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Machine Learning

Time Series Analysis and Forecasting

Interview questions on Time Series Analysis and Forecasting.

150 questions

All subtopicsIntroductionTime Series GraphicsTime Series DecompositionBenchmark Forecasting MethodsTime Series Regression ModelsExponential SmoothingARIMA ModelsDynamic regression modelsProphet ModelVector Autoregressions

Introduction

Q3. State the difference between univariate and multivariate time series?

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Introduction

Q4. Define following terms:

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  • Short-term forecasts
  • Medium-term forecasts
  • Long-term forecasts

Introduction

Q5. List down factors on which predictability of an event or quantity depends?

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Introduction

Q6. Is it correct to assume that forecasts are not possible in a changing environment?

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Introduction

Q7. How would you approach forecasting if there is no available data, or if the data you have is not relevant to the forecasts?

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Introduction

Q8. When can we use Quantitative methods for forecasting use-cases?

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Introduction

Q9. State the difference between exogenous and endogenous variables?

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Time Series Graphics

Q10. Which is the most common plot in time series EDA?

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Time Series Graphics

Q11. What is the difference between seasonal plot and time plot?

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Time Series Graphics

Q12. What is the difference between seasonal plot and time plot? (Part 2)

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Time Series Graphics

Q15. What is seasonal subseries plots? (Part 2)

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Time Series Graphics

Q16. Can we use scatter plots for time series EDA?

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Time Series Graphics

Q17. What is the difference between correlation and autocorrelation?

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Time Series Graphics

Q18. What is the autocorrelation function (ACF)?

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Time Series Graphics

Q19. Write the expression for autocorrelation?

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Time Series Graphics

Q20. Define following terms: (Part 2)

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  • Trend
  • Seasonal
  • Cyclic

Time Series Graphics

Q21. How can we check for trend in time series data using ACF plots?

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Time Series Graphics

Q22. How can we check for seasonality in time series data using ACF plots?

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Time Series Graphics

Q23. How does the ACF plot looks like if data has both trend and seasonality?

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Time Series Graphics

Q24. What does white noise mean in time series?

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Time Series Graphics

Q25. What are the statistical properties of white noise?

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Time Series Graphics

Q26. How could you check if the given time series is white noise?

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Time Series Decomposition

Q27. What is time series decomposition?

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Time Series Decomposition

Q28. Can a given time series posses more than one seasonal component?

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Time Series Decomposition

Q29. What are the benefits of time series decomposition?

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Time Series Decomposition

Q30. What kind of adjustments we can do with time series data to simplify the patterns in it?

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Time Series Decomposition

Q31. Why is it recommended to make adjustments or transformations to time series data before decomposing it?

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Time Series Decomposition

Q32. Why is it recommended to make adjustments or transformations to time series data before decomposing it? (Part 2)

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Time Series Decomposition

Q33. What are some common mathematical transformations that can be applied to time series data?

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Time Series Decomposition

Q34. What are the benefits of using mathematical transformations?

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Time Series Decomposition

Q36. In which scenarios we should use log transformations?

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Time Series Decomposition

Q39. What are additive and multiplicative models in time series decomposition?

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Time Series Decomposition

Q40. Explain additive model in time series decomposition?

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Time Series Decomposition

Q41. When should we use additive model for time series decomposition?

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Time Series Decomposition

Q42. Explain multiplicative model in time series decomposition?

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Time Series Decomposition

Q43. When should we use multiplicative model for time series decomposition?

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Time Series Decomposition

Q44. How do we determine whether to use an additive or multiplicative model?

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Time Series Decomposition

Q45. How does a log transformation allow additive decomposition to approximate a multiplicative decomposition?

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Time Series Decomposition

Q47. Explain moving average smoothing in time series decomposition?

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Time Series Decomposition

Q48. How does order $m$ of moving average impact the modelling?

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Time Series Decomposition

Q49. In an m-order moving average, is symmetry important?

