Subscribe Now Subscribe Today
Science Alert
 
Blue
   
Curve Top
Trends in Applied Sciences Research
  Year: 2009 | Volume: 4 | Issue: 3 | Page No.: 126-137
DOI: 10.3923/tasr.2009.126.137
 
Facebook Twitter Digg Reddit Linkedin StumbleUpon E-mail

A Comparative Study of Neural Networks and Non-Parametric Regression Models for Trend and Seasonal Time Series

Dursun Aydin

Abstract:
In this study, we will investigate and compare the performance of some forecasting methods for time series with both trend and seasonal patterns. The forecasting performance has been compared with six models and these include: Auto Regressive Integrated Moving Average (ARIMA), Smoothing Spline Model (SSM), Regression Spline Model (RSM), Additive Regression Model (ARM), Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) network models. The SSM, RSM and ARM are called as non-parametric regression models, whereas MLP and RBF are known as artificial neural network models. For these models, we conducted a comparison based on actual data sets, the number of tourist coming to Turkey. The empirical results obtained have shown that MLP performed better than other models. In addition, the SSM can be considered as an alternative to MLP.
PDF Fulltext XML References Citation Report Citation
 RELATED ARTICLES:
  •    Forecasting Key Macroeconomic Variables of the South African Economy using Bayesian Variable Selection
How to cite this article:

Dursun Aydin , 2009. A Comparative Study of Neural Networks and Non-Parametric Regression Models for Trend and Seasonal Time Series. Trends in Applied Sciences Research, 4: 126-137.

DOI: 10.3923/tasr.2009.126.137

URL: https://scialert.net/abstract/?doi=tasr.2009.126.137

COMMENT ON THIS PAPER
 
 
 

 

 
 
 
 
 
 
 
 
 

 
 
 
 
 
 
 

Curve Bottom