Performance of state space and ARIMA models for consumer retail sales forecasting

Forecasting future sales is one of the most important issues that is beyond all strategic and planning decisions in effective operations of retail businesses. For profitable retail businesses, accurate demand forecasting is crucial in organizing and planning production, purchasing, transportation and labor force. Retail sales series belong to a special type of time series that typically contain trend and seasonal patterns, presenting challenges in developing effective forecasting models. This work compares the forecasting performance of state space models and ARIMA models. The forecasting performance is demonstrated through a case study of retail sales of five different categories of women footwear: Boots, Booties, Flats, Sandals and Shoes. On both methodologies the model with the minimum value of Akaike's Information Criteria for the in-sample period was selected from all admissible models for further evaluation in the out-of-sample. Both one-step and multiple-step forecasts were produced. The results show that when an automatic algorithm the overall out-of-sample forecasting performance of state space and ARIMA models evaluated via RMSE, MAE and MAPE is quite similar on both one-step and multi-step forecasts. We also conclude that state space and ARIMA produce coverage probabilities that are close to the nominal rates for both one-step and multi-step forecasts.

Learning business strategies for competitive electronic marketplaces

This paper presents a Multi-Agent Market simulator designed for developing new agent market strategies based on a complete understanding of buyer and seller behaviors, preference models and pricing algorithms, considering user risk preferences and game theory for scenario analysis. This tool studies negotiations based on different market mechanisms and, time and behavior dependent strategies. The results of the negotiations between agents are analyzed by data mining algorithms in order to extract rules that give agents feedback to improve their strategies. The system also includes agents that are capable of improving their performance with their own experience, by adapting to the market conditions, and capable of considering other agent reactions.

Position paper on time and event-triggered communication services in the context of e-manufacturing

Modern factories are complex systems where advances in networking and information technologies are opening new ways towards higher efficiency. Such move is being driven by market rules with ever-increasing competition levels, in search for faster time-to-market, improved process yield, non-stop operations, flexible manufacturing and tighter supply-chain coupling. All these aims present a common requirement, i.e. a realtime flow of information, from the plant-floor up to the management, maintenance, suppliers and clients, to support accurate monitoring and control of the factory. This stresses the importance achieved by the communication infrastructure in modern manufacturing industry. This paper presents the authors view concerning the current trends in modern factory communication systems. It addresses the problems of seamlessly integrating different information flows with diverse requirements, mainly in terms of timeliness. In this aspect, the debate between event-triggered and time-triggered communication is revisited as well as the joint support for both types of traffic. Finally, a view of where factory communication systems are moving to is also presented, showing the impact of open and widely available technologies.

A Novel Run-Time Monitoring Architecture for Safe and Efficient Inline Monitoring

20th International Conference on Reliable Software Technologies - Ada-Europe 2015 (Ada-Europe 2015), Madrid, Spain.