Forecasting Netflix Stock Prices by Time Series Analysis: ARIMA - LSTM Hybrid Model

Mingyan Liu ()
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Mingyan Liu: International College of Zhengzhou University, Zhengzhou University

A chapter in Proceedings of the 2025 3rd International Conference on Digital Economy and Management Science (CDEMS 2025), 2025, pp 14-21 from Springer

Abstract: Abstract With the intensifying competition in the streaming media market, the fluctuations in Netflix’s stock price have attracted attention, and accurately predicting its stock price is of great significance to investors. Over the years, many studies have explored the use of time series analysis for stock price forecasting. This study uses the Autoregressive Integrated Moving Average and Long Short-Term Memory (ARIMA-LSTM) hybrid model to predict Netflix’s stock price. It utilizes the stock price data from September 2002 to May 2024. The methodology involves using the ARIMA model to address linear trends, followed by the LSTM model to capture nonlinear characteristics. Findings indicate that this hybrid approach can effectively forecast stock prices. The result is more accurate than the ARIMA model. Future research can introduce more variables and pay attention to market changes to improve the model, providing references for investors and helping them optimize their investment portfolios and formulate risk-hedging strategies.

Keywords: time series analysis; stock price prediction; ARIMA-LSTM model (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-770-0_3

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DOI: 10.2991/978-94-6463-770-0_3

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