Optimizing Stock Market Price Forecasting with ARIMA Parameters using GOA

Problem Definition

From the literature survey conducted, it is evident that the prediction of stock prices remains a challenging task due to the volatile and dynamic nature of the stock market. Existing models often struggle with accuracy and fail to adapt quickly to changing market conditions, leading to unreliable predictions. Additionally, the use of artificial intelligence for stock prediction is hindered by the difficulty in processing real-time information efficiently. This poses a significant limitation as computers may not be able to keep up with the rapidly changing data in the stock market. Researchers also face the challenge of selecting the most appropriate technique for accurate stock price forecasting while minimizing computational complexity.

This decision-making process is crucial for developing effective models that can provide reliable predictions. Furthermore, the static nature of datasets used in previous research works limits the ability to effectively capture changing stock market dynamics over time. The integration of textual data without considering time series also presents a drawback, as the timescale plays a vital role in stock price forecasting accuracy. Addressing these limitations and challenges is essential for improving the predictive capabilities of stock market models and enhancing decision-making processes for investors and researchers alike.

Objective

The objective of this research project is to address the challenges faced by existing stock prediction models by developing a new model based on the ARIMA model. The main goal is to create a stock prediction model with higher accuracy and lower error rates. This objective is achieved through two phases: firstly, by analyzing the performance of five different classifiers with real-time stock data to select the best-performing one, and secondly, by optimizing the chosen model (ARIMA) using the Grasshopper Optimization Algorithm (GOA) to enhance its predictive capabilities and make it more automatic and adaptive. The ultimate aim is to improve stock prediction accuracy by combining traditional machine learning techniques with modern deep learning algorithms and an optimization algorithm, providing reliable predictions for investors and researchers.

Proposed Work

In order to address the challenges faced by existing stock prediction models, a new model based on the ARIMA model is proposed in this research project. The primary goal of this proposed model is to develop a stock prediction model with higher accuracy and lower error rates. To achieve this objective, the project is divided into two phases. In the first phase, the performance of five different classifiers, including ARIMA, NARX, State Space model, LSTM, and Bi-LSTM, is analyzed using real-time stock data from the Yahoo stock market. The dataset comprises information from ten companies over the past five years.

Following this analysis, the best-performing classifier is selected based on its ability to provide accurate stock predictions with minimal error rates. In the second phase of the project, the chosen model (ARIMA in this case) is further optimized using the Grasshopper Optimization Algorithm (GOA). By applying GOA, the research aims to enhance the predictive capabilities of the ARIMA model and make it more automatic and adaptive. The GOA algorithm assists in defining the order for ARIMA and optimizing its training parameters (AIC and BIC) to reduce the complexity and error rates of the model. The results and discussions from both phases are presented in the research paper to showcase the effectiveness of the proposed approach in improving stock prediction accuracy.

This project's approach combines the strengths of traditional machine learning techniques with modern deep learning algorithms, along with an optimization algorithm, to create a robust and accurate stock prediction model capable of adapting to changing market conditions.

Application Area for Industry

This project can be utilized in various industrial sectors such as finance, investment banking, and stock trading. The proposed solutions can be applied within different industrial domains to address the challenges faced by investors and researchers in accurately predicting stock prices. By utilizing advanced techniques such as machine learning models like ARIMA, NARX, LSTM, and Bi-LSTM, this project aims to develop a highly accurate stock prediction model with reduced computational complexity. The optimization approach using Grasshopper algorithm further enhances the model's performance by automating and adapting it to dynamic stock data, thereby improving prediction accuracy and reducing errors. Implementing these solutions in industries can result in more informed investment decisions, better portfolio management, and increased profitability due to accurate stock price forecasts based on the most up-to-date information available.

Application Area for Academics

The proposed project aims to enrich academic research, education, and training by providing a new and effective stock prediction model based on the ARIMA model. This project is relevant in the field of finance and artificial intelligence, offering potential applications in pursuing innovative research methods, simulations, and data analysis within educational settings. Researchers, MTech students, and PHD scholars in the field of finance and artificial intelligence can use the code and literature of this project to study and implement the proposed stock prediction model in their work. The project covers technologies such as LSTM, BiLSTM, ARIMA, SSM, NARN, and GOA, providing a comprehensive approach to stock price forecasting. The model's ability to optimize the performance of the ARIMA model using the GOA algorithm makes it automatic and adaptive, addressing the challenge of predicting stock prices accurately with reduced computational complexity.

By utilizing real-time datasets and considering both time series and textual data, the proposed model offers a robust solution for forecasting stock prices in dynamic market conditions. The future scope of this project includes further refinement of the stock prediction model, exploring additional optimization techniques, and expanding the dataset to include more companies and time periods. This project has the potential to advance research in the field of stock market prediction and contribute to the development of more reliable and accurate forecasting models.

Algorithms Used

The proposed stock prediction model in this project utilizes several algorithms to enhance accuracy and efficiency. Initially, five classifiers are evaluated on real-time stock data obtained from the Yahoo market: ARIMA, NARX, State Space model, LSTM, and Bi-LSTM. Among these classifiers, ARIMA is identified as the best performer with the lowest error rate and high prediction accuracy. In the second phase, the ARIMA model is further optimized using the Grasshopper Optimization Algorithm (GOA). GOA optimizes the training parameters of ARIMA (e.

g., AIC and BIC) to reduce the dimensionality of the dataset, simplify the model, and improve prediction accuracy. The combination of ARIMA and GOA aims to create an automatic and adaptive stock prediction model that can provide more accurate forecasts.

Keywords

stock prediction, financial institutions, stock market forecasting, machine learning, predictive modeling, financial analysis, time series analysis, algorithmic trading, stock market trends, investment strategies, market volatility, financial forecasting, data analytics, quantitative finance, risk management, ARIMA model, classifiers, ML ARIMA, Nonlinear autoregressive neural network, NARX, state Space model, Deep learning models, Long Short-Term Memory, LSTM, Bidirectional Long Short-Term Memory, Bi-LSTM, Grass Hopper optimization Algorithm, GOA, stock information, Yahoo stock market, error rate, stock prediction accuracy, optimization approach, ARIMA, training parameters, AIC, BIC, dataset dimensionality, complexity reduction.

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