Stock Prediction Using Machine Learning

Singh, Shubha; Gutta, Sreedevi; Hadaegh, Ahmad

doi:10.37394/232018.2021.9.17

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…LSTM was first proposed by Hochreiter and Schmidhuber in 1997, and later became very popular especially to address time series prediction problems [9]. Being a modified RNN method, LSTM works well on a large variety of problems, and is widely used now.…”

Section: Methodology and Proposed Model A Long Short-term Memory (Lstm)mentioning

confidence: 99%

Analysis & Stock Price Prediction and Forecasting Using Different LSTM Models

Raut

2024

IJSREM

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The objective of this research is to develop a Deep Learning model to forecast the stock price, by using the variant of Long Short-Term Memory. This model predicts the close price of the stock for the future selected date, choosing as inputs the following data: open, high, low, adj close and close prices. This model shows a comparative analysis between three different LSTM networks: Long Short-Term Memory (LSTM), Stacked Long Short-Term Memory (Stacked LSTM), and Stacked Bi-directional Long Short-Term Memory (Stacked Bidirectional LSTM) concluding which one is the best and implementing the model using that variant. We have used the historical stock prices data from yahoo’s financial website over 5 years, by choosing multiple datasets: Apple, Amazon, Google, Meta, Microsoft and Tesla (daily values). In order to get effective in the forecasting model, we have tested the network with different iterations and epochs. The model represents Multiple Graphs for data visualization in different comparisons. We have estimated the effectiveness of our proposed model by using the following performance indicators: the Mean Square Error (MSE), the Root Mean Square Error (RMSE), and the R-Squared of the model. The experimental results clearly show that our Stacked Bi-LSTM model has the highest accuracy values when comparing with the LSTM and Stacked LSTM Models. Hence, we can conclude that our Stacked Bi-LSTM Model is suitable for accurate prediction of the stock market time series. Key Words: stock price prediction, Machine Learning, stacked LSTM, Bi-directional LSTM, Deep Learning, Data pre-processing techniques, Data normalization, Data Visualization, Training and Testing Set, Financial Time Series, Future prediction

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Section: Methodology and Proposed Model A Long Short-term Memory (Lstm)mentioning

confidence: 99%

Analysis & Stock Price Prediction and Forecasting Using Different LSTM Models

Raut

2024

IJSREM

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Machine Learning-Based Comprehensive Framework for Stock Prediction

Shetty,

Patil,

Sawant

et al. 2024

Lecture Notes in Networks and Systems

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A Quadratic Model based Conjugate Gradient Optimization Method

Halil,

Moghrabi,

Fawze

et al. 2023

Wseas Transactions on Mathematics

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In this paper, we introduce a nonlinear scaled conjugate gradient method, operating on the premise of a descent and conjugacy relationship. The proposed algorithm employs a conjugacy parameter that is determined to ensure that the method generates conjugate directions. It also utilizes a parameter that scales the gradient to enhance the convergence behavior of the method. The derived method not only exhibits the crucial feature of global convergence but also maintains the generation of descent directions. The efficiency of the method is established through numerical tests conducted on a variety of high-dimensional nonlinear test functions. The obtained results attest to the improved behavior of the derived algorithm and support the presented theory.

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Stock Prediction Using Machine Learning

Cited by 3 publications

References 7 publications

Analysis & Stock Price Prediction and Forecasting Using Different LSTM Models

Analysis & Stock Price Prediction and Forecasting Using Different LSTM Models

Machine Learning-Based Comprehensive Framework for Stock Prediction

A Quadratic Model based Conjugate Gradient Optimization Method

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