forecasting-time-series-data

Enables time series forecasting with model selection and confidence interval generation.

• Performs time series forecasting and trend analysis.
• Identifies trends, seasonality, and autocorrelation.
• Selects and trains models like ARIMA or Prophet.
• Generates predictions with confidence intervals.
• Integrates with data analysis and visualization tools.

• Analyzes historical data for trends and seasonality.
• Selects optimal forecasting models automatically.
• Generates future predictions with confidence intervals.
• Supports common use cases like sales and traffic forecasting.
• Can be integrated with other data tools.
• Requires clean, complete, and accurate data.
• Must evaluate model performance with metrics like MAE or RMSE.

1. 1.Provide time series data with clear time stamps.
2. 2.Describe the forecast period or horizon.
3. 3.Specify desired output format or metrics.
4. 4.Ensure data quality and completeness.
5. 5.Choose appropriate model type based on data characteristics.
6. 6.Validate results using error metrics like MAE or RMSE.