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

DOI

10.2991/978-94-6463-770-0_3How to use a DOI?

Keywords

time series analysis; stock price prediction; ARIMA-LSTM model

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.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Mingyan Liu
PY  - 2025
DA  - 2025/06/26
TI  - Forecasting Netflix Stock Prices by Time Series Analysis: ARIMA - LSTM Hybrid Model
BT  - Proceedings of the 2025 3rd International Conference on Digital Economy and Management Science (CDEMS 2025)
PB  - Atlantis Press
SP  - 14
EP  - 21
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-770-0_3
DO  - 10.2991/978-94-6463-770-0_3
ID  - Liu2025
ER  -