الفهرس 
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Abstract
This study investigates the problem of developing accurate and robust price prediction models for Ethereum cryptocurrency. Ethereum is a decentralized blockchain/platform that incorporates diverse types of interactions, including asset, coin, and token trading. It is also a platform for smart contract execution. Ethereum is one of the most popular cryptocurrencies that has witnessed an increase in prices since 2015 while having the second-largest market cap. In this work, we reflect the interactions perceived in the Ethereum network on Ether price prediction models, using Topological Data Analysis (TDA). We introduce a method to extract the features of the indicators of different interaction networks; traded volumes, smart contracts, and transactions between accounts and arrange them for TDA. We use our datasets to train state-ofthe-art models. To assess the robustness of our models, we propose the usage of Adversarial Transformation Networks (ATN) and Generative Adversarial Networks (GAN) to generate adversarial samples against our multivariate time series forecasting and classification models. Our forecasting models achieved a Mean Absolute Percentage Error (MAPE) of 0.702%, 3.82%, and 13.75% for hourly, daily, and weekly forecasts using the LSTM+GRU ensemble and CNN. LSTM+ GRU was found resistant to black-box and transferred attacks across time intervals. The accuracy of classification models reached 90.9% in daily intervals. They are resistant to the proposed black-box attacks in most cases. We conclude that our models are more accurate than previous work, their robustness analysis shows they can withstand minor perturbations in most cases and there is a need to investigate defenses against adversarial attacks in forecasting models.