Simulate the random walk model
The random walk (RW) model is also a basic time series model. It is the cumulative sum (or integration) of a mean zero white noise (WN) series, such that the first difference series of a RW is a WN series. Note for reference that the RW model is an ARIMA(0, 1, 0) model, in which the middle entry of 1 indicates that the model's order of integration is 1.
The arima.sim() function can be used to simulate data from the RW by including the model = list(order = c(0, 1, 0)) argument. We also need to specify a series length n. Finally, you can specify a sd for the series (increments), where the default value is 1.
This is a part of the course
“Time Series Analysis in R”
Exercise instructions
- Use
arima.sim()to generate a RW model. Set themodelargument equal tolist(order = c(0, 1, 0))to generate a RW-type model and setnequal to100to produce 100 observations. Save this torandom_walk. - Use
ts.plot()to plot yourrandom_walkdata. - Use
diff()to calculate the first difference of yourrandom_walkdata. Save this asrandom_walk_diff. - Use another call to
ts.plot()to plotrandom_walk_diff.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Generate a RW model using arima.sim
random_walk <- arima.sim(model = ___, n = ___)
# Plot random_walk
# Calculate the first difference series
random_walk_diff <-
# Plot random_walk_diff
This exercise is part of the course
Time Series Analysis in R
Learn the core techniques necessary to extract meaningful insights from time series data.
In this chapter, you will conduct some trend spotting, and learn the white noise (WN) model, the random walk (RW) model, and the definition of stationary processes.
Exercise 1: Trend spotting!Exercise 2: Random or not random?Exercise 3: Name that trendExercise 4: Removing trends in variability via the logarithmic transformationExercise 5: Removing trends in level by differencingExercise 6: Removing seasonal trends with seasonal differencingExercise 7: The white noise (WN) modelExercise 8: Simulate the white noise modelExercise 9: Estimate the white noise modelExercise 10: The random walk (RW) modelExercise 11: Simulate the random walk modelExercise 12: Simulate the random walk model with a driftExercise 13: Estimate the random walk modelExercise 14: Stationary processesExercise 15: Stationary or not?Exercise 16: Are the white noise model or the random walk model stationary?What is DataCamp?
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