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Stationary Process in Time Series | Finance Train

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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 9 of 27

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Stationary Process in Time Series

A common assumption made in time series analysis is that one of the components of the pattern exhibited by a time series is the stationary series. This is the random or irregular component we discussed earlier. This random variation is not explained by any other factor.

There are three characteristics of a stationary series: