Practical Time Series Analysis

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  • WEEK 1: Basic Statistics
    • During this first week, we show how to download and install R on Windows and the Mac. We review those basics of inferential and descriptive statistics that you'll need during the course.
  • Week 2: Visualizing Time Series, and Beginning to Model Time Series
    • In this week, we begin to explore and visualize time series available as acquired data sets. We also take our first steps on developing the mathematical models needed to analyze time series data.
  • Week 3: Stationarity, MA(q) and AR(p) processes
    • In Week 3, we introduce few important notions in time series analysis: Stationarity, Backward shift operator, Invertibility, and Duality. We begin to explore Autoregressive processes and Yule-Walker equations.
  • Week 4: AR(p) processes, Yule-Walker equations, PACF
    • In this week, partial autocorrelation is introduced. We work more on Yule-Walker equations, and apply what we have learned so far to few real-world datasets.
  • Week 5: Akaike Information Criterion (AIC), Mixed Models, Integrated Models
    • In Week 5, we start working with Akaike Information criterion as a tool to judge our models, introduce mixed models such as ARMA, ARIMA and model few real-world datasets.
  • Week 6: Seasonality, SARIMA, Forecasting
    • In the last week of our course, another model is introduced: SARIMA. We fit SARIMA models to various datasets and start forecasting.