https://github.com/alihassanml/timeseries
ARIMA & SARIMAX | flights |Time Series Analysis
https://github.com/alihassanml/timeseries
Last synced: 2 months ago
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ARIMA & SARIMAX | flights |Time Series Analysis
- Host: GitHub
- URL: https://github.com/alihassanml/timeseries
- Owner: alihassanml
- Created: 2024-04-23T12:22:31.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-01T08:24:36.000Z (over 1 year ago)
- Last Synced: 2025-02-21T11:27:01.833Z (8 months ago)
- Language: Jupyter Notebook
- Size: 547 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# ARIMA-SARIMAX-flights-Time-Series-Analysis
This repository contains a machine learning project focused on time series analysis using the Seaborn dataset *flights*. The project primarily utilizes ARIMA (AutoRegressive Integrated Moving Average) and SARIMAX (Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors) models for forecasting and analyzing the flight data.
## Dataset Description
The *flights* dataset is a classic dataset available in the Seaborn library, containing information about the number of passengers on monthly flights over a period of several years.
## Project Structure
1. **Data Exploration**: Initial exploration of the dataset to understand its structure, trends, and seasonality.
2. **Preprocessing**: Data preprocessing steps including handling missing values, converting data types, and preparing the data for modeling.
3. **Modeling**: Implementation of ARIMA and SARIMAX models for time series forecasting. This includes model training, evaluation, and hyperparameter tuning.
4. **Evaluation**: Evaluation of model performance using appropriate metrics and visualization techniques.
5. **Deployment**: Instructions on how to deploy and use the trained models for forecasting future flight passenger numbers.
## Requirements
- Python 3.x
- Jupyter Notebook
- Required Python libraries: pandas, numpy, matplotlib, seaborn, statsmodels
## Usage
To run the project, follow these steps:
1. Clone this repository to your local machine.
2. Install the required Python libraries using `pip install -r requirements.txt`.
3. Open and run the Jupyter Notebook files in sequential order: `Data_Exploration.ipynb`, `Preprocessing.ipynb`, `Modeling.ipynb`, `Evaluation.ipynb`.
4. Follow the instructions provided in the notebooks for each section.
5. Deploy the trained models for forecasting as per the instructions provided in the deployment section.
## Contributors
- (https://github.com/alihassanml)
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
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**Description:**
Time Series Analysis with ARIMA and SARIMAX models on Seaborn's flights dataset. This project aims to forecast the number of passengers on monthly flights using two popular time series modeling techniques: ARIMA and SARIMAX. The *flights* dataset from Seaborn is utilized, providing historical data on monthly passenger counts over a span of several years. Through data exploration, preprocessing, model training, and evaluation, this project demonstrates the effectiveness of ARIMA and SARIMAX models in analyzing and predicting time series data. The trained models can be deployed for forecasting future flight passenger numbers, aiding in decision-making and planning for airlines and related industries.