Predicting oil price movements: A dynamic Artificial Neural Network approach

Abstract

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A new approach for dynamic modelling of energy consumption in the grinding process using recurrent neural networks

“…A combination of the Elman and Jordan was used for prediction of health condition of gears [21]. The oil price movement was forecasted with nonlinear autoregressive model with exogenous input (NARX) network [22]. A MLP with output feedback was used for urban storm water run-off prediction [23].…”

“…Forecasting from historic values[13][14][15][16][17][18][19][20][21][22][23][24][25][26]. 2 Prediction of a complete time series in a specific time interval from another distinct time series[27].3 Prediction of complete time series in a specific time interval from multiple and distinct time series (proposed solution) Neural Comput & Applic…”

RVM Kernel Parameters Selection Method based on AIC Applied in Gold Prices

“…Previous researchs showed that the main factors causing the world price of gold shortterm T (USD / troy ounce) fluctuations are: the dollar index x1, Dow-Jones index x2, crude oil prices x3 ($ / bbl), United States 30-year bond x4, euro against the dollar (one euro equivalent to USD) x5, etc [6][7][8]. In this paper, the world gold price April 1, 2013 to November 10, 2013 of 188 short-term factors and the main daily data (except holidays) as 188 samples of short-term price fluctuations on the world gold empirical analysis.…”

“…So τ , m is often used to solve ideological unity [5]. Advantage of the current gold price prediction algorithm is that they mainly use neural network algorithm, support vector machines and other algorithms [6][7], because the least squares support vector machine (LSSVM) has ability of high training speed, good generalization.…”

Wavelet Entropy Based Analysis and Forecasting of Crude Oil Price Dynamics

“…Empirical work utilizing the power of these models is on the rise, but with mixed results. For example, Godarzi et al [6] found that the proposed dynamic Artificial Neural Network model achieves the improved forecasting accuracy than the time series and static neural network model [6]. Yu et al [7] found that Artificial Neural Network (ANN) outperforms Autoregressive Moving Average (ARMA) model, but has room for further improvement using ensemble algorithms.…”