Abstract
In the marketplace, the stock is a noticeable channel of investment. Learning how to invest effectively takes lots of time for new investors. In this scenario, there are various methods, from conventional statistical methods like ARIMA to advance deep learning models such as LSTM, TCN, and Seq2Seq. However, facilitating state-of-the-art models requires many hardware commodities, especially on a big scale. Therefore, the BigDL framework recommends robust APIs to conduct plentiful deep learning models, including time series associated issues, one of the first available frameworks provided on BigDL open source. Below are several stocks, including Apple, Amazon, Google, and Microsoft, which we use to evaluate the new BigDL framework for addressing time series forecasting.
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This research was supported by The VNUHCM-University of Information Technology’s Scientific Research Support Fund.
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Ha, NC., Duong, QL., Do, TH. (2023). Stock Price Forecasting on BigDL - A Parallel and Distributed Framework. In: Braubach, L., Jander, K., Bădică, C. (eds) Intelligent Distributed Computing XV. IDC 2022. Studies in Computational Intelligence, vol 1089. Springer, Cham. https://doi.org/10.1007/978-3-031-29104-3_11
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DOI: https://doi.org/10.1007/978-3-031-29104-3_11
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