What Can Be Forecasted?
If you have historical data with timestamps, I can build a forecasting model for it. Common use cases include:
- Monthly or weekly sales revenue
- Product demand and inventory needs
- New customer acquisition rates
- Website traffic and conversion trends
- Seasonal patterns and peak periods
- Churn rate prediction
The minimum requirement is at least 12 data points (e.g., 12 months of historical data). More historical data generally means more accurate forecasts.
What You Receive
- A forecasting model built for your specific data
- Forecast values for your chosen time horizon (3, 6, or 12 months)
- Confidence intervals (upper and lower bounds)
- Forecast chart showing historical + predicted trend
- Model accuracy metrics (MAE, RMSE, MAPE)
- Plain-language interpretation of the forecast
- Excel file with all forecast values
Methods Used
- Linear and polynomial regression
- Moving averages and exponential smoothing
- ARIMA / SARIMA (time series modeling)
- Prophet (Facebook's forecasting library)
- Random Forest / XGBoost regression (for complex patterns)
The best method is selected based on your data's characteristics — you don't need to specify a technique.
Process
Data Review
I examine your historical data for quality, completeness, and suitability for forecasting.
Data Preparation
I clean and preprocess the data, handle seasonality, and engineer features if needed.
Model Building & Validation
I test multiple models and select the one with the best accuracy on your historical data.
Forecast & Report Delivery
You receive the forecast values, accuracy report, and a chart — all clearly explained.
Tools Used
Pricing
Pricing scales with the number of variables to forecast and the time horizon required.
- Single metric forecast
- 3-month horizon
- Forecast chart
- Accuracy report
- Up to 5 metrics forecast
- 6-month horizon
- Seasonality analysis
- Confidence intervals
- Multi-variable forecasting
- 12-month horizon
- Multiple model comparison
- Executive summary
- Unlimited variables
- Custom time horizon
- ML-based models
- Python source code