Published

2003-07-01

COMPARACIÓN DE ALGUNAS DE LAS TÉCNICAS DE PREDICCIÓN EN ANÁLISIS DE SERIES TEMPORALES

COMPARAÇÃO DE ALGUMAS TÉCNICAS DE PREVISÃO EM ANÁLISE DE SÉRIES TEMPORAIS

Keywords:

Modelos Box & Jenkins, suavizamiento exponencial, redes neuronales, predicción (es)
Box & Jenkins models, exponential smoothing, neural networks, forecast (en)
Modelos Box & Jenkins, alisamento exponencial, redes neurais, previsão (pt)

Downloads

Authors

  • Raydonal Ospina Martínez Universidade Federal de Pernambuco
  • Bartolomeu Zamprogno Universidade Federal de Pernambuco
En este trabajo, se comparan los resultados de las metodologías de predicción de series temporales Box y Jenkins, el suavizamiento exponencial y las redes neuronales para diferentes horizontes de pronóstico previamente definidos. Dos series se analizan de acuerdo con estas metodologías y sus predicciones se comparan con diferentes medidas de ajuste.
In this work, we compared the forecasting performance of three approaches: Box & Jenkins, exponential smoothing and neural networks, under different forecasting horizons. Two series are considered
Neste trabalho, nós comparamos o desempenho das metodologias de previsão de séries temporais Box & Jenkins, alisamento exponencial e redes neurais para diferentes horizontes de previsão previamente definidos. São analizadas duas séries de acordo com estas metodologias e suas previsões são comparadas sob diferentes medidas de ajuste.

Article abstract page views

522

Downloads

Download data is not yet available.

How to Cite

Ospina Martínez, R., & Zamprogno, B. (2003). COMPARACIÓN DE ALGUNAS DE LAS TÉCNICAS DE PREDICCIÓN EN ANÁLISIS DE SERIES TEMPORALES. Revista Colombiana De Estadística, 26(2), 129-157. https://revistas.unal.edu.co/index.php/estad/article/view/28680

License

Copyright (c) 2003 Revista Colombiana de Estadística

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).