Metadata-Version: 2.2
Name: superforecast
Version: 0.1.4
Summary: Super Forecast - A modern forecasting API client powered by bio-inspired AI models
Author: San Martim
Author-email: San Martim <superforecast@dataspoc.com>
License: MIT License
        
        Copyright (c) 2024 San Martim
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
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Keywords: forecast,forecasting,AI models,timeseries,demand forecasting,energy forecasting,financial market forecasting
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: requests
Dynamic: author

# 🧠 SuperForecast Client  

**A modern forecasting API client powered by bio-inspired AI models.**  

## 📌 Overview  

AI Forecast Client is a powerful Python package that enables users to interact with an advanced forecasting API. The underlying AI models are inspired by the way the human brain processes information, leveraging neural dynamics to achieve high-accuracy predictions with adaptive learning capabilities.  

## 🚀 Features  

- **Bio-inspired AI Models** – Forecasting algorithms based on human brain mechanisms.  
- **Historical Data Utilization** – Incorporates past trends for robust future predictions.  
- **Configurable Forecasting Methods** – Choose from multiple forecasting strategies.  
- **Segmented Predictions** – Supports advanced fragments-based forecasting.  
- **Flexible API Integration** – Simple and efficient API request handling.  

## 📦 Installation  

Install via `pip`:  

```bash
pip install superforecast
```

## 🔧 Usage  

```python
from superforecast import SuperForecast

# Initialize the client
sp = SuperForecast()

# Request a forecast
forecast = sp.forecast(
    history=[...],  # Your time-series data
    generalization=1,
    steps=10
)

print(forecast)
```

## ⚙️ API Parameters  

| Parameter       | Description                                              | Default | Max (Free Version) |
|----------------|----------------------------------------------------------|---------|--------------------|
| `history`      | Time-series historical data for training                 | N/A     | 360                |
| `generalization` | Generalization level for the forecast                 | 1       | -                  |
| `steps`        | Number of forecast steps                                 | 10      | 10                 |
| `ret_all_series` | Return all series or only forecasted steps            | True    | -                  |
| `ysmooth`      | Apply smoothing to forecast values                       | False   | -                  |
| `method`       | Forecasting method (1 to 7)                              | 1~7       |                  |
| `segments`     | Number of fragments in the forecast                      | 50      | 50                 |

## 📡 API Response  

A typical API response returns a dictionary with predicted values:

```json
{
    "forecast": [123.4, 125.6, 127.8, ...],
    "confidence_intervals": [[120.1, 126.7], [123.5, 129.3], ...]
}
```

## 📖 Documentation  

For detailed API documentation and advanced configurations, visit:  
🔗 [Official Documentation](https://api.superforecast.dev/docs)  

## 🎯 Use Cases  

- **Financial Market Prediction**  
- **Demand Forecasting**  
- **Sensor Data Analysis**  
- **Energy Consumption Prediction**  

## 📬 Support  

For issues, open an issue on [GitHub](https://github.com/dataspoclab/superforecast) or contact support at superforecast@dataspoc.com.  

## 📜 License  

This project is licensed under the MIT License.  

