Dynamic Time Scan Forecasting: A Benchmark With M4 Competition Data

Authors

  • Rodrigo Barbosa De Santis LADEC Lab, Universidade Federal de Minas Gerais, Av. Antonio Carlos 6627, Belo Horizonte 31270-901, MG, Brazil https://orcid.org/0000-0001-8454-4512
  • Tiago Silveira Gontijo LADEC Lab, Universidade Federal de Minas Gerais, Av. Antonio Carlos 6627, Belo Horizonte 31270-901, MG, Brazil https://orcid.org/0000-0003-2636-899X
  • Marcelo Azevedo Costa LADEC Lab, Universidade Federal de Minas Gerais, Av. Antonio Carlos 6627, Belo Horizonte 31270-901, MG, Brazil https://orcid.org/0000-0002-2330-5056

Keywords:

Univariate methods, M4 competition, benchmarking, dynamic time scan forecasting

Abstract

Univariate forecasting methods are fundamental for many different application areas. M-competitions provide important benchmarks for scientists, researchers, statisticians, and engineers in the field, for evaluating and guiding the development of new forecasting techniques. In this paper, the Dynamic Time Scan Forecasting (DTSF), a new univariate forecasting method based on scan statistics, is presented. DTSF scans an entire time series, identifies past patterns whichare similar to the last available observations and forecasts based on the median of the subsequent observations of the most similar windows in past. In order to evaluate the performance of this method, a comparison with other statistical forecasting methods, applied in the M4 competition, is provided. In the hourly domain, an average sMAPE of 12.9% was achieved using hte method with the default parameters, while the baseline competition - the simple average of the forecasts of Holt, Damped and Theta methods - was 22.1%. The method proved to be competitive in longer time series, with high repeatability.

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Author Biographies

Rodrigo Barbosa De Santis, LADEC Lab, Universidade Federal de Minas Gerais, Av. Antonio Carlos 6627, Belo Horizonte 31270-901, MG, Brazil

Rodrigo Barbosa de Santis graduated in Production Engineering from the Federal University of Juiz de Fora (2015) and Master in Computer Modeling from the Federal University of Juiz de Fora (2018). He has experience in operations management, having worked as a consultant at Natura, BASF, Johnson Johnson, and B2W. His skills include developing and implementing predictive and decision support systems. His main interests are Machine Learning, Artificial Intelligence, Operations Management, and Supply Chain.

Tiago Silveira Gontijo, LADEC Lab, Universidade Federal de Minas Gerais, Av. Antonio Carlos 6627, Belo Horizonte 31270-901, MG, Brazil

Tiago Silveira Gontijo is Ph.D. student and Master in Production Engineering. Bachelor of Business Administration and Economics. He has published books and papers and works as a reviewer for international journals. He has experience in the administration of production systems and in the sanitation production chain. Worked at the Regulatory Agency for Water Supply and Sanitary Sewage Services of the State of MG. Has experience in applied statistics and quantitative methods, with emphasis on renewable energies and time series. He works as an independent consultant in economic regulation.

Marcelo Azevedo Costa, LADEC Lab, Universidade Federal de Minas Gerais, Av. Antonio Carlos 6627, Belo Horizonte 31270-901, MG, Brazil

Marcelo Azevedo Costa is a full professor of Applied Statistical Methods in the Department of Production Engineering at the Federal University of Minas Gerais (UFMG) and faculty member of the Graduate Program in Production Engineering (Stochastic Modeling and Simulation) at the same institution. He holds a degree in Electrical Engineering from the Federal University of Minas Gerais (1999), a Ph.D. in Electrical Engineering from the Federal University of Minas Gerais (2002) in Computational Intelligence. He has done post-doctorate from Harvard Medical School And Harvard Pilgrim Health Care (2007) in Spatial Statistics and Epidemiological Surveillance, and a post-doctorate from Linköping University (2018/Sweden) in Statistical Analysis, Diagnosis and Fault Detection in Industrial Environments. He is currently a collaborating professor in the Laboratory of Intelligence and Computational Technology (LITC/UFMG), developing research projects in artificial neural networks. He is also a collaborating professor at the Center of Research in Efficiency, Sustainability and Productivity (NESP/UFMG), developing research projects in economic regulation in the electricity sector. He has published several papers in international journals such as Socio-Economic Planning Sciences, IEEE Transactions on Power Delivery, Statistical Methods in Medical Research, PLOS One, Measurement, among others. He is a reviewer of international and national journals, as well as the author of book chapters published in English. He is a CEMIG/FAPEMIG R andD project coordinator and researcher in R and D projects. He supervises undergraduate, specialization, master and doctoral students in the following areas: statistical models applied to the electrical sector, applied statistics, network analysis, spatial statistics, time series analysis, artificial neural network theory, and applications. He is enthusiastic about using R language programming, which he has applied to solve many practical statistical problems including Big Data analysis.

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Published

2022-09-05

How to Cite

De Santis, R. B., Gontijo, T. S., & Costa, M. A. (2022). Dynamic Time Scan Forecasting: A Benchmark With M4 Competition Data. IEEE Latin America Transactions, 21(2), 320–327. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/6948