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In this course, you will :
- Discover several simplifying assumptions commonly used in time series analysis, as well as common characteristics of financial time series.
- Learn about the white noise (WN) model, the random walk (RW) model, and stationary processes.
- Learn about the autocorrelation function (ACF) and practise estimating and visualising autocorrelations using time series data.
- Learn about the autoregressive (AR) model and some of its fundamental properties. In addition, you will practise simulating and estimating the AR model in R, as well as comparing the AR model to the random walk (RW) model.
- Learn about the simple moving average (MA) model and some of its fundamental properties.
- In addition, you will practise simulating and estimating the MA model in R, as well as comparing the MA model to the autoregressive (AR) model.
Syllabus :
- Exploratory time series data analysis
- Predicting the future
- Correlation analysis and the autocorrelation function
- Autoregression
- A simple moving average
