International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 04 | Apr 2024
p-ISSN: 2395-0072
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“Comparative Analysis of LSTM and GRU Algorithms for Stock Market Prediction: Development of a Web Application” Prof. D.A. Gore1, Sahil Jhodge2, Ankit Singh3, Vaibhav Survase4, Vallabh Yevade5 1Professor, Department of Computer Engineering, NESGI, Savitribai Phule Pune University, Pune 412213, India
2,3,4,5Undergraduate Students, Department of Computer Engineering, NESGI, Savitribai Phule Pune University, Pune
412213, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Machine learning algorithms for stock market
maximize portfolio allocation. Financial institutions use predictive analytics to create trading strategies, control risk exposure, and provide clients with customized investment solutions. Market projections are also used by regulatory agencies and policymakers to assess the state of the economy, implement necessary policies, and preserve market stability.
prediction have attracted a lot of interest because they have the ability to help investors make wise selections. This study compares the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms for stock market prediction. A comparative empirical analysis reveals that GRU outperforms LSTM in stock price prediction. The methodology section provides a detailed description of the experimental setup, evaluation metrics, and data preparation techniques used to compare LSTM with GRU. The results show how successfully the complex patterns present in stock market data are captured by GRU. A web application for stock prediction is made using the GRU algorithm following a comparison of the algorithms. The application provides users with real-time stock predictions based on previous data, allowing them to make wellinformed investment decisions on time.
The goal of researchers and practitioners using machine learning is to improve the precision and dependability of stock market forecasts. Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are two of the many machine learning algorithms that have gained prominence because of their capacity to represent temporal connections and identify intricate patterns in sequential data.
1.1 Long Short-Term Memory
Key Words: Stock Market Prediction, Deep Learning, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Comparative Analysis, yfinance Dataset.
Hochreiter and Schmidhuber invented Long Short-Term Memory (LSTM) networks in 1997 to address the vanishing gradient problem inherent in regular RNNs. LSTM's fundamental innovation is its capacity to selectively keep or delete information over numerous time steps via a series of gating mechanisms. These gates, which include the input gate, forget gate, and output gate, control the flow of information within the network, allowing it to learn and remember long-term dependencies more efficiently. The LSTM architecture is made up of memory cells that maintain a cell state, allowing information to be stored over time while being selectively updated or forgotten based on input signals. Because of its ability to store and retrieve information over long sequences, LSTM is ideal for applications that need the modeling of complex temporal relationships, such as predicting stock values based on previous market data.
1. INTRODUCTION The stock market is a fundamental cornerstone of international finance, reflecting the hopes, fears, and fluctuations of economies all over the world. The stock market constantly tests analysts' and investors' ability to foresee future trends and make well-informed judgements due to its highly volatile character and complex interaction of several elements. The use of machine learning algorithms has become a viable method for predicting stock prices and market movements in response to this demand. For investors looking to take advantage of market trends and reduce risk, predicting stock market moves has long been considered the ultimate goal. Conventional techniques of analysis, such technical and fundamental analysis, have given important insights into the behavior of markets. But in order to find hidden patterns and correlations, more sophisticated methodologies are required due to the sheer volume and complexity of market data.
1.2 Gated Recurrent Unit A relatively recent development in the field of recurrent neural networks is the Gated Recurrent Unit (GRU), which was put out by Cho et al. in 2014. GRU introduces gating mechanisms to overcome the issue of vanishing gradients in conventional RNNs, much like LSTM. But GRU streamlines the architecture, making it more streamlined
Precise forecasts have significant ramifications for different players in the financial system. Predictive models are used by investors to find profitable ventures and
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