Time Series Decomposition
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Machine Learning
Time Series Analysis and Forecasting
Interview questions on Time Series Analysis and Forecasting.
31 questions
All subtopicsIntroductionTime Series GraphicsTime Series DecompositionBenchmark Forecasting MethodsTime Series Regression ModelsExponential SmoothingARIMA ModelsDynamic regression modelsProphet ModelVector Autoregressions
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Time Series Decomposition
Q2. Can a given time series posses more than one seasonal component?
Time Series Decomposition
Q3. What are the benefits of time series decomposition?
Time Series Decomposition
Q4. What kind of adjustments we can do with time series data to simplify the patterns in it?
Time Series Decomposition
Q5. Why is it recommended to make adjustments or transformations to time series data before decomposing it?
Time Series Decomposition
Q6. Why is it recommended to make adjustments or transformations to time series data before decomposing it? (Part 2)
Time Series Decomposition
Q7. What are some common mathematical transformations that can be applied to time series data?
Time Series Decomposition
Q8. What are the benefits of using mathematical transformations?
Time Series Decomposition
Q9. What is log transformation?
Time Series Decomposition
Q10. In which scenarios we should use log transformations?
Time Series Decomposition
Q11. What is power transformations?
Time Series Decomposition
Q12. What are Box-Cox transformations?
Time Series Decomposition
Q13. What are additive and multiplicative models in time series decomposition?
Time Series Decomposition
Q14. Explain additive model in time series decomposition?
Time Series Decomposition
Q15. When should we use additive model for time series decomposition?
Time Series Decomposition
Q16. Explain multiplicative model in time series decomposition?
Time Series Decomposition
Q17. When should we use multiplicative model for time series decomposition?
Time Series Decomposition
Q18. How do we determine whether to use an additive or multiplicative model?
Time Series Decomposition
Q19. How does a log transformation allow additive decomposition to approximate a multiplicative decomposition?
Time Series Decomposition
Q20. What is seasonally adjusted data?
Time Series Decomposition
Q21. Explain moving average smoothing in time series decomposition?
Time Series Decomposition
Q22. How does order $m$ of moving average impact the modelling?
Time Series Decomposition
Q23. In an m-order moving average, is symmetry important?
Time Series Decomposition
Q24. Explain weighted moving averages?
Time Series Decomposition
Q25. What is the major advantage of using weighted moving averages over m-MA?
Time Series Decomposition
Q26. How can we use m-MA for time series decomposition?
Time Series Decomposition
Q27. What are the limitations of classical time series decomposition?
Time Series Decomposition
Q28. How does STL decomposition work?
Time Series Decomposition
Q29. What are the advantages of using STL over classical decomposition?
Time Series Decomposition
Q30. What are the limitations of STL decomposition?
Time Series Decomposition