Differences

This shows you the differences between two versions of the page.

--- ai_predictive_forecaster [2025/05/29 15:44] – [Class Overview] eagleeyenebula
+++ ai_predictive_forecaster [2025/05/29 15:50] (current) – [Class Overview] eagleeyenebula
@@ Line 103: / Line 103: @@
 **Error Handling**:
-   * Raises errors if the model is not fitted before calling `forecast()`.
+   * Raises errors if the model is not fitted before calling **forecast()**.
@@ Line 112: / Line 112: @@
 . **Import the Class**:
      * Import the `PredictiveForecaster` module into your script:
-   <code>
+<code>
    python
    from ai_predictive_forecaster import PredictiveForecaster
-   </code>
+</code>
 . **Initialize the Forecaster**:
      * Set ARIMA parameters and instantiate the `PredictiveForecaster`:
-   <code>
+<code>
    python
    forecaster = PredictiveForecaster(model_order=(5, 1, 0))
-   </code>
+</code>
 . **Fit the Model**:
      * Provide time-series historical data to train the model:
-   <code>
+<code>
    python
    historical_data = [100, 110, 125, 130, 120, 150]
    forecaster.fit(historical_data)
-   </code>
+</code>
 . **Forecast Future Values**:
    Specify how many time steps to predict:
-   <code>
+<code>
    python
    future_predictions = forecaster.forecast(steps=5)
    print(f"Forecast: {future_predictions}")
-   </code>
+</code>
 ===== Advanced Examples =====
@@ Line 275: / Line 275: @@
 ===== Conclusion =====
-The **AI Predictive Forecaster** offers a solid framework for time-series forecasting, relying on the proven ARIMA model to provide accurate and adaptable predictions. Its simplistic yet extensible design makes it a powerful tool for data scientists and engineers working with chronological datasets. From sales forecasts to anomaly detection, this flexible utility simplifies the prediction process while ensuring accuracy and reproducibility.
+The **AI Predictive Forecaster** delivers a reliable and intuitive framework for time-series forecasting, enabling users to generate high-quality predictions with ease. Built upon the trusted **ARIMA** model from statsmodels, it allows analysts and engineers to capture seasonality, trends, and noise within chronological datasets. Its clean **API** and modular structure make integration straightforward, whether in stand-alone scripts or as part of larger AI systems, ensuring rapid deployment and iteration.
+Beyond its core functionality, the tool is built with extensibility in mind allowing for customization and experimentation with different parameter sets or even model substitutions. From forecasting sales, demand, and resource utilization to detecting anomalies in system metrics or financial time series, the AI Predictive Forecaster stands as a versatile asset. It bridges statistical rigor with practical usability, empowering teams to make informed, data-driven decisions confidently and consistently.