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.
Course Overview
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Skills You'll Learn
Time Series Analysis And Forecasting, Statistical Analysis, Data Analysis in R
From the lesson
Module 1: Course Introduction and Intuition for Stationarity
Welcome to Introduction to Time Series! In this module we'll define time series and time series models, and we'll develop some intuition for the fundamental concept of stationarity, and why it's useful.
Taught By
Trevor Leslie
Assistant Professor of Applied Mathematics