Multivariate Time Series Prediction for APPL Stock Price Using Big Data Techniques

Description

This project embarks on a comprehensive exploration aimed at understanding the complexities of Apple Inc.’s market dynamics using a combination of advanced financial analytics, machine learning, and a robust Big Data Pipeline. The project focuses on analyzing and forecasting APPL stock prices using sophisticated Multivariate Time Series Prediction techniques.

Leveraging Apache Spark for large-scale data processing and TensorFlow for predictive modeling, the project integrates historical stock price analysis with sentiment analysis from Reddit to capture public perceptions of Apple’s product offerings. This dual-layered approach adds qualitative depth to the quantitative forecasting.

Key Features of the Project

• Multivariate Time Series Modeling: Employs multiple correlated variables, such as trading volume, market indicators, and social media sentiment, for precise forecasting of stock prices.
• Big Data Pipeline: Includes data collection from APIs and web scraping, data transformation with Apache Spark, storage in a distributed file system, and visualization with tools like Tableau.
• Sentiment Analysis: Uses Natural Language Processing (NLP) techniques to extract sentiments from Reddit discussions, providing insights into consumer perceptions.
• Distributed Processing: Processes massive datasets efficiently using Spark’s distributed architecture, ensuring scalability and performance.
• Advanced Machine Learning Models: Implements LSTMs (Long Short-Term Memory networks) and GRUs (Gated Recurrent Units) for temporal data analysis, capturing complex patterns and trends.
• Visualization: Offers interactive dashboards for stakeholders to understand stock performance and sentiment trends over time.