https://github.com/chutrunganh/stock-price-prediction-using-time-series
This repository focuses on predicting Apple Inc.'s stock prices using time series analysis from 2013 to 2018. The project involves data exploration, stationarity analysis, time series decomposition, model development, and generating insights to assist investors.
https://github.com/chutrunganh/stock-price-prediction-using-time-series
ar-model arima auto-regressive-model hust it2022e moving-average prophet stock-market stock-price-prediction time-series-analysis
Last synced: about 2 months ago
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This repository focuses on predicting Apple Inc.'s stock prices using time series analysis from 2013 to 2018. The project involves data exploration, stationarity analysis, time series decomposition, model development, and generating insights to assist investors.
- Host: GitHub
- URL: https://github.com/chutrunganh/stock-price-prediction-using-time-series
- Owner: chutrunganh
- License: mit
- Created: 2024-06-07T15:37:32.000Z (11 months ago)
- Default Branch: master
- Last Pushed: 2024-07-13T18:19:31.000Z (10 months ago)
- Last Synced: 2024-07-14T19:39:34.237Z (10 months ago)
- Topics: ar-model, arima, auto-regressive-model, hust, it2022e, moving-average, prophet, stock-market, stock-price-prediction, time-series-analysis
- Language: Jupyter Notebook
- Homepage:
- Size: 21.4 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Predicting Stock Market Price with Time Series Analysis
## Overview
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.
## Objectives
1. **Data Exploration and Preprocessing:** Delve into Apple Inc's historical stock price data, preparing it for analysis.
2. **Stationarity Analysis:** Assess the time series data for stationarity and apply transformations if required.
3. **Time Series Decomposition:** Break down the time series to understand its core components.
4. **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.
5. **Insight Generation:** Analyze the model outcomes to offer actionable insights for investors.
For detailed information, please refer to the [Project Report](https://github.com/chutrunganh/Stock-Price-Prediction-Using-Time-Series/blob/master/Docs/AppliedStat_Report.pdf)
## Contributors
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 |