castings drafter Interview Questions and Answers

1. What is forecasting?

   • Answer: Forecasting is the process of making predictions about future events based on past data and trends. It involves analyzing historical information, identifying patterns, and extrapolating those patterns into the future to estimate likely outcomes.

2. What are the different types of forecasting methods?

   • Answer: Forecasting methods can be broadly categorized into qualitative (e.g., expert opinion, Delphi method) and quantitative (e.g., time series analysis, causal models, regression analysis). Quantitative methods utilize numerical data, while qualitative methods rely on expert judgment and intuition.

3. Explain time series analysis.

   • Answer: Time series analysis is a statistical technique used to analyze data points collected over time. It identifies trends, seasonality, and cyclical patterns within the data to forecast future values. Common methods include moving averages, exponential smoothing, and ARIMA models.

4. What is exponential smoothing and when is it most useful?

   • Answer: Exponential smoothing is a time series forecasting method that assigns exponentially decreasing weights to older data points. It's particularly useful for data with relatively stable trends and minimal seasonality. Different variations exist, such as single, double, and triple exponential smoothing, each suited for different data characteristics.

5. Describe ARIMA models.

   • Answer: ARIMA (Autoregressive Integrated Moving Average) models are sophisticated time series models that capture autocorrelations in the data. They consist of autoregressive (AR), integrated (I), and moving average (MA) components, each representing different aspects of the data's temporal structure. The model's order (p,d,q) specifies the number of AR, I, and MA terms.

6. What are the key performance indicators (KPIs) used to evaluate forecast accuracy?

   • Answer: Common KPIs include Mean Absolute Deviation (MAD), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Directional Accuracy (MDA). These metrics quantify the difference between actual and forecasted values, providing insights into forecast accuracy.

7. How do you handle outliers in your forecasting data?

   • Answer: Outliers can significantly affect forecast accuracy. Methods to handle them include identifying and removing them if they're due to errors, transforming the data (e.g., logarithmic transformation), or using robust forecasting methods less sensitive to outliers.

8. Explain the concept of seasonality in forecasting.

   • Answer: Seasonality refers to recurring patterns in data that occur at regular intervals, such as daily, weekly, monthly, or yearly. For example, ice cream sales are typically higher in summer. Seasonality needs to be accounted for in forecasting models to improve accuracy.

9. What is forecasting bias? How can you identify and correct it?

   • Answer: Forecasting bias occurs when a forecast consistently overestimates or underestimates the actual values. It can be identified by analyzing the residuals (differences between actual and forecasted values). Corrections involve adjusting the forecasting model or using techniques like bias correction methods.

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