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Time Series Decomposition

Q51. What is the major advantage of using weighted moving averages over m-MA?

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Time Series Decomposition

Q52. How can we use m-MA for time series decomposition?

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Time Series Decomposition

Q53. What are the limitations of classical time series decomposition?

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Time Series Decomposition

Q55. What are the advantages of using STL over classical decomposition?

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Time Series Decomposition

Q56. What are the limitations of STL decomposition?

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Time Series Decomposition

Q57. How can we use time series decomposition to measure the strength of trend and seasonality in a time series?

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Benchmark Forecasting Methods

Q58. What are some simple forecasting methods?

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Benchmark Forecasting Methods

Q59. Explain mean method in time series forecasting?

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Benchmark Forecasting Methods

Q60. How does naive method works in forecasting?

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Benchmark Forecasting Methods

Q61. How does seasonal naive method works in forecasting?

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Benchmark Forecasting Methods

Q62. How does drift method works in forecasting?

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Benchmark Forecasting Methods

Q63. What do you mean by residual in time series model?

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Benchmark Forecasting Methods

Q64. What properties should innovation residuals have to indicate a good forecasting method?

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Benchmark Forecasting Methods

Q65. What properties should innovation residuals have to indicate a good forecasting method? (Part 2)

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Benchmark Forecasting Methods

Q66. How do you determine the prediction interval for forecasted values?

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Benchmark Forecasting Methods

Q67. How do you determine the prediction interval for forecasted values? (Part 2)

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Benchmark Forecasting Methods

Q68. Express the standard deviation of the forecast distribution in case of one step prediction?

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Benchmark Forecasting Methods

Q69. What happens with prediction intervals in case of multi-step forecasting?

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Benchmark Forecasting Methods

Q70. For benchmark methods write the standard deviation expression for h-step forecast distribution?

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Benchmark Forecasting Methods

Q71. If the residuals from a fitted forecasting model do not exhibit a normal distribution, how would you establish prediction intervals for the forecasted values?

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Benchmark Forecasting Methods

Q72. Can time series decomposition be utilized for forecasting, and if so, what is the method for doing so?

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Benchmark Forecasting Methods

Q73. How does forecast errors differ from residuals?

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Benchmark Forecasting Methods

Q74. What are different techniques for measuring forecast accuracy?

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Benchmark Forecasting Methods

Q75. Is it feasible to apply the cross-validation technique to evaluate the accuracy of forecasts?

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Time Series Regression Models

Q76. What assumptions do we make when using a linear regression model for forecasting?

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Time Series Regression Models

Q77. Explain least squares principle?

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Time Series Regression Models

Q78. What are some typical predictors used in time series regression models?

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Time Series Regression Models

Q79. What is Akaike's Information Criterion (AIC)?

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Time Series Regression Models

Q80. How can the AIC score be interpreted?

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Time Series Regression Models

Q81. Why do we need to adjust bias in AIC score?

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Time Series Regression Models

Q82. What is Bayesian Information Criterion (BIC)?

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Time Series Regression Models

Q83. How does AIC differs from BIC?

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Exponential Smoothing

Q84. What are exponential smoothing methods?

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Exponential Smoothing

Q85. What is simple exponential smoothing method?

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Exponential Smoothing

Q86. What occurs in simple exponential smoothing when \(\alpha = 1\)?

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Exponential Smoothing

Q87. Write the component form of simple exponential smoothing?

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Exponential Smoothing

Q88. Why can’t we use the exponential smoothing method for data with seasonality and trend?

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Exponential Smoothing

Q89. How does Holt's linear trend method work?

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Exponential Smoothing

Q90. What is the main issue with Holt's linear trend method?

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Exponential Smoothing

Q91. Explain working of damped trend methods?

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Exponential Smoothing

Q93. 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 \( \phi < 1 \)?

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Exponential Smoothing

Q94. What are state space models in the context of time series analysis?

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Exponential Smoothing

Q95. What is difference between methods and models?

