aiforecastts 0.3.2

Scientific AI-powered Time Series Research Library

AIForecastTS: A Scientific Framework for Time Series Resonance & Turbulence Analysis

1. Abstract

AIForecastTS is a high-performance scientific library designed to bridge the gap between classical signal processing and modern machine learning. At its core, the library introduces the Harmonic-Gradient Resonance (HGR) algorithm, a novel approach that mathematically decomposes time series into deterministic physical components (Resonance) and stochastic dynamical components (Turbulence). Integrated with Gemini 3 Flash Preview, it provides an automated AI Research Agent capable of interpreting complex temporal patterns.

2. Theoretical Framework: The HGR Algorithm

Traditional forecasting often fails by treating signal and noise as a monolithic entity. HGR operates on the principle of Dual-Component Decomposition:

2.1. Resonance Module (Deterministic Physics)

The Resonance module assumes that every time series contains underlying periodicities driven by systemic cycles.

• Spectral Identification: Uses Fast Fourier Transform (FFT) to map the series into the frequency domain.
• Harmonic Regression: Selects the top $K$ dominant frequencies and reconstructs the signal using a basis of sine and cosine functions: $$y_{res}(t) = \sum_{i=1}^{K} [a_i \sin(\omega_i t) + b_i \cos(\omega_i t)]$$
• Purpose: Captures long-term trends and seasonal cycles with mathematical precision.

2.2. Turbulence Module (Stochastic Dynamics)

The residuals from the Resonance module ($y - y_{res}$) represent "Turbulence" — the chaotic, non-linear interactions of the system.

• Temporally-Weighted Gradient Boosting: Employs a modified XGBoost architecture where training samples are weighted by their temporal proximity: $$W(t) = \alpha + \beta \cdot \frac{t}{T}$$
• Recursive Stochastic Mapping: Models the short-term dependencies and volatility clusters within the noise.

3. Architecture & Modules

3.1. Advanced Analytics (aiforecastts.analytics)

• DataProcessor: Implements time-aware interpolation and rigorous stationarity testing (Augmented Dickey-Fuller).
• FeatureEngineer: Automated generation of high-dimensional feature spaces, including multi-scale lags and technical indicators (RSI, MACD, Bollinger Bands).

3.2. AI Research Agent (aiforecastts.agents)

Powered by Gemini 3 Flash Preview, the agent acts as an automated peer-reviewer. It analyzes the mathematical outputs of the HGR algorithm and generates a scientific synthesis, explaining the interaction between systemic resonance and market turbulence.

4. Installation & Setup

pip install aiforecastts

5. Scientific Workflow Example

import pandas as pd
from aiforecastts import TimeSeriesResearch

# Load dataset (e.g., Financial or Sensor data)
df = pd.read_csv("data.csv", index_col='timestamp', parse_dates=True)

# Initialize the Research Pipeline
research = TimeSeriesResearch(df, target_col='target')

# Execute Full Analysis with Gemini 3 Flash Agent
results = research.run_full_analysis(
    agent_query="Analyze the resonance-turbulence interaction and assess forecast stability.",
    api_key="YOUR_GEMINI_API_KEY"
)

# Access Scientific Report
print(results['agent_report'])

# Access Mathematical Forecast
forecast_df = results['forecast']
print(forecast_df[['resonance', 'turbulence', 'hgr_forecast']])

6. Future Research Directions

• Integration of Quantum-inspired optimization for frequency selection.
• Support for Multi-variate HGR (MV-HGR) to capture cross-series correlations.
• Real-time streaming analytics for high-frequency turbulence monitoring.

7. Citation

If you use this library in your research, please cite it as:

Tuan, T. A. (2025). AIForecastTS: A Scientific Framework for Time Series Resonance & Turbulence Analysis. GitHub Repository.

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Developed by Tran Anh Tuan - (AI Forecast) aiconsultant.org