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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

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 20 of 27

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Forecasting with AutoRegressive (AR) Model in R | Finance Train

Forecasting with AutoRegressive (AR) Model in R

Now that we know how to estimate the AR model using ARIMA, we can create a simple forecast based on the model.

Step 1: Fit the model

The first step is to fit the model as ARIMA(1, 0, 0). We have already seen this in the previous lesson.

> msft_ar <- arima(msft\_ts,c(1,0,0))