How do you assess the predictive power of your time series forecasting models?

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

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