https://github.com/priyanshu501/timeseries_analysis
This repository contains a comprehensive analysis of time series data (stock prices), forecasted using various statistical and deep learning models.
https://github.com/priyanshu501/timeseries_analysis
arima-model exploratory-data-analysis exponential-smoothing garch-model gru-model jupyter-notebook lstm-model machine-learning prophet-model python sarima-model sklearn statsmodels stock-prediction stock-price-prediction timeseries timeseries-forecasting
Last synced: 2 months ago
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This repository contains a comprehensive analysis of time series data (stock prices), forecasted using various statistical and deep learning models.
- Host: GitHub
- URL: https://github.com/priyanshu501/timeseries_analysis
- Owner: Priyanshu501
- License: mit
- Created: 2024-07-14T04:39:06.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-07-18T19:58:30.000Z (11 months ago)
- Last Synced: 2025-01-24T17:45:41.164Z (4 months ago)
- Topics: arima-model, exploratory-data-analysis, exponential-smoothing, garch-model, gru-model, jupyter-notebook, lstm-model, machine-learning, prophet-model, python, sarima-model, sklearn, statsmodels, stock-prediction, stock-price-prediction, timeseries, timeseries-forecasting
- Language: Jupyter Notebook
- Homepage:
- Size: 1.39 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Introduction
This project provides a comprehensive analysis and forecast of Apple Stock Prices, utilizing historical data and advanced statistical models and neural networks.
## Objective
Primary objective is to forecast the following 30 days of Stock Prices. To achieve this, a variety of statistical and deep learning models are implemented. Each model is briefly explained in individual notebooks, providing insights into their methodologies, strengths, and limitations.
## Analysis and Models Implemented
### Exploratory Data Analysis (EDA):
A thorough exploration of historical stock prices, examining trends, seasonality, and other time series characteristics. This analysis sets the foundation for building robust forecasting models.
### Forecasting Models:
#### Statistical Models:
* **ARIMA (AutoRegressive Integrated Moving Average)**: A popular statistical method for time series forecasting.
* **SARIMA (Seasonal AutoRegressive Integrated Moving Average)**: It is a powerful extension of ARIMA that captures seasonality in time series data.
* **Exponential Smoothing**: Methods like Holt-Winters to captures trends and seasonality.
* **Prophet**: Prophet is an open-source tool from Facebook used for forecasting time series data, based on a decomposable additive model where non-linear trends fit with seasonality.
* **GARCH**: The GARCH model effectively captures the volatility clustering observed in financial time series data.
#### Deep Learning Models:
* **LSTM (Long Short-Term Memory)**: A type of recurrent neural network (RNN) well-suited for sequential data.
* **GRU (Gated Recurrent Unit)**: Another RNN variant that is simpler and faster than LSTM.
## Summary
This project integrates both traditional statistical techniques and advanced deep learning models to forecast AAPL stock prices, providing a detailed and multifaceted approach. By leveraging a comprehensive exploratory data analysis and implementing a variety of models, the project aims to enhance predictive accuracy and offer valuable insights into stock price movements. Each model is thoroughly documented, ensuring transparency and understanding of their application and performance in forecasting future stock prices.