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- W4385202555 abstract "Stocks are typically linked with companies that have commercialized operations and are establishing themselves in the business world. They can also be described as shares, a term that is frequently used in everyday language. Stocks are often viewed as a long-term investment strategy that provides a secure source of income during retirement. Within the realm of quantitative trading, accurately forecasting future security returns for stocks is a fundamental aspect of the industry, as trading strategies are developed and implemented based on our outlook of the financial market. However, conventional financial time series prediction techniques solely rely on price and volume to predict future prices. With the recent volatility of the stock market due to the COVID-19 pandemic, utilizing the time series prediction using RNN and ANN models using LSTM and Seq2Seq2 LSTM helps us analyze and visualize the stock volatility and risk associated with the stock and gain the maximum return with low risk without having to involve the human emotion." @default.
- W4385202555 created "2023-07-25" @default.
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- W4385202555 date "2023-01-01" @default.
- W4385202555 modified "2023-10-16" @default.
- W4385202555 title "Stock Price Analysis and Prediction Using Seq2Seq LSTM" @default.
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- W4385202555 doi "https://doi.org/10.1007/978-981-99-3878-0_56" @default.
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