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The most popular cryptocurrency used worldwide is bitcoin. Many everyday folks and investors are now investing in bitcoin. However, it becomes quite difficult to evaluate or foresee the price of bitcoin. The price of bitcoin is extremely difficult to forecast due to its swings. By this point, machine learning has developed a number of models to examine the price behaviour of bitcoin using time series data. The digital money, a different type of payment developed utilising encryption methods, is difficult to forecast. By utilising encryption technology, cryptocurrencies may act as both a medium of exchange and a virtual accounting system. To estimate the values of a future time sequence, this work introduces a deep learning-based technique for time series forecasting that treats the current data as time series and extracts the key traits of the past. To overcome the shortcomings of conventional production forecasting, three algorithms-auto-regressive integrated moving averages (ARIMA), long-short-term memory (LSTM) network, and FB-prophet-were investigated and contrasted. We compared the models using historical bitcoin data of past eight years, from 2012 to 2020. The “FB-prophet” model, which is significant, catches variation that might draw attention and avert possible problems.

Mutual funds become the mode of investment for the common people. Net asset value (NAV) of a mutual fund is one of the performance indicators. The NAV data are nonlinear in nature and form the financial time series data. So machine learning methods are useful in developing forecasting models. In this paper, different variants of neural network models, i.e., multilayered perceptron (MLP), extreme learning machine (ELM), and functional link artificial neural network (FLANN), are used for the 1-day, 3-day, 7-day, and 15-day ahead NAV forecasting of one of the Indian mutual funds. The NAV data are divided into training and testing data with 8:2 ratio. The performance of these models is evaluated using RMSE and MAPE values. The experimental results demonstrate that ELM outperformed the other two models in forecasting the NAV values for these two Indian mutual funds.

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In current days, doing research in stock market is much critical as it shows a nonlinear and random nature based upon several factors. In order to make the profit in future, many invests in stock market rely on some forecast. For prediction of the stock price, people or the investment organization uses some methods and tools. Stock price prediction in stock market is providing main role in stock market business. Use of conventional methods such as fundamental and technical study may not guarantee the consistency of the forecast. In many cases, regression analysis is employed for the forecasting of the stock price. In this paper, we survey the some of the competent regression approach for the price prediction of the stock in stock market. The result of these regression analyses has also been further improvised or can be improvised more using more number of variables and machine learning or data science techniques.