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[转载]【统计学】【2019】单变量时间序列销售预测方法的比较研究

已有 269 次阅读 2021-5-17 20:08 |系统分类:科研笔记|文章来源:转载

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本文为加拿大滑铁卢大学(作者:Vishvesh Shah)的硕士论文,共45页。

 

销售时间序列预测专家、数据科学家和管理者经常使用时间序列预测方法来预测销售情况。尽管如此,如果只有时间序列预报,那么最好使用哪种时间序列方法仍然是一个问题。本文研究并评价了不同的销售时间序列预测方法:乘性Holt-Winters(HW)、加性HW、季节性自回归综合移动平均(SARIMA)方法、长-短期记忆递归神经网络(LSTM)和Prophet方法在32个单变量销售时间序列上的应用。用于预测销售额的数据来自时间序列数据库(TSDL)。对于均方根误差(RMSE)评估指标,我们发现,与其他比较方法相比,SARIMA方法预测销售额的平均性能最好。为了支持本文的研究结果,提供了每种方的观测性能驱动因素的数学和经济推理。然而,决策者或组织需要在预测准确性和每种方法的缺点之间进行权衡。

 

Sales time-series forecasters, data scientists and managers often use timeseries forecasting methods to predict sales. Nonetheless, it is still a question which time-series method a forecaster is best off using, if they only have time to generate one forecast. This study investigates and evaluates different sales time-series forecasting methods: multiplicative Holt-Winters (HW), additive HW, Seasonal Auto Regressive Integrated Moving Average (SARIMA) (A variant of Auto Regressive Integrated Moving Average (ARIMA)), Long Short-Term Memory Recurrent Neural Networks (LSTM) and the Prophet method by Facebook on thirty-two univariate sales time-series. The data used to forecast sales is taken from time-series Data Library (TSDL). With respect to the Root Mean Square Error (RMSE) evaluation metric, we find that forecasting sales with the SARIMA method offers the best performance, on average, relative to the other compared methods. To support the findings, both mathematical and economic reasoning on the drivers of the observed performance for each method are provided. However, a decision maker or an organization need to evaluate the trade-off between forecasting accuracy and the shortcomings associated with each method.

 

1.       引言

2. 相关工作

3. 方法选择

4. 数据

5. 研究结果

6. 讨论

7. 结论与展望


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