Short Time Series Forecasting: Recommended Methods and Techniques

Cruz-Nájera, Mariel Abigail; Treviño-Berrones, Mayra Guadalupe; Ponce-Flores, Mirna Patricia; Terán-Villanueva, Jesús David; Castán-Rocha, José Antonio; Ibarra-Martínez, Salvador; Santiago, Alejandro; Laria-Menchaca, Julio

doi:10.3390/sym14061231

Submit to this Journal Review for this Journal Edit a Special Issue

Article Menu

...

Open AccessArticle

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

Authors to whom correspondence should be addressed.

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)

View Full-Text Download PDF

Browse Figures

Citation Export

Abstract

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

▼ Show Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Share and Cite

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

Find Other Styles

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Abstract views Pdf views Html views

Article Menu

Short Time Series Forecasting: Recommended Methods and Techniques

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Article Access Map by Country/Region

Further Information

Guidelines

MDPI Initiatives

Follow MDPI