Cyberattack-resilient load forecasting with adaptive robust regression

Cyberattacks in power systems that alter the input data of a load forecasting model have serious, potentially devastating consequences. Existing cyberattack-resilient work focuses mainly on enhancing attack detection. Although some outliers can be easily identified, more carefully designed attacks can escape detection and impact load forecasting. Here, a cyberattack-resilient load forecasting approach based on an adaptive robust regression method is proposed, where the observations are trimmed based on their residuals and the proportion of the trim is adaptively determined by an estimation of the contaminated data proportion. An extensive comparison study shows that the proposed method outperforms the standard robust regression in various settings.     

Energy modeling with particular reference to spatial systems

This article focuses the attention on energy models in relation to spatial systems. After a discussion of three types of methods used in the field of energy modeling (econometric models, input output analysis and programming models), the framework of a multi-component regional- energy econometric model is presented.     

The discrete Kalman filter applied to linear regression models: statistical considerations and an application

In this paper we show how the Kalman filter, which is a recursive estimation procedure, can be applied to the standard linear regression model. The resulting "Kalman estimator" is compared with the classical least-squares estimator.
The applicability and (dis)advantages of the filter are illustrated by means of a case study which consists of two parts. In the first part we apply the filter to a regression model with constant parameters and in the second part the filter is applied to a regression model with time-varying stochastic parameters. The prediction-powers of various "Kalman predictors" are compared with "least-squares predictors" by using T heil 's prediction-error coefficient U.     

Natural conjugate priors for the instrumental variables regression model applied to the Angrist–Krueger data

We propose a natural conjugate prior for the instrumental variables regression model. The prior is a natural conjugate one since the marginal prior and posterior of the structural parameter have the same functional expressions which directly reveal the update from prior to posterior. The Jeffreys prior results from a specific setting of the prior parameters and results in a marginal posterior of the structural parameter that has an identical functional form as the sampling density of the limited information maximum likelihood estimator. We construct informative priors for the Angrist–Krueger [1991. Does compulsory school attendance affect schooling and earnings? Quarterly Journal of Economics 106, 979–1014] data and show that the marginal posterior of the return on education in the US coincides with the marginal posterior from the Southern region when we use the Jeffreys prior. This result occurs since the instruments are the strongest in the Southern region and the posterior using the Jeffreys prior, identical to maximum likelihood, focusses on the strongest available instruments. We construct informative priors for the other regions that make their posteriors of the return on education similar to that of the US and the Southern region. These priors show the amount of prior information needed to obtain comparable results for all regions.     

An adaptive empirical likelihood test for parametric time series regression models

We propose an adaptive empirical likelihood (EL) test for a parametric regression model against a class of alternatives for weakly dependent time series observations. The test is formulated by maximizing a standardized version of the EL statistic over a set of smoothing bandwidths. It is demonstrated that the proposed test is able to distinguish the null hypothesis from a series of local alternatives at an optimal rate.     

A note on the estimation of seemingly unrelated regression systems

In a system of simultaneous equations, one obtains the same results for the overidentified equations by applying three-stage least squares to those equations alone as one would obtain by applying three-stage least squares to the entire system. This paper obtains an analogous result for a system of seemingly unrelated regressions. In doing so it generalizes earlier results of several other authors.     

A class of partially adaptive one-step m-estimators for the non-linear regression model with dependent observations

In this paper we consider a class of partially adaptive one-step M-estimators for the non-linear regression model with dependent observations. Those estimators adapt themselves with respect to a measure of the tailthickness of the disturbance distribution (as well as to a measure of the scale). The large-sample behavior of those estimators is examined theoretically for general disturbance distributions and numerically for various specific ones. The estimators considered are motivated by the Student-t maximum-likelihood estimator. Given appropriate specifications of the adaptation parameter the estimators are asymptotically efficient on the family of Student-t distributions including the normal distribution.     

有效刺激消费需求的自回归模型实证研究——基于广东省1981～2008年消费与收入数据

本文分析与居民消费有关的一些因素,认为能刺激消费需求的根本原因是可支配收入的增加,并用实证研究证实了居民当期消费在80%的程度上依赖于当期收入,这与传统的消费取决于能力的理论是吻合的.而有限分布滞后模型则验证了当期收入对后三期消费的抑制作用,这异于传统理论,本文对这一现象从消费意愿及消费品结构的角度作了探讨.     

Recent contributions to censored regression models

A brief survey of estimation of parameters in a censored regression model (known as the Tobit model) and some details of the properties of LAD (least absolute deviation) estimates and tests of significance of linear hypotheses are given.     

