Forecasting the Crude Oil Spot Price by Wavelet Neural Networks Using Oecd Petroleum Inventory Levels

Abstract

In this study, a novel forecasting model based on the Wavelet Neural Network (WNN) is proposed to predict the monthly crude oil spot price. In the proposed model, the OECD industrial petroleum inventory level is used as an independent variable, and the Wavelet Neural Network (WNN) is used to explore the nonlinear relationship between inventories and the price. For verification purposes, the West Texas Intermediate (WTI) crude oil spot price is used for the tested target. Experimental results reveal that the WNN can model the nonlinear relationship between inventories and the price very well. Furthermore, the in-sample and out-of-sample prediction performance also demonstrates that the WNN-based forecasting model can produce more accurate prediction results than other nonlinear and linear models, even when the lengths of the forecast horizon are relatively short or long.

Multi‐model Forecasts of the West Texas Intermediate Crude Oil Spot Price

“…Our goal is to forecast the West Texas Intermediate (WTI) crude oil spot prices using total OECD petroleum inventory levels, surplus production capacity and the Chicago Board Options Exchange (CBOE) Volatility Index (VIX) combined with an information-theoretic (IT) approach. Oil inventories are widely accepted as one of the most important predictors of world oil prices, because oil inventories reflect the demand and supply imbalances that drive fluctuations in the oil price (see Ye et al, 2002Ye et al, , 2005Ye et al, , 2006aMerino and Ortiz, 2005;Manera et al, 2007;He et al, 2010;Kilian and Murphy, 2014;Pang et al, 2011). Surplus production capacity is also included in our model to reflect the increasing importance of producers' abilities to meet growing demand for oil (Ye et al, 2009).…”

Artificial intelligence methods for oil price forecasting: a review and evaluation

“…This review has identified petroleum inventory level as a virtuous market indicator of change in crude oil price. Inventory levels have been a measure of balance or imbalance between production and demand [43]. Ye et al proposed a linear forecasting model using relative inventory level as input for oil prices [36] but later improved Linear-RIL model to non-linear-RIL model due to dynamic relationship between them [37].…”

“…Ye et al proposed a linear forecasting model using relative inventory level as input for oil prices [36] but later improved Linear-RIL model to non-linear-RIL model due to dynamic relationship between them [37]. Pang et al [43] proposed to consider both crude oil inventory level and petroleum product inventory level as input factors for better forecasting performance. Weiqi et al [15] constructed a structural econometric model using relative inventory and OPEC production as explanatory variables for short-run oil price forecasts.…”

“…The results highlighted combination of Harr A Trous wavelet function with back propagation neural network as a promising forecasting tool. Pang et al [43] proposed to predict monthly oil prices using OECD inventory level as independent variable, and used wavelet theory based feed forward neural network to model the non-linear relationship between oil prices and inventory. The proposed model achieved lower RMSE, MAPE and MAE in comparison to both linear and non-linear relative inventory models.…”

Computational Intelligence Techniques With Application to Crude Oil Price Projection: A Literature Survey From 2001–2012

“…Simulated results favored multi-step forecast over one-step. In addition, Pang et al [70] used wavelet neural networks, linear relative inventory and nonlinear relative inventory models to predict one, two and three months ahead prices of crude oil based on data collected for the Organization of Economic Cooperation and Development inventory and West Texas Intermediate crude oil prices. Both data were obtained from the EIAUSDE covering a period from January 1992 to August 2006.…”