An Empirical Study on: Time Series Forecasting of Amazon Stock Prices using Neural Networks LSTM and GAN models
Article No.: 136, Pages 1 - 10
Abstract
The OHLCV (Open, High, Low, Close, Volume) data used in this study is used to forecast time series and anticipate stock price movement. We investigate a wide variety of models, including traditional statistical approaches and cutting-edge deep learning strategies combined with sentiment analysis, feature extraction, and hyperparameter tweaking. Instead of focusing on absolute stock prices, our main goal is to predict swings in stock prices, as this has been shown to produce more accurate outcomes. We start our research by obtaining historical Amazon stock data via the Yahoo API, and then we go on a thorough analytical journey. We generate features on the OHLCV data first, and then we design and test Fourier and Autoregressive Integrated Moving Average (ARIMA) models. We then switch to more sophisticated deep learning methods, using the pre-processed data to apply Long Short-Term Memory (LSTM) models. Interestingly, we add sentiment analysis to the LSTM study, which expands its scope and lets us consider market sentiment as a possible influencing factor. To guarantee the stability of our models, we use a careful train-test split technique and organize the data in a time series manner. The field of financial forecasting and trading methods will ultimately benefit from the insightful information our study's findings provide on the efficacy of different modeling techniques and their capacity to forecast stock price movements.
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Published In
November 2023
1215 pages
ISBN:9798400709418
DOI:10.1145/3647444
Copyright © 2023 ACM.
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Association for Computing Machinery
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Publication History
Published: 13 May 2024
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ICIMMI 2023
ICIMMI 2023: International Conference on Information Management & Machine Intelligence
November 23 - 25, 2023
Jaipur, India
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