How household food shopping behaviors changed during COVID-19 lockdown period: Evidence from Beijing,China

The COVID-19 pandemic has had a great impact on food shopping behavior. However, changes in food shopping behavior and related decision-making mechanisms remain unexplored. This study conducted a face-to-face questionnaire survey with 900 residents in three communities in Haidian, Beijing, China, to analyze food shopping behavior during the periods of pre-lockdown, lockdown (January–March 2020), and post-lockdown. Our results indicated that the preference for fresh food e-commerce shopping was accelerated by the lockdown with the usage rate increasing by 48%; the wet markets were seriously hit by the lockdown policy and had not fully recovered by the post-lockdown period (with a 75% reduction in the usage rate during lockdown). The psychological mechanisms of changes in food shopping behavior revealed that the context (e.g., community facilities) and the perceived risk of COVID-19 were two significant factors impacting the four shopping choices investigated. Specifically, the context factors affected behavior directly for supermarkets and convenience stores and indirectly for e-commerce and wet markets. The findings are expected to help in guiding emergency responses during a future pandemic as well as the long-term construction of food supply facilities.     

基于XGBoost和LSTM模型的金融时间序列预测

随着人工智能快速发展,深度学习模型预测金融时间序列成为热点问题.数据及特征选取是决定模型效果的重要环节,用XGBoost模型进行特征优化并预测黄金价格涨跌趋势,再与LSTM模型比较预测效果.用XGBoost分析动量因子特征重要性并选取有效指标;形态因子做历史回测并选取胜率较高的K线指标,预测准确率提升1.5％.以相同因子为LSTM模型特征值预测准确率提升6.5％,达到80％.以欧元和浦发银行股价数据为样本均证实K线指标有效且LSTM模型预测效果优于XGBoost.     

基于极端梯度提升树算法的测井曲线重构方法

南海西部海域油田测井资料采集过程中经常会出现测井曲线缺失的问题,为油气层测井解释精确评价带来了较大困难。为解决该问题,基于XGBoost算法综合利用测井、气测录井资料实现测井曲线重构。该方法支持处理稀疏数据,并且通过网格搜索实现模型参数自动优选。通过在靶区对比不同机器学习算法以及不同测井曲线的重构效果,该方法的调参难度低、计算精度高,可以有效提高曲线重构的工作效率。将该方法应用到南海西部油田低阻水淹层识别工作中,取得了良好的应用效果。     

FFORMA: Feature-based forecast model averaging

We propose an automated method for obtaining weighted forecast combinations using time series features. The proposed approach involves two phases. First, we use a collection of time series to train a meta-model for assigning weights to various possible forecasting methods with the goal of minimizing the average forecasting loss obtained from a weighted forecast combination. The inputs to the meta-model are features that are extracted from each series. Then, in the second phase, we forecast new series using a weighted forecast combination, where the weights are obtained from our previously trained meta-model. Our method outperforms a simple forecast combination, as well as all of the most popular individual methods in the time series forecasting literature. The approach achieved second position in the M4 competition.     

Using structural topic modelling to predict users’ sentiment towards intelligent personal agents. An application for Amazon’s echo and Google Home

Despite growing levels of usage of Intelligent Personal Assistants (hereinafter, IPA), their in-home usage has not been studied in depth by scholars. To increase our understanding of user interactions with IPA, our research created a theoretical framework rooted in technology acceptance models and Uses and Gratification Theory. Our empirical method designs an ambitious analysis of natural and non-structured narratives (user-generated content) on Amazon’s Echo and Google Home. And to identify key aspects that differentially influence the evaluation of IPA our method employs machine-learning algorithms based on text summarisation, structural topic modelling and cluster analysis, sentiment analysis, and XGBoost regression, among other approaches. Our results reveal that (hedonic and utilitarian) benefits gratification, social influence and facilitating conditions have a direct impact on the users’ sentiment for IPA. To sum up, designers and managers should recognise the challenge of increasing the customer satisfaction of current and potential users by adjusting doubtful users’ technical skills and the (hedonic, cognitive, and social) benefits and functionalities of IPA to avoid boredom after a short lapse of time. Finally, the discussion section outlines future lines of research and theoretical and managerial implications.     

基于特征工程的建设工程造价指数预测模型构建

对建设工程造价指数的预测能够有效解决建设项目前期投资估算误差较大引起的成本问题。结合实际工程中对造价指数预测模型的需求，以U市发布的2012—2021年建设工程造价指数为例，通过对比不同特征工程方法构建的XGBoost和神经网络两者之间预测误差，选择最优预测模型进行建设工程造价指数模型预测。结果表明，基于树模型特征筛选和均值填充数据集的XGBoost模型，在测试集、训练集、交叉验证误差最低，能够作为建设工程造价指数预测的模型。     

The use of ICTs and income distribution in Brazil: A machine learning explanation using SHAP values

This study explores the complex relationship between information and communication technologies (ICTs) and socioeconomic characteristics. We employ a cutting-edge explainable machine learning approach, known as SHAP values, to interpret an XGBoost and neural network model, as well as benchmark traditional econometric methods. The application of machine learning algorithms combined with the SHAP methodology reveals complex nonlinear relationships in the data and important insights to guide tailored policy-making. Our results suggest that there is an interaction between education and ICTs that contributes to income prediction. Furthermore, level of education and age are found to be positively associated with income, while gender presents a negative relationship; that is, women earn less than men on average. This study highlights the need for more efficient public policies to fight gender inequality in Brazil. It is also important to introduce policies that promote quality education and the teaching of skills related to technology and digitalization to prepare individuals for changes in the job market and avoid the digital divide and increasing social inequality.