.464089.5020094339.614148.602916.454084.6420104242.423997.582881.014036.2320114360.334360.533172.184223.7620124690.484694.483342.354577.6320134965.465026.053470.144525.9420145258.715189.583596.763881.60$seasonalQtr1Qtr2Qtr3Qt

5、r420031.12139671.09385490.75359471.031153720041.12139671.09385490.75359471.031153720051.12139671.09385490.75359471.031153720061.12139671.09385490.75359471.031153720071.12139671.09385490.75359471.031153720081.12139671.09385490.75359471.031153720091.12139

6、671.09385490.75359471.031153720101.12139671.09385490.75359471.031153720111.12139671.09385490.75359471.031153720121.12139671.09385490.75359471.031153720131.12139671.09385490.75359471.031153720141.12139671.09385490.75359471.0311537$trend（居中平均TC）Qtr1Qtr2Qt

7、r3Qtr42003NANA2773.4832820.60020042838.0622867.3992900.8252948.68520053030.6623129.6423232.6203298.28920063311.5703299.9773316.6413342.20420073380.1913428.9313473.3063527.67020083576.6653662.9233758.5393821.35020093862.5413872.9323860.1763829.1502010380

8、5.8433795.3613804.0493864.15620113945.9214005.7594070.4694153.48120124216.4964282.0014360.6084436.42620134493.8464503.3594533.5544590.65120144626.9204562.205NANA$random（不规则变动）Qtr1Qtr2Qtr3Qtr42003NANA1.00204220.966088020041.028972