Metadata-Version: 2.4
Name: SeasonalImpute
Version: 0.1.0
Summary: A Python package for imputing missing values in time series data using seasonal weighted averages.
Project-URL: Homepage, https://github.com/hanifkia/SeasonalImpute
Project-URL: Repository, https://github.com/hanifkia/SeasonalImpute.git
Project-URL: Issues, https://github.com/hanifkia/SeasonalImpute/issues
Author-email: Hanif Kia <kia.hanif@gmail.com>
License: MIT License
        
        Copyright (c) 2025 Hanif Kia
        
        Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
License-File: LICENSE
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.8
Requires-Dist: gluonts>=0.12
Requires-Dist: numpy>=1.20
Description-Content-Type: text/markdown

# SeasonalImpute

A Python package for imputing missing values in time series data using a seasonal weighted average approach.

## Installation

```bash
pip install SeasonalImpute
```

## Usage

```python
import numpy as np
from SeasonalImpute import SeasonalWeightedAverageImputation

# Example data
data = np.array([1.0, np.nan, 3.0, 1.0, np.nan, 3.0])

# Impute with seasonality
imputer = SeasonalWeightedAverageImputation(window=3, seasonality={2: 0.5})
imputed_data = imputer(data)
print(imputed_data)
```

## Features

- Imputes missing values using nearby values and seasonal patterns.
- Customizable window size and seasonal weights.
- Built on `gluonts` and `numpy` for robust time series handling.

## Development

To contribute:

1. Clone the repository:

   ```bash
   git clone https://github.com/hanifkia/SeasonalImpute.git
   ```

2. Install dependencies:

   ```bash
   pip install -e .[dev]
   ```

3. Run tests:

   ```bash
   pytest
   ```

## License

MIT License. See [LICENSE](LICENSE) for details.
