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@article{194010,
        author = {P. Chaitanya and K. Gowtam Dharma and K. Donitha and B.V. Durga Anusha and A.V. Durga Pavan},
        title = {CRYPTOCURRENCY MARKET TREND ANALYSIS ACROSS DIGITAL ASSETS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {7977-7983},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194010},
        abstract = {Cryptocurrency markets are highly volatile and influenced by multiple interconnected digital assets, making accurate trend analysis and prediction a challenging task for investors and analysts. Most existing systems focus on single-asset forecasting, ignoring cross-asset relationships and market-wide behaviour. This paper presents a multi-asset cryptocurrency market trend analysis system that collects historical price data from multiple digital currencies and applies data analytics and machine learning techniques to identify trends, correlations, and future price movements. The proposed system performs data preprocessing, feature extraction, correlation analysis, and predictive modelling using statistical and machine learning approaches. Visualization tools such as trend graphs, comparative charts, and heatmaps are used to enhance interpretability and support decision making. Experimental evaluation demonstrates that incorporating cross-asset analysis improves prediction consistency and market understanding compared to isolated single-coin models. The system provides a scalable framework for real-time cryptocurrency monitoring and intelligent forecasting, which can assist traders, researchers, and financial analysts in understanding market dynamics and making informed investment decisions.},
        keywords = {Cryptocurrency, Market Trend Analysis, Machine Learning, Multi-Asset Prediction, Data Analytics, Correlation Analysis.},
        month = {March},
        }