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Article

Short Time Series Forecasting: Recommended Methods and Techniques

Departamento de Posgrado e Investigación, Facultad de Ingeniería, Universidad Autónoma de Tamaulipas, Tampico P.C. 89109, Mexico
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Academic Editor: Dumitru Baleanu
Symmetry 2022, 14(6), 1231; https://doi.org/10.3390/sym14061231
Received: 11 May 2022 / Revised: 28 May 2022 / Accepted: 1 June 2022 / Published: 14 June 2022
(This article belongs to the Special Issue Machine Learning and Data Analysis)
This paper tackles the problem of forecasting real-life crime. However, the recollected data only produced thirty-five short-sized crime time series for three urban areas. We present a comparative analysis of four simple and four machine-learning-based ensemble forecasting methods. Additionally, we propose five forecasting techniques that manage the seasonal component of the time series. Furthermore, we used the symmetric mean average percentage error and a Friedman test to compare the performance of the forecasting methods and proposed techniques. The results showed that simple moving average with seasonal removal techniques produce the best performance for these series. It is important to highlight that a high percentage of the time series has no auto-correlation and a high level of symmetry, which is deemed as white noise and, therefore, difficult to forecast. View Full-Text
Keywords: short-sized time-series; forecasting methods; seasonal extraction; forecasting techniques short-sized time-series; forecasting methods; seasonal extraction; forecasting techniques
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MDPI and ACS Style

Cruz-Nájera, M.A.; Treviño-Berrones, M.G.; Ponce-Flores, M.P.; Terán-Villanueva, J.D.; Castán-Rocha, J.A.; Ibarra-Martínez, S.; Santiago, A.; Laria-Menchaca, J. Short Time Series Forecasting: Recommended Methods and Techniques. Symmetry 2022, 14, 1231. https://doi.org/10.3390/sym14061231

AMA Style

Cruz-Nájera MA, Treviño-Berrones MG, Ponce-Flores MP, Terán-Villanueva JD, Castán-Rocha JA, Ibarra-Martínez S, Santiago A, Laria-Menchaca J. Short Time Series Forecasting: Recommended Methods and Techniques. Symmetry. 2022; 14(6):1231. https://doi.org/10.3390/sym14061231

Chicago/Turabian Style

Cruz-Nájera, Mariel A., Mayra G. Treviño-Berrones, Mirna P. Ponce-Flores, Jesús D. Terán-Villanueva, José A. Castán-Rocha, Salvador Ibarra-Martínez, Alejandro Santiago, and Julio Laria-Menchaca. 2022. "Short Time Series Forecasting: Recommended Methods and Techniques" Symmetry 14, no. 6: 1231. https://doi.org/10.3390/sym14061231

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