In this paper, a hybrid model integrating wavelet and least squares support machines (LSSVM) is proposed for crude oil price forecasting. In this model, Haar à trous wavelet transform is first selected to decompose an original time series into several sub-series with different scales. Then the LSSVM is used to predict each sub-series. And the final oil price forecasting is obtained by reconstru...

in general, energy prices, such as those of crude oil, are affected by deterministic events such as seasonal changes as well as non-deterministic events such as geopolitical events. it is the non-deterministic events which cause the prices to vary randomly and makes price prediction a difficult task. one could argue that these random changes act like noise which effects the deterministic variat...

Crude oil pricing is commonly expressed as a formula referenced to Brent or WTI crude oil. The final price of these two qualities and the spread between WTI and Brent can drive the decision when the purchase of a crude oil cargo is evaluated. A crude oil price-forecasting model is presented. It is based on past data, inventory level and volatility index and it is derived with a neuro fuzzy infe...

We propose a new time series model aimed at forecasting crude oil prices. The proposed specification is an unobserved components model with an asymmetric cyclical component. The asymmetric cycle is defined as a sine-cosine wave where the frequency of the cycle depends on past oil price observations. We show that oil price forecasts improve significantly when this asymmetry is explicitly modelled.

Crude oil is the mixture of petroleum liquids and gases that extracted from ground by wells. It an important source fuel used in production several products. Given role price crude plays, it becomes extremely for managers to predict future while making operational decisions such as: when purchase material, how much produce what modes transportation use. The goal this paper develop a forecasting...

This work examines recent publications in forecasting in various fields, these include: wind power forecasting; electricity load forecasting; crude oil price forecasting; gold price forecasting energy price forecasting etc. In this review, categorization of the processes involve in forecasting are divided into four major steps namely: input features selection; data pre-processing; forecast mode...

Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into severa...

a r t i c l e i n f o JEL classification: C52 C53 Q47 Keywords: Oil prices Futures markets Stochastic processes Kalman filter Forecasting Stochastic process models of commodity prices are important inputs in energy investment evaluation and planning problems. In this paper, we focus on modeling and forecasting the long-term price level, since it is the dominant factor in many such applications....

Sparse and short news headlines can be arbitrary, noisy, ambiguous, making it difficult for classic topic model LDA (latent Dirichlet allocation) designed accommodating long text to discover knowledge from them. Nonetheless, some of the existing research about text-based crude oil forecasting employs explore topics headlines, resulting in a mismatch between further affecting performance. Exploi...

Research on crude oil price forecasting has attracted tremendous attention from scholars and policymakers due to its significant effect the global economy. Besides supply demand, prices are largely influenced by various factors, such as economic development, financial markets, conflicts, wars, political events. Most previous research treats a time series or econometric variable prediction probl...