In the market of cryptocurrency the Bitcoins are the first currency which has gain the significant importance. To predict the market price and stability of Bitcoin in Crypto-market, a machine learning based time series analysis has been applied. Time-series analysis can predict the future ups and downs in the price of Bitcoin. For this purpose we have used ARIMA, FBProphet, XG Boosting for time series analysis as a machine learning techniques. The parameters on the basis of which we have evaluated these models are Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R 2 . We conduct experiments on these three techniques but after conducting time series analysis, ARIMA considered as the best model for forecasting Bitcoin price in the crypto-market with RMSE score of 322.4 and MAE score of 227.3. Additionally, this research can be helpful for investors of crypto-market.

Accurate stock market returns are quite difficult for the company because of the unpredictable and non-linear nature of the financial stock markets. With the development of artificial intelligence and increased computer power, programmed prediction approaches have demonstrated that they are increasingly effective in predicting stock values. In this study, the Artificial Neural Network, LSTM, and LR techniques were used to predict the closing price for the following day for five companies belonging to different business sectors. In today's economy, the stock market or equity market has a profound influence. The prediction of stock prices is quite complex, chaotic, and it is a big challenge to have a dynamic environment. Behavioural finance means that investors' decision-making processes are affected by emotions and attitudes in response to particular news. In order to help investors' judgements, we have supplied a technology for the analysis of the stock exchange. The method combines historical price prediction. For predicting, LSTM (Long Short-Term Memory), ANN and LR are employed. It includes the latest information on trade and analytical indicators. Financial data: Open, high, low and close stock prices are used to build new variables needed for model input. The models are validated with standard strategic indicators: RMSE and MAPE. The low values of these two variables indicate that the models are costeffective.

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