[1] 王海波. 我国零售业态演化的研究[D].北京:北京交通大学,2016.Wang Haibo. Research on the evolution of retail format in China [D]. Beijing: Beijing Jiaotong University, 2016. [2] 北京师范大学.2020新青年新国货消费趋势报告[EB/OL].(2020-11-04) [2021-03-25]. https://sjc.bnu.edu.cn/cms/search/sjc.bnu.edu.cn/kxyj/xzdt/103873.html.Beijing Normal University. 2020 New Youth and New Domestic Products Consumption Trend Report [EB/OL]. (2020-11-04) [2021-03-25]. https://sjc.bnu.edu.cn/cms/search/sjc.bnu.edu.cn/kxyj/xzdt/103873.html. [3] 电子商务研究中心.2015年度中国网络零售市场数据监测报告[EB/OL].(2016-05-16) [2021-03-25]. https://www.100ec.cn/zt/2015ndwlls/.E-commerce Research Center. 2015 China Online Retail Market Data Monitoring Report [EB/OL]. (2016-05-16) [2021-03-25]. https://www.100ec.cn/zt/2015ndwlls/. [4] Xiao Tiaojun,Qi Xiangtong. Price competition, cost and demand disruptions and coordination of a supply chain with one manufacturer and two competing retailers[J]. Omega, 2008, 36(5): 741-753. [5] 张华初,林洪.我国社会消费品零售额ARIMA预测模型[J].统计研究,2006(7):58-60.Zhang Huachu, Lin Hong. ARIMA prediction model of retail sales of consumer goods in China[J]. Statistical Research, 2006(7): 58-60. [6] 王飞.评价贝叶斯向量自回归模型在区域经济预测中的表现[J].中国科技论坛,2014(10):138-143.Wang Fei. Evaluating the performance of Bayesian vector autoregressive model in regional economic forecasting [J]. China Science and Technology Forum, 2014(10):138-143. [7] 桂文林,韩兆洲.我国居民消费季节调整和节日效应测算[J].统计研究,2015,32(2):60-68.Gui Wenlin, Han Zhaozhou. Seasonal adjustment of Chinese residents' consumption and calculation of holiday effects [J]. Statistical Research, 2015, 32(2): 60-68. [8] Ramos P, Santos N, Rebelo R, et al. Performance of state space and ARIMA models for consumer retail sales forecasting[J]. Robotics and Computer Integrated Manufacturing: An International Journal of Manufacturing and Product and Process Development, 2015, 34: 151-163. [9] Chu C W, Zhang G P. A comparative study of linear and nonlinear models for aggregate retail sales forecasting[J]. International Journal of Production Economics, 2003, 86(3): 217-231. [10] Wong W K, Guo Z X. A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm[J]. International Journal of Production Economics, 2010, 128(2): 614-624. [11] Xia Min, Zhang Yingchao, Weng Liguo, et al. Fashion retailing forecasting based on extreme learning machine with adaptive metrics of inputs[J]. Knowledge Based Systems, 2012, 36: 253-259. [12] Xu Jun, Zhou Yun, Zhang Liang, et al. Sportswear retailing forecast model based on the combination of multi-layer perceptron and convolutional neural network[J].Textile Research Journal. June 2021. DOI:10.1177/00405175211020518 [13] Xia Min, Wong W K. A seasonal discrete grey forecasting model for fashion retailing[J]. Knowledge-Based Systems, 2014, 57: 119-126. [14] Arunraj N S, Ahrens D. A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting[J]. International Journal of Production Economics, 2015, 170: 321-335. [15] Aye G C, Balcilar M, Gupta R, et al. Forecasting aggregate retail sales: The case of South Africa[J]. International Journal of Production Economics, 2015, 160(FEB.): 66-79. [16] Loureiro A L D, Miguéis V L, Da S L F M. Exploring the use of deep neural networks for sales forecasting in fashion retail[J]. Decision Support Systems, 2018, 114(OCT.): 81-93. [17] Ma Shaohui, Fildes R. Retail sales forecasting with meta-learning[J]. European Journal of Operational Research, 2021, 288(1): 111-128. [18] Güven , Simsir F. Demand forecasting with color parameter in retail apparel industry using artificial neural networks (ANN) and support vector machines (SVM) methods[J]. Computers & Industrial Engineering, 2020, 147: 106678. [19] Badorf F, Hoberg K. The impact of daily weather on retail sales: An empirical study in brick-and-mortar stores[J]. Journal of Retailing and Consumer Services, 2020, 52: 101921. [20] Huber