Daily Crude Oil Price Forecasting Using Hybridizing Wavelet and Artificial Neural Network Model

Ani Shabri · Ruhaidah Samsudin

53 citations0/0 files storedMathematical Problems in Engineering

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Stock Market Forecasting Methods Forecasting Techniques and Applications Market Dynamics and Volatility

Abstract

A new method based on integrating discrete wavelet transform and artificial neural networks (WANN) model for daily crude oil price forecasting is proposed. The discrete Mallat wavelet transform is used to decompose the crude price series into one approximation series and some details series (DS). The new series obtained by adding the effective one approximation series and DS component is then used as input into the ANN model to forecast crude oil price. The relative performance of WANN model was compared to regular ANN model for crude oil forecasting at lead times of 1 day for two main crude oil price series, West Texas Intermediate (WTI) and Brent crude oil spot prices. In both cases, WANN model was found to provide more accurate crude oil prices forecasts than individual ANN model.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 53 citations exist upstream.

References

1994Comparing Predictive Accuracy 1998Forecasting with artificial neural networks:2002Comparing Predictive Accuracy 2002An Introduction to Wavelets and Other Filtering Methods in Finance and Economics 2010A novel hybridization of artificial neural networks and ARIMA models for time series forecasting 2008Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm 2006Modeling and forecasting petroleum futures volatility 2010International evidence on crude oil price dynamics: Applications of ARIMA-GARCH models

Cited by

2022A novel crude oil prices forecasting model based on secondary decomposition 2015Selecting dynamic moving average trading rules in the crude oil futures market using a genetic approach

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.