Predictability evaluation and joint forecasting method for intermittent time series

doi:10.11772/j.issn.1001-9081.2021071196

Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (9): 2722-2731.DOI: 10.11772/j.issn.1001-9081.2021071196

• Data science and technology • Previous Articles Next Articles

Predictability evaluation and joint forecasting method for intermittent time series

Yiping LANG¹, Wentao MAO¹^,²(), Tiejun LUO³, Lilin FAN¹, Yingying REN⁴, Xia LIU¹

^1.College of Computer and Information Engineering，Henan Normal University，Xinxiang Henan 453007，China
^2.Engineering Lab of Intelligence Business and Internet of Things of Henan Province （Henan Normal University），Xinxiang Henan 453007，China
^3.Zhuzhou CRRC Times Electronic Company Limited，Zhuzhou Hunan 412001，China
^4.State Key Laboratory of Shield and Tunneling Technology，Zhengzhou Henan 450001，China

Received:2021-07-08 Revised:2021-09-18 Accepted:2021-09-22 Online:2021-09-30 Published:2022-09-10
Contact: Wentao MAO
About author:LANG Yiping， born in 1997， M. S. candidate. Her research interests include time series forecasting， industrial big data.
LUO Tiejun， born in 1970， senior engineer. His research interests include industrial engineering.
FAN Lilin， born in 1970， Ph. D.， associate professor. His research interests include industrial big data.
REN Yingying， born in 1985， M. S.， senior engineer. Her research interests include industrial big data.
LIU Xia， born in 1996， M. S. candidate. His research interests include time series forecasting， inventory optimization.
Supported by:
National Key Research and Development Program of China(2018YFB1701400);National Natural Science Foundation of China(U1704158);Henan Province Science and Technology Research Project(212102210103)

间歇性时间序列的可预测性评估及联合预测方法

郎祎平¹, 毛文涛¹^,²(), 罗铁军³, 范黎林¹, 任颖莹⁴, 刘侠¹

^1.河南师范大学计算机与信息工程学院，河南新乡 453007
^2.智慧商务与物联网技术”河南省工程实验室（河南师范大学），河南新乡 453007
^3.株洲中车时代电气股份有限公司，湖南株洲 412001
^4.盾构及掘进技术国家重点实验室，郑州 450001

通讯作者: 毛文涛
作者简介:郎祎平（1997—），女，河南开封人，硕士研究生，主要研究方向：时间序列预测、工业大数据；
罗铁军（1970—），男，湖南湘潭人，高级工程师，主要研究方向：工业工程；
范黎林（1970—），男，河南周口人，副教授，博士，主要研究方向：工业大数据；
任颖莹（1985—），女，河南南阳人，高级工程师，硕士，主要研究方向：工业大数据；
刘侠（1996—），男，江西赣州人，硕士研究生，主要研究方向：时间序列预测、库存优化。
基金资助:
国家重点研发计划项目(2018YFB1701400);国家自然科学基金资助项目(U1704158);河南省科技攻关项目(212102210103)

Abstract

Abstract:

In the operation and maintenance of high-end manufacturing enterprises， the spare parts demand occurs randomly， accompanied by a large number of zero demand periods. At the same time， the corresponding sparse parts demand data is of small scale and has intermittent and distribution with lump formation characteristics. Consequently， most of current time series forecasting methods are hard to effectively predict the demand trends. To solve this problem， a predictability evaluation and joint forecasting method for intermittent time series was proposed. Firstly， a new intermittent-similarity metric was proposed. In this metric， the frequency and positions of the "0" element occurring in the two sequences were counted， while the metrics such as maximal information coefficient and average demand interval were combined to evaluate the tendency information and fluctuation pattern of the sequences effectively and realize the quantification of the predictability of the intermittent time series. Then， based on this metric， an intermittent-similarity hierarchical clustering method was constructed to adaptively select the sequences with high similarity and strong predictability as well as eliminate extremely sparse and unpredictable sequences. Moreover， the structured information between the sequences was explored and utilized， a Multi-output Support Vector Regression （M-SVR） model was constructed， thereby achieving the joint prediction of intermittent time series with small-scale data. Finally， the experiments were conducted on two public datasets （UCI （University of California Irvine） gift retail dataset and Huawei computer accessory dataset） and a real-world spare parts after-sales dataset of a large manufacturing enterprise， respectively. The results show that compared with several representative time series forecasting methods， the proposed method can effectively exploit the predictability of various kinds of intermittent sequences and improve the prediction accuracy of intermittent time series with small-scale data. Therefore， the proposed method provides a new solution for the spare parts demand forecasting of manufacturing enterprises.

