TIME SERIES ANALYSIS INTERVIEW QUESTIONS
1. What is Time series analysis?
Time series analysis is a statistical method used to analyze and interpret data points collected or recorded at successive, equally spaced time intervals. It involves identifying patterns, trends, and relationships within the data to make predictions or forecasts about future values.
2. What are some common components of time series data?
Common components of time series data include trend, seasonality, cyclicality, and irregular fluctuations (noise).
3. How do you differentiate between autocorrelation and partial autocorrelation?
Autocorrelation measures the correlation between a time series and its lagged values, while partial autocorrelation measures the correlation between a time series and its lagged values after removing the effects of intervening observations.
4. What is the difference between ARIMA and SARIMA models?
ARIMA (AutoRegressive Integrated Moving Average) models are used for time series forecasting and consist of autoregressive (AR), differencing (I), and moving average (MA) components. SARIMA (Seasonal ARIMA) models extend ARIMA models to incorporate seasonal patterns in the data.
5. Explain the concept of stationarity in time series analysis?
Stationarity refers to the property of a time series where the mean, variance, and autocorrelation structure do not change over time. Stationary time series are easier to model and forecast compared to non-stationary time series.
Recommended by LinkedIn
6. What are some methods for identifying and removing trends and seasonality from time series data?
Common methods include differencing to remove trends, seasonal decomposition to separate seasonal and trend components, and exponential smoothing techniques.
7. How do you evaluate the performance of a time series forecasting model?
Performance evaluation metrics for time series forecasting models include Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and forecast accuracy measures such as forecast bias and forecast precision.
8. What is the purpose of cross-validation in time series analysis?
Cross-validation is used to assess the performance and generalization ability of time series forecasting models by partitioning the data into training and testing sets. Time series-specific cross-validation techniques include rolling origin and walk-forward validation.
9. How do you handle missing values or outliers in time series data?
Missing values can be imputed using methods such as forward fill, backward fill, interpolation, or mean imputation. Outliers can be identified and treated using techniques like winsorization, trimming, or modeling them separately.
10. What are some common techniques for time series forecasting?
Common techniques include ARIMA models, exponential smoothing methods (such as Holt-Winters), seasonal decomposition methods (such as STL), machine learning algorithms (such as Random Forests or Gradient Boosting Machines), and deep learning models (such as Recurrent Neural Networks or Long Short-Term Memory networks).