Forecasting Netflix Subscription Growth: A Time Series Analysis using ARIMA and LSTM Models

Manasvi Gade

Title:
Forecasting Netflix Subscription Growth: A Time Series Analysis using ARIMA and LSTM Models

Authors:
Manasvi Gade

Cite This Article :

Manasvi Gade "Forecasting Netflix Subscription Growth: A Time Series Analysis using ARIMA and LSTM Models" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Advancements and Emerging Trends in Computer Applications - Innovations, Challenges, and Future Prospects, March 2025, pp.1614-1620, URL: https://www.ijtsrd.com/papers/ijtsrd81078.pdf

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Abstract :
The rapid growth of digital streaming platforms has transformed the entertainment industry, with Netflix leading the market. This study analyzes historical Netflix subscription data to identify trends, seasonality, and patterns affecting subscriber growth. We preprocess the data, handle missing values, and perform exploratory data analysis (EDA) to understand its characteristics.To forecast future subscriptions, we implement and compare ARIMA and LSTM models. Both models are trained on historical data and evaluated using RMSE and MAPE for accuracy. The analysis reveals seasonal patterns and growth spikes, offering insights into future subscription trends. Based on these predictions, we provide data-driven recommendations to optimize business strategies and maintain market leadership.

Keywords :
Netflix, Time Series Forecasting, ARIMA, LSTM, Subscription Growth, Data Analysis, Predictive Modeling

Publication Details:

Unique Identification Number : IJTSRD81078

Published In : Special Issue | Advancements and Emerging Trends in Computer Applications - Innovations, Challenges, and Future Prospects, March 2025

Page Number(s) : 1614-1620

Publisher Name : IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470

Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

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