aiforecastts 0.1.2

AI-powered Time Series Forecasting Library

AIForecastTS

This project is part of AI Forecast at aiconsultant.org. AIForecastTS is a practical Python library for time series analysis and forecasting — it includes classic utilities (moving average, seasonal decomposition, stationarity testing, ARIMA) and a higher-level ensemble forecaster (SuperForecaster) that combines Prophet, AutoARIMA, and XGBoost.

Features

• Time series exploration utilities (moving averages, descriptive statistics)
• Seasonal decomposition (trend / seasonal / residual)
• Stationarity tests (ADF)
• ARIMA forecasting utilities
• SuperForecaster: an ensemble that trains Prophet, AutoARIMA and XGBoost with feature engineering (lags, rolling stats, RSI, MACD, Bollinger bands)

Installation

pip install aiforecastts

Quick Start

import pandas as pd
from aiforecastts import TimeSeriesAnalyzer, SuperForecaster

# Sample data
data = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], index=pd.date_range('2020-01-01', periods=10))

analyzer = TimeSeriesAnalyzer(data)
print(analyzer.moving_average(3))          # MA
print(analyzer.forecast_arima(steps=5))    # ARIMA
print(analyzer.is_stationary())            # ADF

# SuperForecaster (ensemble Prophet + AutoARIMA + XGBoost)
series = pd.Series(range(1, 121), index=pd.date_range('2020-01-01', periods=120))
forecaster = SuperForecaster(series)
metrics = forecaster.fit_ensemble(train_size=0.8)
print(metrics['mae'])
print(forecaster.predict(steps=7))

Important: This library does not fetch market data automatically. Please provide your own time series (CSV files, data APIs or a DataFrame) when using TimeSeriesAnalyzer or SuperForecaster.

Development

git clone https://github.com/tuanthescientist/aiforecastts
cd aiforecastts
pip install -e .[dev]
python -m unittest discover -v tests
ruff check . --fix
black .

Build & Publish

python -m build
twine upload dist/*

Contact / project

• Repository: https://github.com/tuanthescientist/aiforecastts
• Project: https://aiconsultant.org (AI Forecast)