TRADE FORECASTING WITHAI: INTEGRATING MACHINE LEARNING, DEEPLEARNING, AND EXPLAINABILITYFORCRUDE OIL PRICE PREDICTION

Crude oil price prediction remains a challenging task due to volatile market conditions. Traditional models often fail to adjust, while modern AI models like LSTMcan find patterns but are hard to explain. This study combines Machine Learning (Random Forest), Deep Learning (LSTM), and explainability tools (SHAP and LIME). This study aims to develop a model that is both accurate and easy to understand. The results exhibit that the combined model of ers better accuracy and trust for decision-making.

Authors

Publication Details

Published
2025-08-16
DOI
https://doi.org/10.5121/csit.2025.151504
Primary Topic
Market Dynamics and Volatility
Type
article
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article

TRADE FORECASTING WITHAI: INTEGRATING MACHINE LEARNING, DEEPLEARNING, AND EXPLAINABILITYFORCRUDE OIL PRICE PREDICTION

S. Balasubramanian, M. Natarajan
Market Dynamics and Volatility
article

TRADE FORECASTING WITHAI: INTEGRATING MACHINE LEARNING, DEEPLEARNING, AND EXPLAINABILITYFORCRUDE OIL PRICE PREDICTION

S. Balasubramanian, M. Natarajan
article en

Abstract

Crude oil price prediction remains a challenging task due to volatile market conditions. Traditional models often fail to adjust, while modern AI models like LSTMcan find patterns but are hard to explain. This study combines Machine Learning (Random Forest), Deep Learning (LSTM), and explainability tools (SHAP and LIME). This study aims to develop a model that is both accurate and easy to understand. The results exhibit that the combined model of ers better accuracy and trust for decision-making.

Openalex Percentile: Top 70%
Market Dynamics and Volatility
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TRADE FORECASTING WITHAI: INTEGRATING MACHINE LEARNING, DEEPLEARNING, AND EXPLAINABILITYFORCRUDE OIL PRICE PREDICTION — S. Balasubramanian, M. Natarajan · (2025) | TGRS Research Map | TGRS