This capstone project is part of the Applied Statistics and Experimental Design course, offered in the 20232 semester at Hanoi University of Science and Technology.
The focus is on forecasting stock market price, specifically examining Apple Inc's stock prices (stock symbol as AAPL on the NASDAQ stock exchange) from 2013 to 2018, through comprehensive Time Series analysis. By applying statistical and machine learning methodologies, the project aims to uncover patterns, decipher the dynamics influencing stock prices, and construct models to predict future movements. These insights are intended to assist investors in making well-informed decisions.
- Data Exploration and Preprocessing: Delve into Apple Inc's historical stock price data, preparing it for analysis.
- Stationarity Analysis: Assess the time series data for stationarity and apply transformations if required.
- Time Series Decomposition: Break down the time series to understand its core components.
- Model Development and Evaluation: Construct and assess the performance of various predictive models, including AR (Autoregressive), MA (Moving Average), ARIMA (Autoregressive Integrated Moving Average), and Prophet, to forecast future stock prices.
- Insight Generation: Analyze the model outcomes to offer actionable insights for investors.
For detailed information, please refer to the Project Report
This project is a collaborative effort by the following students:
| Name | Student ID |
|---|---|
| Chu Trung Anh (Leader) | 20225564 |
| Vu Duc Thang | 20225553 |
| Dao Minh Quang | 20225552 |
| Nguyen Sy Quan | 20225585 |