Multi-modal transform-based fusion model for new product sales forecasting

New product sales prediction is crucial for the digital economy as it enables businesses to make informed decisions about product development, inventory management, marketing strategies, and ultimately driving economic growth and innovation. In the digital economy era, traditional sales forecasting methods often struggle to address the unique challenges of forecasting demand for new products, primarily due to limited historical data and high levels of uncertainty. To address this challenge, we propose a multi-modal transform-based fusion model for new product sales prediction (M2TFM), which integrates multiple data sources (e.g., product images, attributes, text descriptions and context factors like holidays, weather and trends.) to predict new product sales with remarkable accuracy. The proposed method leverages diffusion embedding to fuse heterogeneous data modalities including images, text, and time series into a unified representation that models their complex interactions. By encoding multi modal data using Transformer self-attention, our approach is able to extract nuanced signals across modalities to make more accurate new product sales forecasts. We perform a comprehensive evaluation on a large e-commerce dataset with more than 10,000 fashion items, and the results demonstrate that the proposed method is more effective than existing state-of-the-art baselines for new product sales forecasting.