Time Series :: Part-1 :: Interview Questions & Answers

Here are some interview questions and sample answers related to time series:

Ques1: What is a time series, and how is it different from cross-sectional data?

Answer: A time series is a sequence of observations recorded over time, usually at regular intervals. Time series data is different from cross-sectional data in that it tracks changes over time, while cross-sectional data is collected at a single point in time and typically captures information about a single population or sample.

Ques2: What are some common methods used for time series forecasting?

Answer: Some common methods used for time series forecasting include ARIMA models, exponential smoothing, and machine learning algorithms such as random forests and neural networks.

Ques3: What is stationarity, and why is it important in time series analysis?

Answer: Stationarity refers to the statistical properties of a time series remaining constant over time, including the mean, variance, and autocorrelation. It is important in time series analysis because many modeling techniques and statistical tests assume that the data is stationary, and failing to account for non-stationarity can lead to inaccurate results.

Ques4: How do you identify trends and seasonality in a time series?

Answer: Trends can be identified by plotting the time series data and looking for patterns of upward or…