Novel statistical forecasting models for crude oil price, gas price, and interest rate based on meta-heuristic bat algorithm
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{NADERI:2019:JPSE,
-
author = "Meysam Naderi and Ehsan Khamehchi and Behrooz Karimi",
-
title = "Novel statistical forecasting models for crude oil
price, gas price, and interest rate based on
meta-heuristic bat algorithm",
-
journal = "Journal of Petroleum Science and Engineering",
-
volume = "172",
-
pages = "13--22",
-
year = "2019",
-
keywords = "genetic algorithms, genetic programming, Artificial
neural network, Auto-regressive integrated moving
average, Forecasting oil and gas price and interest
rate, Least square support vector machine,
Meta-heuristic bat algorithm",
-
ISSN = "0920-4105",
-
DOI = "doi:10.1016/j.petrol.2018.09.031",
-
URL = "http://www.sciencedirect.com/science/article/pii/S0920410518307861",
-
abstract = "Investment in the petroleum industry is usually faced
with a high degree of risk due to uncertainty
associated with economic factors. Typical factors
include oil and gas price, interest rate, operational
and capital expenditure. In addition, the investment
risk increases as offshore exploration, drilling and
production activities increase. Therefore, accurate
prediction of economic factors is crucial in an
upstream oil and gas sector in order to make better
strategic decisions with minimized risk. In the present
study, four methods of the least square support vector
machine (LSSVM), genetic programming (GP), artificial
neural network (ANN), and auto-regressive integrated
moving average (ARIMA) were initially used to forecast
monthly oil price (MOP), daily gas price (DGP), and
annual interest rate (AIR). Next, the meta-heuristic
bat algorithm (BA) was applied in order to optimally
combine the four mentioned forecasting methods in an
integrated equation as a novel approach. All required
historical data to forecast oil price, gas price and
interest rate were collected from the Central Bank of
the Islamic Republic of Iran. Error analysis in terms
of coefficient of determination (R2), average absolute
relative error percentage (AAREP), root-mean square
error (RMSE), and cumulative probability distribution
versus absolute relative error percentage were used to
compare the prediction performance of forecasting
methods. Error analysis proves that the BA optimized
method is superior over all other forecasting methods
in terms of highest R2 and lowest RMSE. After the BA
optimized method, construction of LSSVM, ARIMA, ANN,
and GP has better prediction ability, respectively. The
results indicate that the BA optimized method reduces
RMSE at least by 6.61percent in MOP forecast; by
18.33percent in DGP forecast; and by 23.13percent in
AIR forecast over all other forecasting methods",
- }
Genetic Programming entries for
Meysam Naderi
Ehsan Khamehchi
Behrooz Karimi
Citations