Oil-Price-Database-For-Forecasting
Oil Price Database For Forecasting
Oil price plays a vital role in a country's economy. Oil price forecasting helps in making better economic planning and decisions. In this research, a combined architecture of Multivariate Long Short Term Memory (MLSTM) is proposed with Mahalanobis and Z-score transformations. These transformations improve the data to uncorrelated and standardized variance, thus making data more suitable for regression analysis.
The available historical time-series data on the West Texas Intermediate (WTI) oil prices and the factors affecting the oil prices are collected to form the data set.
In this research, five factors that influence oil price are considered for regression analysis and historical daily prices of time series data are collected. The data collected for these time series are from January 4, 2000 to June 10, 2019 with a total of 4947 data points. The other five independent variables - Gold Futures, S&P 500 Futures, US Dollar Index, US 10 Year Bond Yield and Dow Jones Utilities.
This research is published in
https://www.sciencedirect.com/science/article/abs/pii/S0360544221012111
Cite as: Siddhaling Urolagin, Nikhil Sharma, Tapan Kumar Datta, A combined architecture of multivariate LSTM with Mahalanobis and Z-Score transformations for oil price forecasting, Energy, Volume 231, 2021, 120963, ISSN 0360-5442, https://doi.org/10.1016/j.energy.2021.120963.
For more projects on data science:
Dr. Siddhaling Urolagin, PhD, Post-Doc, Machine Learning and Data Science Expert, Passionate Researcher, Deep Learning, Machine Learning and applications, dr.siddhaling@gmail.com