- 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
AutoRegressive (AR) Model in R
AutoRegressive (AR) model is one of the most popular time series model. In this model, each value is regressed to its previous observations. AR(1) is the first order autoregression meaning that the current value is based on the immediately preceding value.
We can use the
arima.sim() function to simulate the AutoRegressive (AR) model.
Note that model argument is meant to be a list giving the ARMA order, not an actual arima model. So, for the AutoRegressive model, we will specify model as
list(ar = phi) , in which phi is a slope parameter from the interval (-1, 1).
Below we create two sets of simulations with AR model, one with a slope of 0.5 and another with a slope of 0.8.
# Simulate AutoRegressive model with 0.5 slope AR_1 <- arima.sim(model = list(ar = 0.5), n = 200) # Simulate AutoRegressive model with 0.8 slope # AR_2 <- arima.sim(model = list(ar = 0.9), n = 200) plot.ts(cbind(AR_1 , AR_2 ), main="AR Model Simulated Data")
It is important to note that Random Walk is a special case of AutoRegressive models, where the slope parameter is equal to 1.
ACF and PACF of Autoregressive Model
We can calculate the Autocorrelation and Partial Autocorrelation Functions of the Autoregressive model using the
acf() and the
The following are the respective ACF and PACF plots for the
> acf(AR_1) > pacf(AR_1)
Characteristics of AutoRegressive Model
Persistence: The slope in an AR model can range from -1 to 1. As the slope gets closer to 1, the model shows higher persistence, i.e., it shows higher correlation with previous values. Also, the higher the slope, the slower is the decay of ACF to 0.
Oscillatory behavior: This refers to a large amount of variation between an observation and its lag. As the slope reduces, the AR model exhibits oscillatory behavior.