A reference architecture for IoT-based logistic information systems in agri-food supply chains

ABSTRACT

Logistics in the food and agribusiness industry must address perishable products, unpredictable supply variations and stringent food safety and sustainability requirements. The Internet of Things (IoT) could contribute significantly to solve these challenges because it allows for remotely controlling the location and conditions of shipments and products. This paper develops a reference architecture for IoT-based logistic information systems in agri-food supply chains. It proposes a hybrid solution that combines the IoT and cloud computing. The architecture supports the provision of affordable tailor-made solutions by utilising technology enablers of the European Future Internet programme and by facilitating the reuse of domain-specific functionalities.     

Top-down strategies based on adaptive fuzzy rule-based systems for daily time series forecasting

This paper presents a data-driven approach applied to the long term prediction of daily time series in the Neural Forecasting Competition. The proposal comprises the use of adaptive fuzzy rule-based systems in a top-down modeling framework. Therefore, daily samples are aggregated to build weekly time series, and consequently, model optimization is performed in a top-down framework, thus reducing the forecast horizon from 56 to 8 steps ahead. Two different disaggregation procedures are evaluated: the historical and daily top-down approaches. Data pre-processing and input selection are carried out prior to the model adjustment. The prediction results are validated using multiple time series, as well as rolling origin evaluations with model re-calibration, and the results are compared with those obtained using daily models, allowing us to analyze the effectiveness of the top-down approach for longer forecast horizons.     

An agent-based modeling optimization approach for understanding behavior of engineered complex adaptive systems

The objective of this study is to present a formal agent-based modeling (ABM) platform that enables managers to predict and partially control patterns of behaviors in certain engineered complex adaptive systems (ECASs). The approach integrates social networks, social science, complex systems, and diffusion theory into a consumer-based optimization and agent-based modeling (ABM) platform. Demonstrated on the U.S. electricity markets, ABM is integrated with normative and subjective decision behavior recommended by the U.S. Department of Energy (DOE) and Federal Energy Regulatory Commission (FERC). Furthermore, the modeling and solution methodology address shortcomings in previous ABM and Transactive Energy (TE) approaches and advances our ability to model and understand ECAS behaviors through computational intelligence. The mathematical approach is a non-convex consumer-based optimization model that is integrated with an ABM in a game environment.     

Media industry creativity management applied to technology

Six cases of media companies venturing into technological innovation are studied. These companies feature expertise in creativity management but lack experience in managing technological innovation, production, or marketing. Failures highlight the needs for clear project definition and comprehension of the project from the outset. Success stories indicate lessons to be learned from media creativity management: the tailoring of ad hoc groups, openness to hiring the very best outside consultants, centralized decision making, top management authority and interest, legitimacy of intuitive decision making, and the creative individual in focus.     

Goal programming applied to police patrol allocation

This paper describes the development of a decision-making methodology to aid the police patrol planner in resolving the patrol allocation problem. The methodology developed provides information to assist in two major decision areas: staff sizing and vehicle requirement schedules. This research uses a queuing based simulation model to derive expected service levels for a number of different performance measures. Given these results, tradeoff curves are mapped between specified performance measures and utilization rates of patrol vehicles. With this information the patrol planner specifies limiting levels for both performance measures and other managerial constraints which, in turn, are inputted into a goal programming algorithm to determine appropriate staff sizes and vehicle requirements. The primary output provided the patrol planner is the required amount of patrol vehicle hours to allocate to each watch per day per precinct in order to satisfy a designated set of performance measure levels and managerial constraints. The methodology presented in this research was developed in conjunction with the Columbus, Ohio, Police Department with data being provided by the Research and Planning Division.     

Mathematical analysis applied to school attendance areas

The objective of this paper is to develop a generalized mathematical model of pupil assignment within school districts. This model can then be used to examine various policies of student integration. Proposed bussing schemes, school location policies, educational parks, attendance boundaries, etc., can be tested for cost, travel time or other measures of efficiency. Extension to other areas of educational planning is feasible.

Mathematical programming techniques are used to assign resources (say school children) to facilities (say schools) subject to restrictions on facilities (say capacity limits) and resources (say a maximum travel time or a desirable range of school “mixtures”) so that a measure of performance, the “objective function” (say total cost or total time of travel) is optimized. The model is intended to allow examination of a wide range of objective functions and system constraints.     

华润万佳的"并购不适症"

一路高歌的华润万佳,近来似乎要走入全面整合的“休眠期”了。 徐刚走了。这位华润万佳的领军人物,这位在2002年11月第四届中国连锁经营协会年会上荣获“中国连锁业突出成就奖”的明日之星,在这个多雨的初春