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@article{162988,
        author = {Ch.Sai Chand and K.Prasanna and G.Nikitha},
        title = {Cryptocurrency price analysis using Artificial Intelligence},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {11},
        pages = {569-578},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=162988},
        abstract = {Cryptocurrency is playing an increasingly important role in reshaping the financial system due to its growing popular appeal and merchant acceptance. While many people are making investments in Cryptocurrency , the dynamical features, uncertainty, the predictability of Cryptocurrency are still mostly unknown, which dramatically risk the investments . It is a matter to try to understand the factors that influence the value formation. In this study, we use advanced artificial intelligence frameworks of fully connected Artificial Neural Network(ANN) and Long Short-Term Memory (LSTM) Recurrent Neural Network to analysis the price dynamics of Bitcoin, Ethereum, and Ripple. We find that ANN tends to rely more on long-term history while LSTM tends to rely more on short-term dynamics, which indicate the efficiency of LSTM to utilize useful information hidden in historical memory is stronger than ANN . However, given enough historical information ANN can achieve a similar accuracy, compared with LSTM. This study provides a unique demonstration that Cryptocurrency market price is predictable. However, the explanation of the predictability could vary depending on the nature of the involved machine-learning model},
        keywords = {AI, Crypto currency , ANN , LSTM.},
        month = {},
        }