Key words: demand forecasting, intermittent time series, predictability evaluation, time series forecasting, time series clustering

摘要：

在高端制造企业的运维业务中，配件需求随机发生，且伴随有大量的零需求阶段，同时，对应的配件需求数据量小，且呈现出间歇性和块状分布的特点，导致现有时间序列预测方法难以有效预测配件需求走势。为解决该问题，提出了一种间歇性时间序列的可预测性评估及联合预测方法。首先，提出了一种新的间歇相似度指标，通过统计两条序列中“0”元素出现的频次和位置，并结合最大信息系数和平均需求间隔等度量指标，有效评估了序列的趋势信息和波动规律，并实现了对间歇性序列可预测性的量化；其次，基于该指标，构建了一个间歇相似度层次聚类方法来自适应地筛选相似性高、可预测性强的序列，剔除极度稀疏、无法预测的序列；此外，探索利用序列间的结构化信息，并构建多输出支持向量回归（M-SVR）模型，从而实现小样本下的间歇性序列联合预测；最后，分别在两个公开数据集（UCI礼品零售数据集和华为电脑配件数据集）和某大型制造企业实际配件售后数据集上进行实验。实验结果表明，相比多个典型的时间序列预测方法，所提方法可有效挖掘各类间歇性序列的可预测性，提高小样本间歇性序列的预测精度，从而为制造企业配件需求预测提供了一种新的解决方案。

关键词: 需求预测, 间歇性时间序列, 可预测性评估, 时间序列预测, 时间序列聚类

CLC Number:

TP181

Yiping LANG, Wentao MAO, Tiejun LUO, Lilin FAN, Yingying REN, Xia LIU. Predictability evaluation and joint forecasting method for intermittent time series[J]. Journal of Computer Applications, 2022, 42(9): 2722-2731.

郎祎平, 毛文涛, 罗铁军, 范黎林, 任颖莹, 刘侠. 间歇性时间序列的可预测性评估及联合预测方法[J]. 《计算机应用》唯一官方网站, 2022, 42(9): 2722-2731.

Figures/Tables 18

Fig. 1 Flowchart of the proposed method

Fig. 2 Schematic diagram of MIC calculation of intermittent sequences

Tab. 1 Experimental dataset information

数据集

数据

长度

数据

维度

属性

数目

配件编号、时间（月）、每月故障数量、每月修复入库数量、每月故障出库数量等

Fig. 3 Trend distribution of experimental data in this paper

Fig. 4 Distribution of ADI and CV2 in experimental data

Tab. 2 Comparison methods

算法类型	对比方法
单维时间序列预测	ARIMA^［3］
单维时间序列预测	SVR^［4］
多元时间序列联合预测	ELM^［21］
	LSTM^［8］
	BP神经网络^［7］
	BHT-ARIMA^［16］

Fig. 5 Some clustering result examples of Huawei computer accessory dataset

Fig. 6 Some clustering result examples of UCI gift retail dataset

Fig. 7 Prediction results comparison by different methods on Huawei computer accessory dataset

Fig. 8 Prediction results comparison by different methods on UCI gift retail dataset

Fig. 9 Performance comparison of different prediction methods on public datasets

Fig. 10 Analysis of sequences with abnormal results on Huawei computer accessory dataset

Fig. 11 MSE corresponding to different parameter values on public datasets

Fig. 12 Some clustering result examples on real-world spare parts after-sales dataset

Fig. 13 Prediction results of the proposed method on sequences such as No. 21

Fig. 14 Prediction results of the proposed method on sequences such as No.127

Fig. 15 Comparison of prediction results by different methods on real-world spare parts after-sales dataset

Fig. 16 Performance comparison of different prediction methods on real-world spare parts after-sales dataset

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Predictability evaluation and joint forecasting method for intermittent time series

间歇性时间序列的可预测性评估及联合预测方法

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