Multimodal Deep Learning for Oil Price Forecasting Using Economic Indicators

Abstract

In the evolving landscape of global energy, accurately forecasting oil prices plays a pivotal role in strategizing the transition to green energy. Traditional forecasting methods, though widely used, often fall short in capturing the multifaceted influences on oil price dynamics. This research introduces a multimodal deep learning approach that incorporates both time series oil price data and key economic indicators to enhance forecasting accuracy. By employing a combination of Long Short-Term Memory (LSTM) and Temporal Convolutional Networks (TCN), the model adeptly captures temporal patterns and economic influences. The findings not only demonstrate the model's superior performance over traditional methods but also underscore the profound impact of specific economic indicators on oil prices.