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

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

This recipe explains what is a White Noise model and how can you simulate it using R

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.

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:

- model = It has three parts (p = autoregressive, d = order of integration, q = moving average order). In a WN model, p=d=q=0
- 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)
```

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