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Time Series Analysis in R

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5

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$12

$6

49% OFF

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Course Features

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Duration

4 hours

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Mobile, Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Intermediate

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Teaching Type

Self Paced

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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

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Virtual Labs

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International Faculty

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Post Course Interactions

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Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

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Intermediate R

What You Will Learn

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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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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

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

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

Course Instructors

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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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Course Cover

$12

$6

49% OFF

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