A fast and scalable ensemble of global models with long memory and data partitioning for the M5 forecasting competition

This work presents key insights on the model development strategies used in our cross-learning-based retail demand forecast framework. The proposed framework outperforms state-of-the-art univariate models in the time series forecasting literature. It has achieved 17th position in the accuracy track of the M5 forecasting competition, which is among the top 1% of solutions.     

Green consumption behavior prediction based on fan-shaped search mechanism fruit fly algorithm optimized neural network

Predicting consumption behavior is very important for adjusting supplier production plans and enterprise marketing activities. Conventional statistical methods are unable to accurately predict green consumption behavior because it is characterized by multivariate nonlinear interactions. The paper proposes an optimized fruit fly algorithm (FOA) and extreme learning machine (ELM) model for consumption behavior prediction. First, to address the problem of uneven search direction of FOA leading to insufficient search ability and low efficiency, the paper proposes a sector search mechanism instead of a random search mechanism to improve the global search ability and convergence speed of FOA. Second, to address the issue that the initial weights and hidden layer bias values of the ELM are randomly generated, which affects the learning efficiency and generalization of the ELM, the paper uses an improved FOA to optimize the weights and bias values of ELM for improving the prediction accuracy. Taking the green vegetable consumption behavior of Beijing residents as an example, the results show the optimization of the initial weight and threshold of ELM by the GA, PSO, FOA, and SFOA, the prediction accuracy of the GA-ELM, PSO-ELM, FOA-ELM, and SFOA-ELM models all surpass those of ELM. Compared with BPNN, GRNN, ELM, GA-ELM, PSO-ELM, and FOA-ELM models, the RMSE value of SFOA-ELM was decreased by 9.45%, 8.40%, 11.89%, 5.84%, 2.22%, and 2.69%, respectively. These findings demonstrate the effectiveness of the SFOA-ELM model in green consumption behavior prediction and provide new ideas for the accurate prediction of consumption behaviors of other green products with similar characteristics.     

水生态系统服务供需视角下的水网乡村 适应性规划策略——以吴江张鸭荡片区为例

苏南乡村地区一直是中国乡村建设的先行区域，特殊的纵横交错的水网结构构成了一个错综复杂的系统，呈现出其他地区不具有的复杂性和生态特殊性。随着城镇的扩张，乡村的发展建设使其水网空间的平衡发展面临极大的挑战。传统单一静态的规划方法逐渐显示出无法适应经济、社会等发展要求的缺陷。苏南乡村地区以水为核心，从水生态系统服务供需关系的视角下探究水网乡村的适应性规划策略更加适应当前的乡村现状和需求。以传统水网乡村空间形态转译为基础，建立水生态系统供需服务评估体系，在评估水生态系统服务供需能力的基础上，分析供需分异模式及供需矛盾。从构建乡村水域空间生态格局、乡村水域空间功能分区规划，以及乡村水域空间多情景预判3个方面，提出苏南水网乡村的适应性规划策略，并为水网乡村的生态实践提出新思路。     

Time preference and international trade

We first consider a closed model, where households' time discount depends on externality in consumption. We can prove that there is a unique steady state, which is a saddle point. Then we extend the model to a two‐country world, and derive the condition on the effects of consumption externality under which there is a unique free trade steady state with saddle‐point stability.     

东南亚国家社交商务与非本土节日消费行为分析——以中国春节为例

在世界范围内,中国春节越来越受消费者欢迎,特别是东南亚消费者会在社交商务中参加中国春节活动。然而,现实中却存在一个矛盾的现象,即一些东南亚消费者春节期间热衷于购买中国商品,而春节过后却抵制中国商品。实证研究发现,东南亚消费者并未完全从态度上偏好中国春节和中国商品,只是出于社交商务的需要才参与中国春节和购买中国商品。东南亚消费者可区分为文化杂食型和节日身份型两个细分市场。其中,文化杂食型消费者包容并喜欢多元文化,会长期偏好中国节日和中国商品;节日身份型消费者仅在中国节日期间会参与中国节日和购买中国商品。这样的结论对指导中国企业实践具有重要价值,首先,为更好地响应“一带一路”倡议,应树立中国品牌意识,在“一带一路”沿线国家培育偏好中国文化和产品的朋友圈,转变消费者态度;其次,为更好地实施“走出去”战略,应在国际市场进行文化杂食和节日身份的细分,对文化杂食型消费者以文化为纽带培育其长期稳定的消费习惯,对节日身份型消费者通过营销策划刺激其消费;其三,为成功进入东南亚市场,对在东南亚运营的社交商务,应侧重培育消费者亲中国文化与产品的态度,并增加其使用社交商务平台的愉悦感。     

