🔍 1. Ensure High-Quality Data

  • Use clean, relevant, and up-to-date datasets
  • Check for missing values and outliers
  • Normalize data to avoid bias
Data_Quality

🧠 2. Choose the Right Model

  • Select models based on data characteristics (e.g., ARIMA for time series, ML for complex patterns)
  • Compare models using metrics like MAE, RMSE, or R²
  • Avoid overfitting by using cross-validation
Model_Selection

🧪 3. Validate and Test Predictions

  • Split data into training, validation, and test sets
  • Use backtesting to evaluate historical performance
  • Monitor model accuracy over time
Validation_Methods

📚 4. Document Your Process

  • Record assumptions, parameters, and results
  • Share methodologies with stakeholders
  • Update documentation as models evolve
Documentation_Process

For advanced techniques, visit our guide on /en/forecasting-techniques to explore machine learning and statistical modeling methods.