Exponential Smoothing
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Machine Learning
Time Series Analysis and Forecasting
Interview questions on Time Series Analysis and Forecasting.
18 questions
All subtopicsIntroductionTime Series GraphicsTime Series DecompositionBenchmark Forecasting MethodsTime Series Regression ModelsExponential SmoothingARIMA ModelsDynamic regression modelsProphet ModelVector Autoregressions
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Exponential Smoothing
Q2. What is simple exponential smoothing method?
Exponential Smoothing
Q3. What occurs in simple exponential smoothing when \(\alpha = 1\)?
Exponential Smoothing
Q4. Write the component form of simple exponential smoothing?
Exponential Smoothing
Q5. Why can’t we use the exponential smoothing method for data with seasonality and trend?
Exponential Smoothing
Q6. How does Holt's linear trend method work?
Exponential Smoothing
Q7. What is the main issue with Holt's linear trend method?
Exponential Smoothing
Q8. Explain working of damped trend methods?
Exponential Smoothing
Q9. What is Holt-Winter's method?
Exponential Smoothing
Q10. In the damped Holt-Winters method with multiplicative seasonality, what role does the parameter \( \phi \) play, and what would be the effect on the trend if \( \phi = 1 \) versus \( \phi < 1 \)?
Exponential Smoothing
Q11. What are state space models in the context of time series analysis?
Exponential Smoothing
Q12. What is difference between methods and models?
Exponential Smoothing
Q13. What is the forecast error in simple exponential smoothing model?
Exponential Smoothing
Q14. What is the forecast error in simple exponential smoothing model? (Part 2)
Exponential Smoothing
Q15. Write the expression for SES with additive errors?
Exponential Smoothing
Q16. For an additive error model, maximising the likelihood (assuming normally distributed errors) gives the same results as minimising the sum of squared errors?
Exponential Smoothing
Q17. For an multiplicative error model, maximising the likelihood (assuming normally distributed errors) gives the same results as minimising the sum of squared errors?
Exponential Smoothing