https://github.com/chandkund/time-series-forecasting
A time series forecasting project using the Airline Passenger dataset. This project leverages ARIMA modeling to predict future passenger numbers by analyzing historical trends and seasonality from 1949-1960. The results demonstrate effective forecasting for better business planning.
https://github.com/chandkund/time-series-forecasting
arima-forecasting arima-model data-science numpy pandas python seaborn
Last synced: 3 months ago
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A time series forecasting project using the Airline Passenger dataset. This project leverages ARIMA modeling to predict future passenger numbers by analyzing historical trends and seasonality from 1949-1960. The results demonstrate effective forecasting for better business planning.
- Host: GitHub
- URL: https://github.com/chandkund/time-series-forecasting
- Owner: chandkund
- Created: 2024-08-21T12:48:58.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-21T13:38:06.000Z (almost 2 years ago)
- Last Synced: 2025-09-03T01:38:04.336Z (10 months ago)
- Topics: arima-forecasting, arima-model, data-science, numpy, pandas, python, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 135 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Time Series Forecasting Using Airline Passenger Data
This project focuses on forecasting future airline passenger numbers using historical data. The dataset records monthly passenger counts from 1949 to 1960, and the goal is to predict future values by analyzing trends and seasonality.
## Project Overview
The main objectives of this project are:
- **Data Exploration**: Analyzing the data to identify trends, seasonality, and patterns.
- **Modeling**: Building an ARIMA model to forecast future passenger numbers.
- **Evaluation**: Assessing the model's accuracy and visualizing the forecast.
## Dataset
The dataset used in this project is the **Airline Passenger** dataset, which contains the following columns:
- `Month`: Date of observation (in `YYYY-MM` format).
- `Passengers`: Number of passengers (in thousands).
### Data Source
The dataset is publicly available and can be found [here](https://github.com/jbrownlee/Datasets/blob/master/airline-passengers.csv).
## Installation
To run this project, you need Python 3.x installed along with the following packages:
```bash
pip install pandas numpy matplotlib statsmodels
```
## Usage
1. **Clone the repository**:
```bash
git clone https://github.com/yourusername/airline-passenger-forecasting.git
cd airline-passenger-forecasting
```
2. **Run the Jupyter Notebook**:
```bash
jupyter notebook Time_Series_Forecasting.ipynb
```
## Methodology
### 1. Data Exploration
- Visualized the time series data to identify trends and seasonality.
- Checked for stationarity using the Augmented Dickey-Fuller (ADF) test.
### 2. Data Preprocessing
- Applied log transformation and differencing to make the data stationary.
### 3. Modeling
- Built an ARIMA model to fit the stationary data.
- Used the AIC (Akaike Information Criterion) to optimize the model parameters.
### 4. Forecasting
- Forecasted future passenger numbers for the next 1 months.
- Visualized the forecast and compared it with the original data.
## Results
The ARIMA model successfully captured the trend and seasonality in the data, providing accurate forecasts for future passenger numbers. These forecasts can be useful for airlines in planning operations and resources.
## Conclusion
This project highlights the effectiveness of ARIMA models in time series forecasting. By understanding past trends and patterns, businesses can make informed decisions for future planning.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
### Explanation:
- **Project Overview**: Summarizes the goals and the dataset.
- **Dataset**: Briefly describes the dataset and provides a link to the source.
- **Installation**: Provides commands to install the necessary Python packages.
- **Usage**: Instructions to clone the repository and run the notebook.
- **Methodology**: Explains the steps taken in the project, from data exploration to forecasting.
- **Results**: Summarizes the outcome of the model.
- **Conclusion**: Highlights the significance of the project.
- **License**: Mentions the project's license.