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.     

Combining forecasts for universally optimal performance

There are two potential directions of forecast combination: combining for adaptation and combining for improvement. The former direction targets the performance of the best forecaster, while the latter attempts to combine forecasts to improve on the best forecaster. It is often useful to infer which goal is more appropriate so that a suitable combination method may be used. This paper proposes an AI-AFTER approach that can not only determine the appropriate goal of forecast combination but also intelligently combine the forecasts to automatically achieve the proper goal. As a result of this approach, the combined forecasts from AI-AFTER perform well universally in both adaptation and improvement scenarios. The proposed forecasting approach is implemented in our R package AIafter, which is available at https://github.com/weiqian1/AIafter.     

A hybrid machine learning model for forecasting a billing period’s peak electric load days

Many models have been studied for forecasting the peak electric load, but studies focusing on forecasting peak electric load days for a billing period are scarce. This focus is highly relevant to consumers, as their electricity costs are determined based not only on total consumption, but also on the peak load required during a period. Forecasting these peak days accurately allows demand response actions to be planned and executed efficiently in order to mitigate these peaks and their associated costs. We propose a hybrid model based on ARIMA, logistic regression and artificial neural networks models. This hybrid model evaluates the individual results of these statistical and machine learning models in order to forecast whether a given day will be a peak load day for the billing period. The proposed model predicted 70% (40/57) of actual peak load days accurately and revealed potential savings of approximately USD $80,000 for an American university during a one-year testing period.     

Fiscal rules to tame the political budget cycle: Evidence from Italian municipalities

The paper provides evidence that fiscal rules can limit the political budget cycle. It uses data on Italian municipalities during the 2000s and shows that: 1) municipalities are subject to political budget cycles in capital spending; 2) the Italian sub-national fiscal rule (Domestic Stability Pact, DSP) introduced in 1999 has been enforced by the central government; 3) municipalities subject to the fiscal rule show more limited political budget cycles than municipalities not subject to the rule. In order to identify the effect, we rely on the fact that the domestic fiscal rule does not apply to municipalities below 5000 inhabitants. We find that the political budget cycle increases real capital spending by about 10–20 percent on average in the years prior to municipal elections and that municipalities subject to the DSP show a pre-electoral increase in capital spending which is only a quarter of the one of municipalities not subject to the rule.     

Economic interpretations of DEA

Research on productive efficiency at the firm level has developed as an important and active strand of research the last decades, both within operations research, management science and economics. Two apparently different definitions of efficiency are examined, but it is shown that when both estimation methods are based on solving linear programming problems the definitions of efficiency are identical. The purpose of the paper is to give the basic ideas of efficiency analyses using DEA as a tool for researchers not so familiar with efficiency analysis and DEA. The concept of shadow prices is given special attention.     

PFIs involving multiple public partners: a case study from the Italian healthcare sector

The financial crisis experienced by many countries since 2008 has given new importance to private finance initiatives (PFIs) for providing public services. This paper analyses the relationships between multiple public and private sector actors participating in a PFI in the healthcare sector in order to better understand the motives and behaviour of public and private sector partners. High levels of trust and the active participation of a regulatory body were found to be significant factors in terms of creating a partnership that benefits all sides.     

Motives behind retailers’ post-entry expansion - Evidence from the Chinese luxury fashion market

This study aims to examine the motives behind retailers’ post-entry expansion in foreign markets. Through case studies of eleven participating luxury fashion retailers in China, qualitative data was collected from twenty-two executive interviews. Although their initial market entry was driven by both reactive (push) and proactive (pull) factors, the motives behind their post-entry expansion have become predominantly proactive, especially long-term growth strategies, the ambition to extend their brands and retail formats to more cities, and the experiences gained in entry markets. The desire to optimize the retail store portfolio through multiple channel strategies have slowed down the expansions of physical stores.     

Banding together in a festival context: Examining effects of a joint-stakeholder external service recovery

Despite substantial growth in revenue and attendance numbers on a global and country level over the past decades, failure is still a fairly common phenomenon in the events/festival industry. Drawing on two scenario studies and data collected in the United States, this research investigated the impact on festival-goers’ evaluations and behavior of joint-stakeholder external service recoveries, in contrast to the prevalent focus on examining service recoveries by a single stakeholder that caused the failure. Findings revealed that festival-goers had different perceptions of and behavioral intentions towards the various stakeholders depending on their perception of fault for the failure. Yet, rather than a predicted joint effect with failure severity, we found a significant interaction effect of the locus of causality with service recovery measures, either via an internal or external recovery. Implications of study findings and directions for future research are discussed.     

An evaluation framework for developing green infrastructure by using a new hybrid multiple attribute decision-making model for promoting environmental sustainability

With the expansion of urbanization caused by the growth of population and industrial activities, the urban/city and suburban areas are facing a variety of environmental threats. Although more research and urban policy has advocated and practiced the development of green infrastructure (GI) to support urban sustainable environment, the evaluation framework for the development of GI for promoting environmental sustainability is still insufficient. Moreover, the Analytic Hierarchy Process (AHP) commonly applied in published literature, makes an unrealistic assumption of independent relationships among dimensions/criteria in decision making for satisfying the real-world problem. Therefore, the purpose of this study is to construct the evaluation framework, including four dimensions and related ten criteria, using a new hybrid-modified multiple attribute decision-making (MADM) model for developing and improving the GI for promoting environmental sustainability. This MADM model is combined with three different methodologies of MADM, including the Decision-Making Trial and Evaluation Laboratory (DEMATEL) for constructing the influential network relation map (INRM) to explore the complex influential inter-relationships and DEMATEL based on Analytic Network Process (DANP) for determining the influential weights with the VIse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) for evaluating and presenting improvement strategies for six different GIs. The empirical study indicates that DEMATEL and DANP Results suggest that decision-makers should pay more attention to the improvement of Design (D₄) and Materials (D₂) in terms of dimensions when utilizing the GI to promote environmental sustainability. Because these dimensions are enhanced, Species (D₁) and Energy (D₃) will be improved in synchronization. From the perspective of criteria, five are key core criteria and need to be focused on first: increasing the green coverage rate (B₉), utilizing sustainable materials (B₄), using ecological engineering (B₈), shaping species biodiversity (B₁), and reducing energy consumption (B₅). Modified VIKOR reveals that “grass swales” are a comparatively better choice among six GIs for promoting environmental sustainability toward achieving the aspiration level. Therefore, this MADM model is beneficial to provide a more convincing assessment framework and improvement strategies for the development of GI for promoting environmental sustainability. As a result, these modified MADM models can be shown more conveniently and reasonably than traditional methods such as traditional AHP or ANP method.