Forecasting of Stock Price Using LSTM and Prophet Algorithm

Abstract

AbstractAdvancement in new era of computational techniques has offered a wide opportunity to develop and deploy efficient and faster algorithmic solutions to the extensive research problem in various application domain. Although, they are wide application domain accessible but financial forecasting is among the most desirable area of research due to its broad attainment around the globe. In, context with same stock price prediction is quite essential for any organization in respect to financial gain. Mostly, all financial assets are intense to identify the next move of the share market to attain the maximum profit from it. In past, various machine learning and regression techniques have been applied to detect stock price prediction but they are unable to publish the significant results. In current study of approach, we have implemented the LSTM (Long Short-Term Memory) and Prophet algorithm over the stock market data. The financial time series data has been analyzed for last six years to perform the future forecasting and comparative results shows that LSTM out performs the Prophet algorithm for stock market prediction.KeywordsLong short-term memoryProphetDeep learningNeural networkRecurrent neural networkFinancial data