城市基础设施与居民消费水平——基于线上线下协同的促进机制

优化城市基础设施对于提升居民消费水平具有积极意义。交通与通讯两类基础设施对居民线上、线下消费的影响存在差异。在普及网购的同时改善城市居民出行条件，使实体店在展示产品、现场体验等方面与电商形成互补，将有助于实现线上、线下渠道的积极互动。以2011—2016年全国近300个地级市构建的面板数据进行计量分析，验证了基础设施对居民消费的促进作用，并发现当前交通、通讯两类设施的交互作用主要体现为协同效应。进一步的研究表明，以完善基础设施支持线上、线下渠道共同发展，还能够适应居民家庭年龄结构变化的新特点。上述结论说明,以完善基础设施促成渠道协同，将对全面优化消费者体验、提升居民消费水平产生重要作用。     

大学生消费行为调查与分析

大学生是社会消费群体的重要组成部分,他们的消费行为特征受到越来越多人的关注。为了更好地了解大学生的具体消费行为,本文采取问卷调查的方式,以合肥师范学院在校大学生作为调查对象,进一步了解大学生的消费理念、方式以及水平、结构等。最后根据调查结果的数据分析,对大学生理性消费提出具体建议,以引导大学生树立正确的消费观念。     

Do you bear to reject them? The effect of anthropomorphism on empathy and consumer preference for unattractive produce

Consumer generally prefer produce with perfect/attractive appearance and reject unattractive produce (e.g., spotted apples, curved cucumbers), which results in global economic and sustainability issues. Researchers are making efforts to find strategies to reduce consumer or retailer waste of unattractive produce. This article aims to study the role of anthropomorphism communication in the marketing of unattractive produce and to identify an empathy-helping underlying psychological mechanism. Three experimental studies found that when unattractive produce is anthropomorphized, the situation of rejection evokes consumer empathy. Feelings of empathy drive consumers to accept and purchase unattractive produce. In addition, the positive effect of anthropomorphism on purchase intentions is moderated by popularity of unattractive produce (e.g., number of shoppers). The effect of anthropomorphism is strengthened when the produce is unpopular, and it is weakened when the produce is popular.     

Optimal targeting of latent tourism demand segments

This paper identifies for the first time the optimal target markets employing the latent tourism demand expenditure, a novel concept in tourism literature. The study quantifies latent tourism demand between each pair of origin-destination through distinguishing by type of tourism and seasonality. It works with market shares that are estimated via a fractional regression model. Moreover, latent demand is clustered using a market segmentation approach based on a latent class regression. Finally, the optimal target markets are chosen depending on the expected latent tourism expenditure. The result has clear policy implications in terms of which markets are promoted, the optimal channels of communication and the maximum budget for each marketing campaign.     

Is the system reliability profitable for retailing and consumer service of a dynamical system under cross-price elasticity of demand?

Retailing strategy is one of the most crucial factors for industries. A proper retailing strategy can help to enhance consumer service and increase the industry's profit. An improved approach to retailing is suggested in this research to deliver superior customer service while maximizing profits in a dynamic system. The study analyzes a retailing strategy for a demand with cross-price elasticity upon the retail price. A product's cross-price elasticity and the system reliability are critical factors in retailing. Understanding the cross-price elasticity of demand between products helps retailers to make pricing decisions that maximize profits by maintaining demand. Imperfect products are produced due to an imperfect production system. The imperfect ones must be adjusted with some costs to make them perfect for better retailing. The system failure rate is crucial for retailing under cross-price elasticity of demand patterns. Production system reliability, cross-price elasticity of demand, and consumer service are all essential factors that can impact a company's success in the market. The production rate is considered time- and system failure rate-dependent. Contradictory to the literature, a dynamical system is proposed for improved retail management, which is solved using the Euler-Lagrange theory. Finally, one can achieve the expected maximum profit for this retail system with optimum selling prices for different products by reducing the system failure rate. Some numerical illustrations with graphical representations are provided to validate the current study. Numerical examples show that applying cross-price elasticity of demand for more than two identical products provides 35% more profit for the retail industry than a single type of product.