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Stock Forecasting Using Prophet vs. LSTM Model Applying Time-Series Prediction


Mohammed Ali Alshara


Vol. 22  No. 2  pp. 185-192


Forecasting and time series modelling plays a vital role in the data analysis process. Time Series is widely used in analytics & data science. Forecasting stock prices is a popular and important topic in financial and academic studies. A stock market is an unregulated place for forecasting due to the absence of essential rules for estimating or predicting a stock price in the stock market. Therefore, predicting stock prices is a time-series problem and challenging. Machine learning has many methods and applications instrumental in implementing stock price forecasting, such as technical analysis, fundamental analysis, time series analysis, statistical analysis. This paper will discuss implementing the stock price, forecasting, and research using prophet and LSTM models. This process and task are very complex and involve uncertainty. Although the stock price never is predicted due to its ambiguous field, this paper aims to apply the concept of forecasting and data analysis to predict stocks.


Predicting; Modelling; Analysis; Machine Learning; Time-series; Stock price; data analysis, Long Short-Term Memory (LSTM), forecasting.