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About

José Manuel Oliveira holds a Licenciatura in Applied Mathematics to Computer Science in 1992, an MSc in Telecommunications in 1996 and a Ph.D. in Engineering Sciences in 2005, all from the University of Porto.

He is an Assistant Professor at the Faculty of Economics, University of Porto, where he teaches in the Mathematics and Information System Group. He is a researcher at INESC TEC since 1992, where he develops work in the Centre for Telecommunications and Multimedia. His research interests are mainly in Wireless Networks, including radio resource management, self-configuration of networks and systems and communications data analytics.

José has participated in several research projects, including the European projects: FP6 DAIDALOS Phase 2, IST VESPER, IST OPIUM and ACTS SCREEN; the QREN projects: SITMe and Portal Douro; the CMU SELF-PVP project; and the P2020 Marecom project.

Interest
Topics
Details

Details

001
Publications

2016

A Procedure for Identification of Appropriate State Space and ARIMA Models Based on Time-Series Cross-Validation

Authors
Ramos, P; Oliveira, JM;

Publication
ALGORITHMS

Abstract
In this work, a cross-validation procedure is used to identify an appropriate Autoregressive Integrated Moving Average model and an appropriate state space model for a time series. A minimum size for the training set is specified. The procedure is based on one-step forecasts and uses different training sets, each containing one more observation than the previous one. All possible state space models and all ARIMA models where the orders are allowed to range reasonably are fitted considering raw data and log-transformed data with regular differencing (up to second order differences) and, if the time series is seasonal, seasonal differencing (up to first order differences). The value of root mean squared error for each model is calculated averaging the one-step forecasts obtained. The model which has the lowest root mean squared error value and passes the Ljung-Box test using all of the available data with a reasonable significance level is selected among all the ARIMA and state space models considered. The procedure is exemplified in this paper with a case study of retail sales of different categories of women's footwear from a Portuguese retailer, and its accuracy is compared with three reliable forecasting approaches. The results show that our procedure consistently forecasts more accurately than the other approaches and the improvements in the accuracy are significant.

2016

Evaluating the Forecasting Accuracy of Pure Time Series Models on Retail Data

Authors
Ramos, P; Oliveira, JM; Rebelo, R;

Publication
ADVANCES IN MANUFACTURING TECHNOLOGY XXX

Abstract
Forecasting future sales is one of the most important issues that is beyond all strategic and planning decisions in effective operations of retail supply chains. For profitable retail businesses, accurate sales forecasting is crucial in organizing and planning purchasing, production, transportation and labor force. Retail sales series belong to a special type of time series that typically contain strong trend and seasonal patterns, presenting challenges in developing effective forecasting models. This paper 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. An approach based on cross-validation is used to identify automatically appropriate state space and ARIMA models. The forecasting performance of these models is also compared by examining the out-of-sample forecasts. The results indicate that the overall out-of-sample forecasting performance of ARIMA models evaluated via RMSE, MAE and MAPE is better than state space models. The performance of both forecasting methodologies in producing forecast intervals was also evaluated and the results indicate that ARIMA produces slightly better coverage probabilities than state space models for the nominal 95% forecast intervals. For the nominal 80% forecast intervals the performance of state space models is slightly better.

2016

Management of Promotional Activity Supported by Forecasts Based on Assorted Information

Authors
Ribeiro, C; Oliveira, JM; Ramos, P;

Publication
ADVANCES IN MANUFACTURING TECHNOLOGY XXX

Abstract
Aggressive marketing causes rapid changes in consumer behavior and some significant impact in the retail business. In this context, the sales forecasting at the SKU level can help retailers to become more competitive by reducing inventory investment and distribution costs. Sales forecasts are often obtained combining basic univariate forecasting models with empirical judgment. However, more effective forecasting methods can be obtained by incorporating promotional information, including price, percentage of discount (direct discount or loyalty card discount), calendar events and weekend indicators not only from the focal product but also from its competitors. To deal with the high dimensionality of the variable space, we propose a two-stage LASSO regression to select optimal predictors and estimate the model parameters. At the first stage, only focal SKUs promotional explanatory variables are included in the Autoregressive Distributed Lag model. At the second stage, the in-sample forecast errors from the first stage are regressed on the explanatory variables from the other SKUs in the same category with the focal SKU, and to use that information more effectively three different approaches were considered: select the five top sales SKUs, include all raw promotional information, and preprocess raw information using Principal Component Analysis. The empirical results obtained using daily data from a Portuguese retailer show that the inclusion of promotional information from SKUs in the same category may improve the forecast accuracy and that better overall forecasting results may be obtained if the best model for each SKU is selected.

2016

Sales Forecasting in Retail Industry Based on Dynamic Regression Models

Authors
Pinho, JM; Oliveira, JM; Ramos, P;

Publication
ADVANCES IN MANUFACTURING TECHNOLOGY XXX

Abstract
Sales forecasts gained more importance in the retail industry with the increasing of promotional activity, not only because of the considerable portion of products under promotion but also due to the existence of promotional activities, which boost product sales and make forecasts more difficult to obtain. This study is performed with real data from a Portuguese consumer goods retail company, from January 2012 until April 2015. To achieve the purpose of the study, dynamic regression is used based on information of the focal product and its competitors, with seasonality modelled using Fourier terms. The selection of variables to be included in the model is done based on the lowest value of AIC in the train period. The forecasts are obtained for a test period of 30 weeks. The forecasting models overall performance is analyzed for the full period and for the periods with and without promotions. The results show that our proposed dynamic regression models with price and promotional information of the focal product generate substantially more accurate forecasts than pure time series models for all periods studied.

2016

The self-configuration of nodes using RSSI in a dense wireless sensor network

Authors
Abdellatif, MM; Oliveira, JM; Ricardo, M;

Publication
TELECOMMUNICATION SYSTEMS

Abstract
Wireless sensor networks (WSNs) may be made of a large amount of small devices that are able to sense changes in the environment, and communicate these changes throughout the network. An example of a similar network is a photo voltaic (PV) power plant, where there is a sensor connected to each solar panel. The task of each sensor is to sense the output of the panel which is then sent to a central node for processing. As the network grows, it becomes impractical and even impossible to configure all these nodes manually. And so, the use of self-organization and auto-configuration algorithms becomes essential. In this paper, three algorithms are proposed that allow nodes in the network to automatically identify their closest neighbors, relative location in the network, and select which frequency channel to operate in. This is done using the value of the Received Signal Strength Indicator (RSSI) of the messages sent and received during the setup phase. The performance of these algorithms is tested by means of both simulations and testbed experiments. Results show that the error in the performance of the algorithms decreases as we increase the number of RSSI values used for decision making. Additionally, the number of nodes in the network affects the setup error. However, the value of the error is still acceptable even with a high number of nodes.

Supervised
thesis

2015

Previsão de Vendas no Setor do Retalho sob o efeito de Ações Promocionais

Author
José Manuel Rocha dos Santos Pinho

Institution
UP-FEP

2015

A Scalable, Self-organizing Communications System for very large Wireless Sensor Networks

Author
Mohammad Mahmoud Ahmed Abdellatif

Institution
UP-FCUP

2015

Gestão da Atividade Promocional Baseada em Previsões Suportadas por Informação Competitiva Diversa

Author
Cátia Sofia Pinto Ribeiro

Institution
UP-FEP