Time series analysis uses statistical techniques to determine how a sequence of numerical data points varies during a specific period of time. For example, time series analysis is used in the financial sector to track the prices of securities, commodities, and other assets to discover how they may vary seasonally. These techniques are important in business, science, public opinion polling, or any other field which seeks to understand how things change over time.

The insights from time series analysis are often used for time series forecasting, which seeks predictions in the future changes in the value of a variable based on its movement in the past. Financial modeling and other applications of these techniques may also use linear regression to establish relationships between the variable of interest and other variables, such as overall economic factors like the unemployment rate or weather patterns.

Even more powerful prediction engines can be developed using machine learning and deep learning approaches. The use of TensorFlow, R, and Python programming for increasingly sophisticated applications of time series analysis is making it possible to uncover complex patterns in vast datasets that would have been impossible - or, at least, very difficult and time-consuming - in the not-too-distant past.

Time series analysis skills are important for a wide range of careers in business, science, journalism, and many other fields. Whether you’re a biologist seeking to understand seasonal growth of an invasive species population or a political scientist analyzing trends in support for a candidate over the course of a campaign, time series analysis is a fundamental tool for describing change.

The combination of time series analysis and time series forecasting is particularly useful in the world of finance, where quantitative analysts or “quants” that discover hidden correlations in the movements of stocks can generate incredible value for clients. According to the Bureau of Labor Statistics, analysts in securities, commodities, and other financial investments make a median annual salary of $98,690 per year and typically only have a bachelor’s degree, making this a high-paying job to begin your career in the business world.

Yes, absolutely! Coursera offers a breadth of learning opportunities across business as well as data science topics, including courses and Specializations that teach time series analysis skills. Wherever you live, you can learn about time series analysis remotely from top-ranked schools from around the world like the University of Pennsylvania, the State University of New York, Erasmus University Rotterdam, and The Hong Kong University of Science and Technology. You can also build skills in time series analysis by completing step-by-step tutorials alongside experienced instructors with Coursera’s Guided Projects.

Regardless of what you choose, learning on Coursera gives you the flexibility to view and complete course materials on your own schedule, making this ideal for students as well as mid-career professionals looking to add new skills to their resume.