John Maxwell has completed

# Time Series Analysis in R

4 hours
4,600 XP

## Course Description

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.

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1. 1

### Exploratory time series data analysis

Free

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.

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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
2. 2

### Predicting the future

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.

3. 3

### Correlation analysis and the autocorrelation function

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.

4. 4

### Autoregression

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.

5. 5

### A simple moving average

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.

### GroupTraining 2 or more people?

Collaborators

Prerequisites

Intermediate R
David S. Matteson

Associate Professor at Cornell University

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