Recruiter's Glossary

Examples: Ad Hoc Analysis • Data Mining • Data Cleansing

Time Series Analysis

Term from Data Analytics industry explained for recruiters

Time Series Analysis is a method data analysts use to study patterns in data that's collected over time - like monthly sales figures, daily website visits, or yearly temperature readings. Think of it as studying trends and patterns in numbers that occur in sequence over time to make predictions about what might happen next. It's similar to how weather forecasters predict tomorrow's weather by looking at past weather patterns. Companies use this to make business decisions, like predicting customer demand, planning inventory, or forecasting revenue.

Examples in Resumes

Conducted Time Series Analysis to forecast quarterly sales patterns, resulting in 25% more accurate inventory planning

Applied Time Series Analysis and Time Series Forecasting to predict customer behavior patterns

Led a team in implementing Time Series models to optimize supply chain operations

Typical job title: "Time Series Analysts"

Also try searching for:

Data Analyst Quantitative Analyst Data Scientist Forecasting Analyst Business Intelligence Analyst Statistical Analyst Predictive Modeling Analyst

Where to Find Time Series Analysts

Online Communities

Professional Networks

Job Boards

Example Interview Questions

Senior Level Questions

Q: How would you handle a time series analysis project with missing or irregular data?

Expected Answer: A senior analyst should discuss various approaches to handle data gaps, including interpolation methods, adjusting for seasonal patterns, and explaining how they would validate their approach to ensure reliable results.

Q: Can you explain how you would choose the right forecasting method for a business problem?

Expected Answer: Should demonstrate decision-making process based on data characteristics, business needs, and practical constraints, while explaining trade-offs between different approaches in simple terms.

Mid Level Questions

Q: What factors would you consider when analyzing seasonal business data?

Expected Answer: Should explain how they account for regular patterns like holiday seasons, weather effects, or business cycles, and how these impact predictions.

Q: How do you validate the accuracy of your time series predictions?

Expected Answer: Should discuss ways to check if predictions are reliable, such as comparing predictions to actual results and using different accuracy measures.

Junior Level Questions

Q: What is the difference between a trend and a seasonal pattern?

Expected Answer: Should be able to explain that a trend is a long-term direction (like steady growth over years) while seasonal patterns are regular cycles (like higher sales during holidays).

Q: How would you present time series analysis results to non-technical stakeholders?

Expected Answer: Should demonstrate ability to explain findings using simple visualizations and clear business language without technical jargon.