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Financial Time Series Analysis in R
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Financial Time Series Analysis in R

Learn the fundamentals of analyzing a financial time series in R

This course provides an introduction to the financial times series data and how we can analyze the time series data in R.

  • You will learn about how to explore and build time-series data, calculate its key statistics, and plot time series charts.
  • You will also learn about how to use the important time series models such as White Noise, Random Walk, Autoregression, and Moving Average.
  • You will learn how to simulate these models in R and fit these models into financial time series data using the ARIMA functions.
  • Finally, you will learn about predictive modeling and how to use these models to predict the future.
  • We've provided various step-by-step examples using real financial time series data such as stock prices, and economic factors.

What's Included

  • Detailed concepts and explanations about each topic
  • Step-by-step instructions for all models built in R
  • All the data files used in the book
  • Complete downloadable R code for all examples used in the book

Lessons

01

Financial Time Series Data

Start
02

Exploring Time Series Data in R

Start
03

Plotting Time Series in R

Start
04

Handling Missing Values in Time Series

Start
05

Creating a Time Series Object in R

Start
06

Check if an object is a time series object in R

Start
07

Plotting Financial Time Series Data (Multiple Columns) in R

Start
08

Characteristics of Time Series

Start
09

Stationary Process in Time Series

Start
10

Transforming a Series to Stationary

Start
11

Time Series Transformation in R

Start
12

Differencing and Log Transformation

Start
13

Autocorrelation in R

Start
14

Time Series Models

Start
15

ARIMA Modeling

Start
16

Simulate White Noise (WN) in R

Start
17

Simulate Random Walk (RW) in R

Start
18

AutoRegressive (AR) Model in R

Start
19

Estimating AutoRegressive (AR) Model in R

Start
20

Forecasting with AutoRegressive (AR) Model in R

Start
21

Moving Average (MA) Model in R

Start
22

Estimating Moving Average (MA) Model in R

Start
23

ARIMA Modelling in R

Start
24

ARIMA Modelling - Identify Model for a Time Series

Start
25

Forecasting with ARIMA Modeling in R - Case Study

Start
26

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

Start
27

Financial Time Series in R - Course Conclusion

Start

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