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Exponential Smoothing

Q96. What is the forecast error in simple exponential smoothing model?

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Exponential Smoothing

Q97. What is the forecast error in simple exponential smoothing model? (Part 2)

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Exponential Smoothing

Q98. Write the expression for SES with additive errors?

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Exponential Smoothing

Q99. For an additive error model, maximising the likelihood (assuming normally distributed errors) gives the same results as minimising the sum of squared errors?

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Exponential Smoothing

Q100. For an multiplicative error model, maximising the likelihood (assuming normally distributed errors) gives the same results as minimising the sum of squared errors?

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Exponential Smoothing

Q101. Write the expression of $AIC $,$ AIC_{c}$and$ BIC$ for ETS models?

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ARIMA Models

Q102. What is the main difference between ARIMA models and exponential smoothing models?

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ARIMA Models

Q103. What What do you mean by stationary time series?

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ARIMA Models

Q104. What What do you mean by stationary time series? (Part 2)

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ARIMA Models

Q105. When is a time series considered non-stationary?

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ARIMA Models

Q107. Is a time series that exhibits cyclic behavior but lacks any trend or seasonality considered stationary?

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ARIMA Models

Q108. How can we make non-stationary time series stationary?

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ARIMA Models

Q109. How does differencing transform a non-stationary time series into a stationary one?

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ARIMA Models

Q110. How can we stabilize the variance of a time series?

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ARIMA Models

Q111. How can one determine if a time series is stationary?

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ARIMA Models

Q115. Why is it important to avoid applying more differencing than necessary in time series analysis?

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ARIMA Models

Q116. How can we determine the required order of differencing?

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ARIMA Models

Q117. How can the $d$ th-order difference be expressed using backshift notation?

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ARIMA Models

Q119. What are the differences between a linear regression model and an autoregression model?

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ARIMA Models

Q120. What are the differences between a linear regression model and an autoregression model? (Part 2)

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ARIMA Models

Q121. State the expression of autoregressive model of order $p$?

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ARIMA Models

Q122. Why are autoregressive models typically restricted to stationary data?

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ARIMA Models

Q123. What are the parameter constraints for stationarity in AR(1) and AR(2) models?

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ARIMA Models

Q125. What is the difference between moving average model and moving average smoothing?

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ARIMA Models

Q126. In the context of ARIMA what does integration mean?

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ARIMA Models

Q130. Why do we need partial autocorrelation instead of just autocorrelation?

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ARIMA Models

Q131. How can we determine the parameters of ARIMA models?

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ARIMA Models

Q133. How does the value of \(d\) affect the prediction interval in ARIMA models?

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ARIMA Models

Q134. State the difference between ARIMA and ETS models?

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ARIMA Models

Q135. What are the differences between ARIMA models and regression models?

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Dynamic regression models

Q136. What are the limitations of regression and ARIMA models, and how do dynamic regression models address these needs?

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Dynamic regression models

Q137. How does dynamic regression models differ from regression models?

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Dynamic regression models

Q138. What must be checked before estimating a regression with ARMA errors?

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Dynamic regression models

Q139. How do you forecast using a regression model with ARIMA errors?

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Dynamic regression models

Q140. How do you forecast using a regression model with ARIMA errors? (Part 2)

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Dynamic regression models

Q141. What is dynamic harmonic regression?

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Dynamic regression models

Q142. What is benefit of dynamic harmonic regression?

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Dynamic regression models

Q144. What challenges arise when forecasting higher frequency time series data with complicated seasonal patterns?

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Prophet Model

Q145. What is the Prophet model, and what are its key features?

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Vector Autoregressions

Q147. How does the VAR model address stationarity and non-stationarity in time series data?

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Vector Autoregressions

Q148. Write the governing equation of 2-dimensional VAR(1) model?

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Vector Autoregressions

Q149. What are the challenges of dealing with weekly, daily and sub-daily data?

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Vector Autoregressions

Q150. How to handle missing values in time series?

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