Introduction to Time Series
About this course
This course introduces basic time series analysis and forecasting methods. Topics include stationary processes, ARMA models, modeling and forecasting using ARMA models, nonstationary and seasonal time series models, state-space models, and forecasting techniques. By the end of this course, students will be able to: - Describe important time series models and their applications in various fields. - Formulate real life problems using time series models. - Use statistical software to estimate models from real data and draw conclusions and develop solutions from the estimated models. - Use visual and numerical diagnostics to assess the soundness of their models. - Communicate the statistical analyses of substantial data sets through explanatory text, tables, and graphs. - Combine and adapt different statistical models to analyze larger and more complex data.
68/100
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- 32/45
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- 20/35
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- 16/20
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What you'll learn
- Describe important time series models and their applications
- Formulate real-life problems using time series models
- Use statistical software to estimate models from real data
- Assess the soundness of models through visual and numerical diagnostics
- Communicate statistical analyses through explanatory text, tables, and graphs
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