Finance Train

Library Blog Membership

Finance Train

Home

Learn

Getting Started

Python

R Programming $Quantitative Foundations$ Quantitative Foundations

Data Analysis

Machine Learning

Finance Apps

Blog Tools Membership

Forecasting with ARIMA Modeling in R - Case Study | Finance Train

Back to CoursePremium

Financial Time Series Analysis in R

27 lessons0%

Resources

Lessons

Financial Time Series Data

Exploring Time Series Data in R

Plotting Time Series in R

Handling Missing Values in Time Series

Creating a Time Series Object in R

Check if an object is a time series object in R

Plotting Financial Time Series Data (Multiple Columns) in R

Characteristics of Time Series

Stationary Process in Time Series

Transforming a Series to Stationary

Time Series Transformation in R

Differencing and Log Transformation

Autocorrelation in R

Time Series Models

ARIMA Modeling

Simulate White Noise (WN) in R

Simulate Random Walk (RW) in R

AutoRegressive (AR) Model in R

Estimating AutoRegressive (AR) Model in R

Forecasting with AutoRegressive (AR) Model in R

Moving Average (MA) Model in R

Estimating Moving Average (MA) Model in R

ARIMA Modelling in R

ARIMA Modelling - Identify Model for a Time Series

Forecasting with ARIMA Modeling in R - Case Study

Automatic Identification of Model Using auto.arima() Function in R

Financial Time Series in R - Course Conclusion

Lesson 25 of 27

Premium lesson

This lesson requires a premium membership to access.

Access all premium courses and features with one membership

Already have access? Sign in here

AI Instructor

Hello, how can I help today?

Ask questions about this lesson and get instant answers.

Forecasting with ARIMA Modeling in R - Case Study

In this lesson, we will take a new dataset (stock prices) and use all that we have learned to create a forecast using the ARIMA Models.

We will take the closing prices of Facebook stock for this example.

Step 1: Load the Data

We will load Facebook daily closing prices for the past 3 years from the WIKI/PRICES dataset in Quandl.