Authors: Yadav Shubham Shivpratap, Yadav Rakhi Ramkripal, Prof. Rajesh Gaikwad
Abstract: Stock market prediction has always caught the attention of many analysts and researchers. Popular theories suggest that stock markets are essentially a random walk and it is a fool’s game to try and predict them as they have a number of variables involved. In the short term, they behave like a voting machine but in the long term, it acts like a weighing machine and hence there is scope for predicting the market movements for a longer timeframe.
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In recent years, many companies have successfully used ml and various in house and other algorithms to predict market trends. In this project, we are applying various time-series algorithms like Arima, LSTM, etc to successfully make predictions for future stock market trends. We first analyze the data and find trends and relations between them and see if they are suitable for predictions. We have also made an suitable API to make use in any user interface available. For this project we will demonstrate our predictions using android or web interface. The algorithms used show promising results for analyzing different time-series data.
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