143 results for "time series analysis"

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.‎

Skills you typically need when starting to learn time series analysis include technical skills and math skills. The technology is used for collecting and organizing data sets into usable information. In the area of math, you generally need to have some experience in statistics. It may also help if you have experience reading graphs and charts to interpret information. You might also have to use your technical skills to download software programs, such as R, that are needed for time series analysis learning. Python, machine learning, AI, statistics, and linear algebra are some other skills you may need to possess along with an interest in data analysis.‎

People who are curious and who have a passion for learning are typically best suited for time series analysis. People who are highly analytical may enjoy this field of work. Time series analysis can also be well suited to people who enjoy exploring advanced mathematical models and using highly technical computer programs. These people can come from many other technical fields, such as engineering or science. Additionally, people who are involved with AI-powered algorithms can be well suited for roles in time series analysis.‎

Time series analysis could be right for you if you enjoy learning about topics like statistical hypothesis testing and if you have an appreciation for complex mathematical models using statistics. If you find solving complex puzzles interesting, time series analysis could be right for you. Time series analysis could also be right for you if you’re working in a field that delivers large amounts of data and there is no good way to process that data. Learning time series analysis could potentially help you close that data processing gap.‎

Some career paths for someone in time series analysis include science, business, and engineering. Data analysts, financial analysts, and business analysts are also common career paths in this field. It can also typically be applied by people who specialize in machine learning and AI. Research is another common career path in this field. Time series analysis can be used by researchers to create models which can then be used to solve real-world problems.‎