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@article{168193,
        author = {Ankita Soni and Dr. Sachin Patel},
        title = {A Survey on Data Driven Models for Crypto Price Forecasting},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {5},
        pages = {124-129},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168193},
        abstract = {Crypto price prediction is a category of time series prediction which extremely challenging due to the dependence of crypto prices on several financial, socio-economic and political parameters etc. Moreover, small inaccuracies in crypto price predictions may result in huge losses to firms which use crypto price prediction results for financial analysis and investments.  Conventional statistical methods render substantially lesser accuracy compared to new age machine learning techniques. This machine learning based techniques are being used widely for crypto price prediction due to relatively higher accuracy compared to conventional statistical techniques. This paper presents a review on contemporary data driven approaches for crypto currency forecasting highlighting the salient attributes. Moreover, the identified non-trivial research gap in the existing approaches has been used as an underpinning for subsequent direction of research in the domain. The paper culminates with the performance metrics and concluding remarks.},
        keywords = {Crypto price Forecasting, Data Drien Models, Regression Analysis, Machine Learning, Performance Metrics.},
        month = {October},
        }