• K. Harshith Goud
• D.Bikshalu
• Dr. K. Kranthi Kumar
• K.Sai Sangameshvar
• K. Suhrit Kumar

The underlying decentralisation and transparency of cryptocurrencies has recently sparked a lot of attention from investors. Given the volatility and distinctive qualities of cryptocurrencies, precise price prediction is crucial for creating effective trading strategies. To do this, the creators of this paper recommend a state of the art structure that estimates the cost of Bitcoin (BTC), a notable digital currency. The change point location approach is utilized for consistent expectation execution in unnoticed cost range. Time-series data are segmented in particular so that normalisation may be carried out individually based on segmentation. On-chain data is also gathered and used as an input variable to forecast prices. On-chain data refers to the distinct records recorded on the blockchain that are intrinsic in cryptocurrencies. Also, this paper proposes utilizing SAM-LSTM, which consolidates various LSTM modules for on-chain variable gatherings and the consideration component, as the expectation model. SAM-LSTM represents self-consideration based numerous long short-term memory. The usefulness of the suggested framework in predicting BTC prices has been demonstrated in experiments using real-world BTC price data and several technique settings. The greatest MAE, RMSE, MSE, and MAPE values were 0.3462, 0.5035, 0.2536, and 1.3251, respectively, and the findings are encouraging.

Blockchain, cryptocurrency, Bitcoin, deep learning, prediction methods, change detection algorithms

"A deep learning-based cryptocurrency price prediction model that uses on chain data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.p225-p233, May-2023, Available :http://www.jetir.org/papers/JETIR2305G31.pdf

"A deep learning-based cryptocurrency price prediction model that uses on chain data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppp225-p233, May-2023, Available at : http://www.jetir.org/papers/JETIR2305G31.pdf