J, Stuckenschmidt H. Daily retail demand forecasting using machine learning with emphasis on calendric special days[J]. International Journal of Forecasting, 2020, 36(4): 1420-1438. [21] Huang Yiting, Pai Pingfeng. Using the least squares support vector regression to forecast movie sales with data from twitter and movie databases[J]. Symmetry, 2020, 12(4): 625. [22] Li Maobin, Ji Shouwen, Liu Gang. Forecasting of Chinese E-commerce sales: An empirical comparison of ARIMA, nonlinear autoregressive neural network and a combined ARIMA-NARNN Model[J]. Mathematical Problems in Engineering, 2018, 2018:1-12. [23] Andiojaya A, Demirhan H. A bagging algorithm for the imputation of missing values in time series[J]. Expert Systems with Applications, 2019, 129(SEP.):10-26. [24] 金连,王宏志,黄沈滨,等.基于Map-Reduce的大数据缺失值填充算法[J].计算机研究与发展,2013,50(S1):312-321.Jin Lian, Wang Hongzhi, Huang Shenbin, et al. A big data missing value filling algorithm based on Map-reduce[J]. Computer Research and Development, 2013, 50(S1): 312-321. [25] Lee M, An J, Lee Y. Missing-value imputation of continuous missing based on deep imputation network using correlations among multiple IoT data streams in a smart space[J]. IEICE Transactions on Information and Systems, 2019, 102(2): 289-298. [26] Ma Qian, Gu Yu, Lee W C, et al. REMIAN: Real-time and error-tolerant missing value imputation[J]. ACM Transactions on Knowledge Discovery from Data, 2020, 14(6): 1-38. [27] 孙晓丽,郭艳,李宁,等.基于改进神经过程的缺失数据填充算法[J].中国科学院大学学报,2021,38(2):280-287.Sun Xiaoli, Guo Yan, Li Ning, et al. Missing data filling algorithm based on improved neural process[J]. Journal of the University of Chinese Academy of Sciences, 2021, 38(2): 280-287. [28] 王军,李建勋,韩山,等.一种效能评估中缺失数据的填充方法[J].上海交通大学学报,2017,51(2):180-185.Wang Jun, Li Jianxun, Han Shan, et al. A filling method for missing data in effectiveness evaluation[J]. Journal of Shanghai Jiaotong University, 2017, 51(2): 180-185. [29] Ma Qian, Gu Yu, Lee W C, et al. Order-sensitive imputation for clustered missing values[J]. IEEE Transactions on Knowledge and Data Engineering, 2018, 31(1): 166-180. [30] Ramosaj B, Pauly M. Predicting missing values: a comparative study on non-parametric approaches for imputation[J]. Computational Statistics, 2019, 34(6):1-24. [31] Biessmann F, Rukat T, Schmidt P, et al. DataWig: Missing Value Imputation for Tables[J]. Journal of Machine Learning Research,2019, 20:1-6. [32] 熊中敏,郭怀宇,王鑫.基于共享知识的不完整大数据填充方法[J].计算机应用研究,2021,38(9):2683-2689.Xiong Zhongmin, Guo Huaiyu, Wang Xin. Incomplete big data filling method based on shared knowledge [J]. Computer Application Research, 2021, 38(9): 2683-2689. [33] McKinley S, Levine M. Cubic spline interpolation[J]. College of the Redwoods, 1998, 45(1): 1049-1060. [34] Lepot M, Aubin J B, Clemens F H L R. Interpolation in time series: An introductive overview of existing methods, their performance criteria and uncertainty assessment[J]. Water, 2017, 9(10): 796. [35] Junninen H, Niska H, Tuppurainen K, et al. Methods for imputation of missing values in air quality data sets[J]. Atmospheric Environment, 2004, 38(18): 2895-2907. [36] 王永斌,李向文,田珍榛,等.时间序列分解法在我国食物中毒发病人数预测中的应用[J].中国卫生统计,2015,32(4):624-626.Wang Yongbin, Li Xiangwen, Tian Zhenzhen, et al. Application of time series decomposition method in predicting the number of people with food poisoning in my country [J]. China Health Statistics, 2015, 32(4): 624-626. [37] Cleveland R B, Cleveland W S, McRae J E, et al. STL: A seasonal-trend decomposition[J]. Journal of Official Statistics, 1990, 6: 3-73. [38] Box G E, Jenkins G M. Time series analysis for casting and control[M]. San Francisco: Holden-day, 1970. [39] 刘思峰,党规国,方志耕.灰色系统理论及其应用[M]. (第五版).北京:科学出版社, 2010.Liu Sifeng, Dang Guiguo, Fang Zhigeng. Grey system theory and its application[M]. (Fifth Edition). Beijing: Science Press, 2010.
|