[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Archive :: Search :: Submit :: Contact ::
Main Menu
Home::
Journal Information::
Articles archive::
For Authors::
For Reviewers::
Registration::
Contact us::
Site Facilities::
Webmail::
::
Search in website

Advanced Search
..
Receive site information
Enter your Email in the following box to receive the site news and information.
..
:: Volume 4, Issue 14 (3-2014) ::
2014, 4(14): 111-150 Back to browse issues page
Forecasting natural gas spot price with Nonparametric Nonlinear Model
Narges Salehnia , Mohamad Ali Falahi , Ahmad Seifi , Mohammad Hossein Mahdavi Adeli
Abstract:   (5757 Views)
Developing models for accurate natural gas spot price forecasting is critical because these forecasts are useful in determining a range of regulatory decisions covering both supply and demand of natural gas or for market participants. A price forecasting modeler needs to use trial and error to build mathematical models (such as ANN) for different input combinations. This is very time consuming since the modeler needs to calibrate and test different model structures with all the likely input combinations. In addition, there is no guidance about how many data points should be used in the calibration and what accuracy the best model is able to achieve. In this study, the Gamma Test has been used for the first time as a mathematically nonparametric nonlinear smooth modeling tool to choose the best input combination before calibrating and testing models. Then, several nonlinear models have been developed efficiently with the aid of the Gamma test, including regression models Local Linear Regression (LLR), Dynamic Local Linear Regression (DLLR) and Artificial Neural Networks (ANN) models. We used daily, weekly and monthly spot prices in Henry Hub from Jan 7, 1997 to Mar 20, 2012 for modeling and forecasting. Comparing the results of regression models show that DLLR model yields higher correlation coefficient and lower MSError than LLR and will make steadily better predictions. The calibrated ANN models specify the shorter the period of forecasting, the more accurate results will be. Therefore, the forecasting model of daily spot prices with ANN can interpret a fine view. Moreover, the ANN models have superior performance compared with LLR and DLLR. Although ANN models present a close up view and a high accuracy of natural gas spot price trend forecasting in different timescales, its ability in forecasting price shocks of the market is not notable.
Keywords: Natural gas, spot price, Gamma Test, Nonparametric nonlinear model
     
Type of Study: Applicable | Subject: انرژی، منابع و محیط زیست
Received: 2012/11/2 | Accepted: 2013/06/2 | Published: 2014/08/13
Send email to the article author

Add your comments about this article
Your username or Email:

CAPTCHA


XML   Persian Abstract   Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Salehnia N, Falahi M A, Seifi A, Mahdavi Adeli M H. Forecasting natural gas spot price with Nonparametric Nonlinear Model. Journal title 2014; 4 (14) :111-150
URL: http://jfm.khu.ac.ir/article-1-577-en.html


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 4, Issue 14 (3-2014) Back to browse issues page
فصلنامه تحقیقات مدلسازی اقتصادی Journal of Economic Modeling Research
Persian site map - English site map - Created in 0.09 seconds with 37 queries by YEKTAWEB 4666