Metadata-Version: 2.4
Name: ts-programs-gg
Version: 1.0.0
Summary: A collection of 7 time series analysis programs
Home-page: https://github.com/yourusername/ts-programs
Author: Your Name
Author-email: Your Name <your.email@example.com>
License: MIT
Project-URL: Homepage, https://github.com/yourusername/ts-programs
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas>=1.3.0
Requires-Dist: numpy>=1.21.0
Requires-Dist: matplotlib>=3.4.0
Requires-Dist: statsmodels>=0.13.0
Requires-Dist: scikit-learn>=0.24.0
Dynamic: author
Dynamic: home-page
Dynamic: license-file
Dynamic: requires-python

# Time Series Programs Library

A Python library containing 7 time series analysis programs for forecasting and analysis using the AirPassengers dataset.

## Installation

You can install this package using pip:

```bash
pip install .
```

Or for development mode:

```bash
pip install -e .
```

## Requirements

- Python >= 3.7
- pandas >= 1.3.0
- numpy >= 1.21.0
- matplotlib >= 3.4.0
- statsmodels >= 0.13.0
- scikit-learn >= 0.24.0

## Usage

After installation, you can import and run any of the 7 programs:

```python
from ts_programs import p1, p2, p3, p4, p5, p6, p7

# Run program 1: Exponential Smoothing Methods
p1()

# Run program 2: Stationarity Testing
p2()

# Run program 3: Moving Averages & ACF/PACF
p3()

# Run program 4: AutoRegressive Models
p4()

# Run program 5: Moving Average Models
p5()

# Run program 6: ARMA Model
p6()

# Run program 7: ARIMA Model
p7()
```

## Programs Overview

### p1() - Exponential Smoothing Methods
Compares Simple Moving Average (SMA), Simple Exponential Smoothing (SES), and Holt-Winters forecasting methods. Displays metrics (MAE, MSE, RMSE) and visualization plots.

### p2() - Stationarity Testing
Tests for stationarity using Augmented Dickey-Fuller (ADF) and KPSS tests. Compares the original series with white noise.

### p3() - Moving Averages & ACF/PACF Plots
Visualizes moving averages (6-month and 12-month) and autocorrelation functions (ACF and PACF).

### p4() - AutoRegressive (AR) Models
Fits AR models of various orders (1, 2, 3, 5, 10) and evaluates their performance using MAE, MSE, and RMSE.

### p5() - Moving Average (MA) Models
Fits MA models of various orders (1, 2, 3, 5) and evaluates their performance.

### p6() - ARMA Model
Fits an ARMA(2,1) model and generates forecasts for the test period.

### p7() - ARIMA Model
Fits an ARIMA(2,1,2) model and generates future forecasts.

## Data Requirements

All programs expect an `AirPassengers.csv` file in your current working directory with the following structure:
- Column 1: `Month` (date column)
- Column 2: `#Passengers` (numerical values)

You can download the AirPassengers dataset from various sources or use your own time series data with the same format.

## License

MIT License
