forecast_prophet_model
Forecast Indian stock prices with Prophet by inputting a ticker and forecast horizon. Get confidence intervals, seasonality detection, and visual charts for data-driven decisions.
Instructions
Train Prophet model and generate forecasts with confidence intervals.
Provides:
Automatic seasonality detection (yearly, weekly patterns)
Trend changepoint identification
Holiday effects support (Indian market holidays)
Component decomposition (trend + seasonality)
Multi-period forecasting with confidence bands
Model validation and quality checks
Visual forecast charts with historical data
Args: ticker: Stock ticker symbol (e.g., 'RELIANCE', 'TCS', 'INFY') periods: Number of periods to forecast (default: 20 trading days) confidence: Confidence interval level (0.8-0.99, default: 0.95) period: Time period for training data ('1mo', '3mo', '6mo', '1y', '2y', '5y') yearly_seasonality: Enable yearly seasonality (default True) weekly_seasonality: Enable weekly seasonality (default True) seasonality_mode: 'additive' or 'multiplicative' (default 'additive') changepoint_prior_scale: Flexibility of trend changes (default 0.05) seasonality_prior_scale: Flexibility of seasonality (default 10.0) holidays_prior_scale: Flexibility of holiday effects (default 10.0) validation_split: Train-validation split ratio (default 0.2) include_holidays: Include Indian market holidays (default False)
Returns: List containing text analysis and ImageContent with forecast plot
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| period | No | 1y | |
| ticker | Yes | ||
| periods | No | ||
| confidence | No | ||
| include_holidays | No | ||
| seasonality_mode | No | additive | |
| validation_split | No | ||
| weekly_seasonality | No | ||
| yearly_seasonality | No | ||
| holidays_prior_scale | No | ||
| changepoint_prior_scale | No | ||
| seasonality_prior_scale | No |