AI-Driven Cryptocurrency Price Prediction for Enhanced Decision-Making in E-Commerce

Cryptocurrency price prediction is a critical challenge in financial technology (FinTech), with significant implications for eCommerce. This study proposes a hybrid Temporal Convolutional Network (TCN)-Transformer model to predict Ethereum prices, enhancing decision-making in online transactions. The model is trained on historical Ethereum price data and achieves high accuracy in forecasting future prices. Its applications in eCommerce include risk management in cryptocurrency payment systems, optimized transaction timing, and intelligent customer support tools. To validate its effectiveness, we simulate a real-world eCommerce scenario where an online store leverages the model for cryptocurrency payments. The results indicate that integrating AI-driven price prediction can mitigate risks associated with price volatility and enhance business profitability. This research contributes to the intersection of artificial intelligence and eCommerce by providing a practical framework for leveraging cryptocurrency forecasts in financial management and transaction optimization.

AI-Driven Cryptocurrency Price Prediction for Enhanced Decision-Making in E-Commerce Keywords:

eCommerce , Financial Technology , Artificial Neural Networks , Transformers , Cryptocurrency Price Prediction