英国论文代写推荐-销售水平的附加季节性
英国论文代写推荐-销售水平的附加季节性。当销售水平随季节下降，季节幅度变小时，称为乘法季节性。采用最小二乘误差法对销售趋势所表现出的季节性进行了判别。数据显示季节变化较小，因此可以预测为附加季节性。时间序列中的分解过程用来描述时间序列中的趋势和季节因素。更广泛的分解还可能包括长期周期、假期效应和工作日效应等等。这里，我们只考虑趋势和季节分解。
Decomposition procedures are used in time series to describe the trend and seasonal factors in a time series. More extensive decompositions might also include long-run cycles, holiday effects and day of week effects and so on. Here, we’ll only consider trend and seasonal decompositions.
Structure used for decomposition procedure is as follows –
- Additive: xt = Trend + Seasonal + Random
- Multiplicative: xt = Trend * Seasonal * Random
Additive model is being used for less seasonal variation overtime while multiplicative model is used for increases in seasonal variation. - Additive decomposition
Yt = Tt + C t+St+It
Difference of actual to CMA = Yt – Tt- C t
= St+It
Seasonal index = average of S+I for each quarter
Index for quarter I = 0
= unadjusted seasonal index
Index – mean of all unadjusted index
Realized value = (T+C+I) = Yt – St = Tt + C t+It
Deterred value = (C+I) : Yt – St – Tt = C t+It
Cyclic indices = 3 period MA + CI - Multiplicative model
Y = TCSI RATIO OF ANNUAL CMA = y /(TC)
Unadjusted seasonal index = Average s + I for each quarter
Seasonal index of quarter I = length of L
Adjusted SI = Unadjusted seasonal index * L/ seasonal Index
Deseasonalised value = (T+C+I) = Y/S= TCI
Trend estimate = T = b+ bt
Deterend value =(C+I) = Y/ (ST)= CI
CYCLIC Indices = 3- period MA of c+I
For cast = TCS
(iii) Exponential smoothing forecast for random variable as the data in consideration has trend and it will not be an acceptable forecast method.
It is similar to weighted moving average more suitable. Value of alpha has been taken from 0.3 to 1 . For alpha = 0 the demand does not changes thee for it is completely insensitive.
For alpha =1 , the forecast has demonstrated a knife forecasting where previous period actual become next period forecast which make alpha highly sensitive.
(iv) Holt method
FT+M = ( L T + MTT)*ST+M-S
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