What is a White Noise model and how can you simulate it using R?

Recipe Objective

White Noise (WN) model is considered to be a basic time series model. White Noise model has a fixed constant mean and volume; and no correlation over time. It is also considered to be the basis for more elaborate models. ​

Learn Time Series Forecasting using ARIMA Model in Python

In this recipe, we will learn how to simulate a white noise model. ​

We will mainly focus on the simplest version of WN i.e. by taking independent with identically distributed data. ​

We will use arima.sim() function to simulate the WN model. ARIMA stands for Autoregressive integrated movinf average class of models. ​

Syntax: arima.sim(model, n) ​

where: ​

1. model = It has three parts (p = autoregressive, d = order of integration, q = moving average order). In a WN model, p=d=q=0
2. n = length of output series

# simulating a WN-model white_noise_model = arima.sim(model = list(order=c(0,0,0)), n = 300) #plotting the model using ts.plot ts.plot(white_noise_model)