R中的预测值相同 [英] Forecasting Values are coming same in R

问题描述

我有一个样本数据

Sno     period       year_quarter   country            city        sales_revenue
1       1/1/2009        2009-Q1     Argentina       Buenos Aires        3008
2       1/4/2009        2009-Q2     Argentina       Buenos Aires        3244
3       1/7/2009        2009-Q3     Argentina       Buenos Aires        8000
4       1/10/2009       2009-Q4     Argentina       Buenos Aires        8719
5       1/1/2010        2010-Q1     Argentina       Buenos Aires        3008
6       1/4/2010        2010-Q2     Argentina       Buenos Aires        3244
7       1/7/2010        2010-Q3     Argentina       Buenos Aires        78
8       1/10/2010       2010-Q4     Argentina       Buenos Aires        7379
9       1/1/2011        2011-Q1     Argentina       Buenos Aires        3735
10      1/4/2011        2011-Q2     Argentina       Buenos Aires        7339
11      1/7/2011        2011-Q3     Argentina       Buenos Aires        17240
12      1/10/2011       2011-Q4     Argentina       Buenos Aires        20465
13      1/1/2012        2012-Q1     Argentina       Buenos Aires        13134
14      1/4/2012        2012-Q2     Argentina       Buenos Aires        15039

借助ETS(A，N，N)预测了第三季度，即2012年第三季度，2012年第四季度和2013年第一季度，预测代码如下

I forecasted the three quarter i.e 2012 q3, 2012 q4 and 2013 q1 with the help of the ETS(A,N,N).Code for the prediction is as below

retail_data.xts<-xts(retail_data$sales_revenue, retail_data$period);
retail_data.ts <- as.ts(retail_data.xts);
retail_data.ets <- ets(retail_data.ts,model="ANN");
retail_data.fore <- forecast(retail_data.ets, h=4);
plot(retail_data.fore);

计算结果为

 Point Forecast    Lo 80    Hi 80     Lo 95    Hi 95
15       14905.37 8925.968 20884.78 5760.6608 24050.09
16       14905.37 7202.071 22608.68 3124.1881 26686.56
17       14905.37 5798.868 24011.88  978.1739 28832.58
18       14905.37 4584.713 25226.04 -878.7150 30689.46

所有预测值都相同.
是因为数据集太小还是我的方法不好?
需要建议.

All the forecast values are the same.
Is it due to the small dataset or my approach is not good?
Need advice.

推荐答案

通过使用model = "ANN"，您可以拟合具有加法误差(A)的简单指数平滑模型.有关可能的模型，请参见help(ets)；对于自动模型选择，请保留模型参数.您的模型没有趋势，也没有季节性(NN).

By using model = "ANN" you are fitting a simple exponential smoothing model with additive errors (A). See help(ets) for possible models or leave the model argument out for automatic model selection. Your model includes no trend and no seasonality (NN).

有关可能模型的数学详细信息，请参见使用指数自动预测的状态空间框架平滑方法，如ets的帮助页面上所述.如第441和442页所述，序列级别 l_t 是原始时间序列 Y_t 的线性函数.在没有趋势和季节性的模型(例如ANN)中，预测 F_ {t + h} 不依赖于 h ， F_ {t + h} = l_t .这就是为什么上面的示例中的预测在所有范围内都是相同的，只是置信区间随着 h 的增加而扩大.

Mathematical details on the possible models are given in A state space framework for automatic forecasting using exponential smoothing methods as stated on the help page for ets. As explained on pages 441 and 442, the series level l_t is a linear function of the original time series Y_t. In a model without trend and seasonality (e.g. ANN) the forecasts F_{t+h} are not dependent on h, F_{t+h} = l_t. This is why the forecasts in the above example are the same for all horizons, only the confidence intervals widen with increasing h.

我想在这里讨论哪种模型合适是合适的，但是我认为鉴于时间序列较短，使用指数平滑的方法是合理的.

I guess a discussion on which model is appropriate would be OT here, but I think your approach using exponential smoothing is reasonable given the short time series.

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