- 16 Videos
- 58 Exercises
- 4 hours
- 3,040 Participants
- 4600 XP

**Instructor(s):**

David S. Matteson is Professor of Statistical Science at Cornell University and co-author of Statistics and Data Analysis for Financial Engineering with R examples.

Lore Dirick

Matt Isaacs

Many phenomena in our day-to-day lives, such as the movement of stock prices, are measured in intervals over a period of time. Time series analysis methods are extremely useful for analyzing these special data types. In this course, you will be introduced to some core time series analysis concepts and techniques.

This chapter will give you insights on how to organize and visualize time series data in R. You will learn several simplifying assumptions that are widely used in time series analysis, and common characteristics of financial time series.

- Welcome to the Course! 50 xp
- Exploring raw time series 100 xp
- Basic time series plots 100 xp
- What does the time index tell us? 100 xp
- Sampling Frequency 50 xp
- Identifying the sampling frequency 100 xp
- When is the sampling frequency exact? 50 xp
- Missing values 100 xp
- Basic Time Series Objects 50 xp
- Creating a time series object with ts() 100 xp
- Testing whether an object is a time series 100 xp
- Plotting a time series object 100 xp

In this chapter, you will conduct some trend spotting, and learn the white noise (WN) model, the random walk (RW) model, and the definition of stationary processes.

In this chapter, you will review the correlation coefficient, use it to compare two time series, and also apply it to compare a time series with its past, as an autocorrelation. You will discover the autocorrelation function (ACF) and practice estimating and visualizing autocorrelations for time series data.

In this chapter, you will learn the autoregressive (AR) model and several of its basic properties. You will also practice simulating and estimating the AR model in R, and compare the AR model with the random walk (RW) model.

In this chapter, you will learn the simple moving average (MA) model and several of its basic properties. You will also practice simulating and estimating the MA model in R, and compare the MA model with the autoregressive (AR) model.