Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/140016
Title: Stock prediction and trading using long-short-term memory neural networks
Authors: R Nishitha
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Publisher: Nanyang Technological University
Abstract: In this project, I applied the Long Short Term Memory (LSTM) model to predict the future stock price Zoetis (ZTS). I first developed an LSTM algorithm to predict the stock prices. Subsequently, the original model was then subjected to five variations to determine which variation of the model gives the most accurate result of the future stock price. To simplify the experiment, I have taken into consideration closing price of the Zoetis stock. From the experiment, it was found that the model with 300 epochs, 1 layer and 256 nodes in each layer has the best result , followed by the model with 100 epochs, 1 layer and 256 nodes each layer. The model with 250 epochs, 4 layers, 256 epochs and with dropouts has the worst performance.
URI: https://hdl.handle.net/10356/140016
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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