On the role of local blockchain network features in cryptocurrency price formation

Cryptocurrencies and the underpinning blockchain technology have gained unprecedented public attention recently. In contrast to fiat currencies, transactions of cryptocurrencies, such as Bitcoin and Litecoin, are permanently recorded on distributed ledgers to be seen by the public. As a result, public availability of all cryptocurrency transactions allows us to create a complex network of financial interactions that can be used to study not only the blockchain graph, but also the relationship between various blockchain network features and cryptocurrency risk investment. We introduce a novel concept of chainlets, or blockchain motifs, to utilize this information. Chainlets allow us to evaluate the role of local topological structure of the blockchain on the joint Bitcoin and Litecoin price formation and dynamics. We investigate the predictive Granger causality of chainlets and identify certain types of chainlets that exhibit the highest predictive influence on cryptocurrency price and investment risk. More generally, while statistical aspects of blockchain data analytics remain virtually unexplored, the paper aims to highlight various emerging theoretical, methodological and applied research challenges of blockchain data analysis that will be of interest to the broad statistical community. The Canadian Journal of Statistics 48: 561–581; 2020 © 2020 Statistical Society of Canada     

Real-Time Forecasting With a Mixed-Frequency VAR

This article develops a vector autoregression (VAR) for time series which are observed at mixed frequencies—quarterly and monthly. The model is cast in state-space form and estimated with Bayesian methods under a Minnesota-style prior. We show how to evaluate the marginal data density to implement a data-driven hyperparameter selection. Using a real-time dataset, we evaluate forecasts from the mixed-frequency VAR and compare them to standard quarterly frequency VAR and to forecasts from MIDAS regressions. We document the extent to which information that becomes available within the quarter improves the forecasts in real time. This article has online supplementary materials.     

Evaluation of realized volatility predictions from models with leptokurtically and asymmetrically distributed forecast errors

Accurate volatility forecasting is a key determinant for portfolio management, risk management and economic policy. The paper provides evidence that the sum of squared standardized forecast errors is a reliable measure for model evaluation when the predicted variable is the intra-day realized volatility. The forecasting evaluation is valid for standardized forecast errors with leptokurtic distribution as well as with leptokurtic and asymmetric distributions. Additionally, the widely applied forecasting evaluation function, the predicted mean-squared error, fails to select the adequate model in the case of models with residuals that are leptokurtically and asymmetrically distributed. Hence, the realized volatility forecasting evaluation should be based on the standardized forecast errors instead of their unstandardized version.     

Tourist number prediction of historic buildings by singular spectrum analysis

A wooden historic building located in Tibet, China, experienced structural damage when subjected to tourists visit. This kind of ancient building attends to too many visitors every day because heritage sites never fail to attract tourists. There should be a balance between accepting the visitors and the protection of historic buildings considering the importance of the cultural relics. In this paper, the singular spectrum analysis (SSA) is used for forecasting the number of tourist for the building management to exercise maintenance measures to the structure. The analyzed results can be used to control the tourist flow to avoid excessive pedestrian loading on the structure. The relationship between the measured acceleration from the structure and the tourist number is firstly studied. The root-mean-square (RMS) value of the measured acceleration in the passage route of the tourist is selected for forecasting future tourist number. The forecasting results from different methods are compared. The SSA is found slightly outperforms the autoregressive integrated moving average model (ARIMA), the X-11-ARIMA model and the cubic spline extrapolation in terms of the RMS error, mean absolute error and mean absolute percentage error for long-term prediction, whereas the opposite is observed for short-term forecasting.     

A SARIMAX coupled modelling applied to individual load curves intraday forecasting

A dynamic coupled modelling is investigated to take temperature into account in the individual energy consumption forecasting. The objective is both to avoid the inherent complexity of exhaustive SARIMAX models and to take advantage of the usual linear relation between energy consumption and temperature for thermosensitive customers. We first recall some issues related to individual load curves forecasting. Then, we propose and study the properties of a dynamic coupled modelling taking temperature into account as an exogenous contribution and its application to the intraday prediction of energy consumption. Finally, these theoretical results are illustrated on a real individual load curve. The authors discuss the relevance of such an approach and anticipate that it could form a substantial alternative to the commonly used methods for energy consumption forecasting of individual customers.     

等维新息灰色预测模型及预测精度比较——基于我国城镇居民养老保险预测分析

本文在对我国城镇居民养老保险分析和预测中引用了等维新息灰色预测模型并与传统灰色预测模型进行了预测精度比较,最终引入了等维新息处理的灰色预测模型。等维新息灰色预测在每一步预测中,不断推陈出新对原始数据进行等维新息处理。通过对实际案例研究证实,文中提出的预测模型可以在建模过程中成功地反映数据运动规律,具有合理、有效的中长期预测功能。笔者希望通过对我国城镇居民养老保险的灰色预测分析,为今后养老保险系统的后续研究打点基础。     

农村公路网发展规模的影响因素分析

影响农村公路网规模发展的因素很多,这些因素相互之间不仅存在着复杂的内在联系,而且每个因素的影响作用也不相同,笔者提出了以灰色关联度为基础的定量分析方法,找出影响农村公路网发展规模的主要因素,以便对农村公路网规模建设进行科学预测。     

基于需求预测和模块化的应急物资库库联动方法研究

本文从应急物资预储体系整体优化的角度出发,研究应急物资在国家库之间联动的方法。综合考虑了物资的需求不确定性、模块化和时效性等性质,以应急物资预储调整方案总成本最小为目标,构建一个基于机会约束规划的线性库库联动模型。最后,我们将该方法应用到我国20个中央级应急物资储备库进行的物资预储水平模拟调整中,在相同的物资保障水平下较大幅度的降低了预储调整的成本。     

地空导弹总体性能多层次灰色评价

依据地空导弹武器系统总体性能战术技术要求,给出了性能评价因素指标体系的层次结构模型,并采用层次分析法(AHP)确定了各层评价指标的权重。在此基础上,运用灰色系统的原理和方法,提出一种定性分析与定量分析相结合,以定量分析为主的总体性能评价方法。通过在具体实例中的应用表明,采用该评价方法切实可行,使对导弹武器系统的评价更为科学、合理。     

考虑促销因素的医药电商平台需求预测研究

医药电商平台需求预测涉及到药品自身属性及电商平台推出的各种促销活动,本文针对以上影响药品销量的因素提出了时间序列-机器学习组合模型对医药电商平台进行需求预测。传统研究促销因素的需求预测文献将促销阶段商品销量拆分为常规销量和促销增量的线性组合,本文首先拟合各药品促销阶段的常规销量,根据各药品常规销量时间序列数据及服用周期,使用SARIMA模型拟合药品的常规销量预测值,并将常规销量预测值与商品促销特征数据一同输入XGBoost模型进行集成学习预测。本文使用国内某医药电商平台真实销售数据测试组合模型的有效性,结果显示组合预测模型的预测效果相比其他三种传统预测模型更优。此外,本文验证了不同折扣力度下组合预测模型的有效性,以及促销变量在预测模型中的有效性,同时研究了数据共享策略在需求预测中的应用场景,结果显示预测模型在引入促销变量和采用数据共享策略后都能显著降低模型的预测误差。