Sample interview questions: How do you assess the predictive power of your time series forecasting models?
Sample answer:
1. Historical Accuracy:
- Compare the model’s forecasts against actual historical data.
- Calculate metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) to quantify forecast accuracy.
2. Cross-Validation:
- Divide the historical data into training and validation sets.
- Train the model on the training set and evaluate its performance on the validation set.
- Repeat this process several times with different splits of the data to get a robust estimate of the model’s accuracy.
3. Forecast Stability:
- Assess the consistency of the model’s forecasts over time.
- Examine the forecast errors to identify any systematic patterns or biases.
- Employ techniques like Monte Carlo simulations to estimate the variability of the forecasts.
4. Out-of-Sample Testing:
- Hold out a portion of the historical data for out-of-sample testing.
- Train the model on the remaining data and use it to forecast the held-out data.
- Evaluate the model’s performance on the out-of-sample data to assess its ability to generalize to unseen data.
5. Residual Analysis:
- Examine the residuals (the difference between actual values and forecasts) to identify any patterns or anomalies.
- Check for autocorrelation, heteroscedasticity, or non-normality in the residuals.
- Residual analysis can reveal potential weaknesses in the model or suggest areas for improvement.
6. Model Tuning… Read full answer
Source: https://hireabo.com/job/7_4_20/Quantitative%20Analyst%20%28Quant%29