https://github.com/zrkhadija/data-analysis-for-financial-time-series

In this notebook, we performed data analysis on financial time series data from Yahoo Finance for the US market. We examined seasonality, trends, stationarity, and other aspects such as outliers and correlations.
https://github.com/zrkhadija/data-analysis-for-financial-time-series

autocorrelation correlation-analysis data-analysis financial-analysis time-series-analysis timeseries-forecasting visualization

Last synced: about 1 month ago
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In this notebook, we performed data analysis on financial time series data from Yahoo Finance for the US market. We examined seasonality, trends, stationarity, and other aspects such as outliers and correlations.

• Host: GitHub
• URL: https://github.com/zrkhadija/data-analysis-for-financial-time-series
• Owner: zrkhadija
• Created: 2024-08-28T10:50:03.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-11-21T14:50:49.000Z (2 months ago)
• Last Synced: 2024-11-21T15:35:38.593Z (2 months ago)
• Topics: autocorrelation, correlation-analysis, data-analysis, financial-analysis, time-series-analysis, timeseries-forecasting, visualization
• Language: Jupyter Notebook
• Homepage:
• Size: 470 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 💰 Financial Data Analysis: US Market Study

This project explores financial data from the US market using data fetched from Yahoo Finance. It involves analyzing feature correlations, autocorrelation, stationarity, and other key characteristics to gain deeper insights into financial trends and patterns.

## 📊 Features

- **📈 Correlation Analysis**: Explore the relationships between various financial features.

- **📈🔄 Autocorrelation**: Study the dependency of financial time series on their past values.

- **📈📉 Stationarity Analysis**: Perform stationarity tests (e.g., ADF Test) to ensure suitability for time series modeling.

- **📈🔍 Exploratory Data Analysis (EDA)**: Gain insights into financial data trends, volatility, and seasonality.

## 📄 Dataset Description

- **📂 Data Source**: Yahoo Finance API.

- **🔢 Contains historical financial data**

  ## 🛠️ Technologies Used

- **🐍 Python**: For scripting and analysis.

- **📊 pandas**: For data manipulation and processing.

- **📉 statsmodels**: For time series analysis (e.g., autocorrelation and stationarity testing).

- **🎨 Matplotlib/Seaborn**: For visualizing trends, correlations, and patterns.