Time Series Analysis in R

Name: Time Series Analysis in R
Rating: 5 (3 reviews)

DataCamp

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Course Features

Duration

4 hours

Delivery Method

Online

Available on

Limited Access

Accessibility

Mobile, Desktop, Laptop

Language

English

Subtitles

English

Level

Intermediate

Teaching Type

Self Paced

Video Content

4 hours

Course Description

Intervals can be used to measure many phenomena that we encounter in our everyday lives, such as the movement of stock prices over time. These data types can be used to analyze time series analysis methods. This course will teach you the fundamental concepts and techniques of time-series analysis.

Course Overview

Virtual Labs

International Faculty

Post Course Interactions

Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Intermediate R

What You Will Learn

You will learn several simplifying assumptions that are widely used in time series analysis, and common characteristics of financial time series

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

You will discover the autocorrelation function (ACF) and practice estimating and visualizing autocorrelations for time series data

You will also practice simulating and estimating the AR model in R, and compare the AR model with the random walk (RW) model

Course Content

Expand all sections

Module 1: Exploratory time series data analysis

Module 2: Predicting the future

Module 3: Correlation analysis and the autocorrelation function

Module 4: Autoregression

Course Instructors

David S. Matteson

Associate Professor at Cornell University

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.

Course Reviews

Average Rating Based on 3 reviews

5.0

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$12