Copyright © 2023 The Author(s): This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY-NC 4.0) which permits unrestricted use, distribution, and reproduction in any medium for non-commercial use provided the original author and source are credited. International Journal of Scientific Research in Computer Science, Engineering and Information Technology ISSN : 2456-3307 (www.ijsrcseit.com) doi : https://doi.org/10.32628/CSEIT23903112 455 Movie Recommendation Based System Using Time Series Data Ayush Sachdev 1 , Ashutosh Naik 1 , Advin Manhar 2 1 Student, Amity University Chhattisgarh, India 2 Assistant Professor, Amity University Chhattisgarh, India A R T I C L E I N F O A B S T R A C T Article History: Accepted: 01 June 2023 Published: 12 June 2023 Finding the right movie from a wide selection can be difficult, leading to frustration and wasted time. Recommendation systems offer a solution by providing personalized movie recommendations based on users' interests and preferences. These systems use data analytics, machine learning algorithms and temporal analysis techniques to understand user behavior and provide accurate recommendations. Collaborative filtering algorithms identify similarities between users or movies, while content-based filtering separates movie features based on user preferences. Time series analysis methods collect temporal patterns for dynamic recommendations. The results of the literature review support the effectiveness of movie recommendation systems based on time series data, showing their ability to provide accurate recommendations despite changing information and changing preferences. Real-time data collection improves system efficiency. Overall, the proposed solution aims to improve the movie selection process, save users time and effort, and at the same time improve the movie viewing experience. Keywords : Recommendation Based System, Collaborative filtering, Time Series Data, Content Based filtering, Sentiment analysis, Multifractal Detrended Mobility Cross-Correlation Analysis, Recurrent Neural Network Publication Issue Volume 9, Issue 3 May-June-2023 Page Number 455-458 I. INTRODUCTION Scope of Problem Finding a movie that suits one's preferences can be a daunting task, especially with the vast pool of options available. It can be a time-consuming and frustrating task - browsing through numerous movies across different genres, checking the ratings and comments, only to end up with a disappointing choice. Providing Solutions By offering individualised ideas that are catered to specific user interests, recommendation systems have become an effective means of addressing this problem. These data science-based solutions give precise and pertinent suggestions that improve user experience and contribute to company success by utilising large-scale data analysis and machine learning approaches. Massive volumes of user data have been produced by the growth of online platforms, e-commerce sites,