Crude Oil Price Prediction Using Artificial Neural Network

One of the critical tasks in artificial neural networks (ANNs) is predicting the price of crude oil, because of countless factors that impact oil prices which involve geopolitical scenarios, natural disasters, economic policies and technical developments in derived methods. ANNs is appropriate for this system due to its influence for understanding the complicated structures in data. Get a ground breaking research project on Crude oil price prediction by seeking expert assistance .We are your number one trusted partner where we provide scholars with Crude oil price prediction topics ,ideas and much more.

The process for creating a crude oil price prediction system is enlisted below,

  1. Data Collection :
  • Historical prices:

Based on the expected granularity, we acquire crude oil prices regularly or monthly.

  • Exogenous Variables

The data is gathered on possible predictor variables like global price of oil production, consumption prices, stock market exponents, currency exchange price and geopolitical scenarios etc.

  1. Data Preprocessing :
  • Normalization :

By employing methods like Min-Max scaling or z-score normalization, the data are standardized by us to carry all features in a similar scale.

  • Lag Features :

Develop lag features for performing time series forecasting. In case we want to predict the crude oil price of tomorrow depending on the previous seven days, we could possess seven lag features.

  • Train-Test Split :

Dataset is separated into training and test sets. For time series data, it is very essential to follow the sequential order. Thus we take the previous 80% of data points that are allocated for training and the last 20% of data points are allocated for testing.

  1. Model Architecture :

This is a simple predictive neural network and it is considered as an initial point for securing the sequential nature of time series data with much enhanced techniques we occupy like LSTM (Long Short-Term Memory) or GRU (Gated Recurrent Units) is applicable.

For the purpose of basic ANN (Artificial Neural Networks),

  • Input layer:

 It consists of neurons which are similar to features. (Number of neurons=number of features)

  • Hidden layers :

One or more hidden layers appropriate with activation functions similar to ReLU.

  • Output layer:

It is associated with a single neuron while it possesses regression and linear activation.

  1. Training :
  • Loss Function:

 If it is a regression problem, then make use of Mean Squared Error (MSE) or Mean Absolute Error (MAE).

  • Optimizer:

Adam or RMSprop are the general optimizers.

  • Early Stopping:

    When the performance starts corrupting, the early stopping method is deployed by us for observing the loss in validation and quitting the training process.

  1. Evaluation:

The performance of the model is estimated on the test set by applying metrics like MAE (Mean Absolute Error), RMSE (Root Mean Squared Error) and MAPE (Mean Absolute Percentage Error).

  1. Refinement and Feature Importance :
  • Feature Engineering :

Regarding their importance in prediction, the features are inserted or separated.

  • Hyperparameter Tuning:

We verify the various network structures like batch sizes, learning rates, etc.

  1. Deployment :

If the performance of model is agreed by us, then approach our model in real -time or close-actual time forecastings. Frequently train the model for retraining and upgrading with latest data.


  • Volatility: The prices of crude oil are famously volatile and it is affected by unexpected global scenarios.
  • Overfitting: Our model is comfortably overfit because of complicated and corrupted data. Dropout layers and regularization methods help us in this process.


Even though ANNs offer better understanding and assist us in forecasting the prices of crude oil over the range, it is so crucial to address the oil market about its implied insecurities and instabilities, then making it complicated for attaining the perfect accuracy regularly. Merging the ANNs (Artificial Neural Networks) with field knowledge and other systematic methods are developing its strength in predictions. Our experienced researchers carry of research work by mixing and merging with various techniques and methodologies.

Crude Oil Price Prediction using Artificial Neural Network Ideas

Crude Oil Price Prediction Using Artificial Neural Network Thesis Topics

                    Get customised thesis topic solutions on Crude oil price prediction where we provide research and thesis support on current topics without compromising our quality at an affordable price. Unlock the door to your academic success by having in all your Crude oil price prediction project.

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  29. Do EEMD based decomposition-ensemble models indeed improve prediction for crude oil futures prices?
  30. Artificial Intelligence-Based Prediction of Crude Oil Prices Using Multiple Features under the Effect of Russia–Ukraine War and COVID-19 Pandemic
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  39. Indonesian Crude Oil Price (ICP) Prediction Using Multiple Linear Regression Algorithm
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