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As a first step, I would check whether this time series is autoregressive, that is, of the form $$ y_t = c + \phi_1 y_{t-1} + \ldots + \phi_p y_{t-p} + \varepsilon_t. $$ If this is the only feature of your data, then you should have stationary residuals $\varepsilon$.


Simulate Random Walk Series We can now simulate a random walk series in R as shown below: RW <- arima.sim(model= list(order = c(0, 1, 0)), n=200) We can plot the newly generated series as well using the plot.ts() function. plot.ts(RW,main="Random Walk", col=4) Random Walk with Drift RW_drift <- arima.sim(model= list(order = c(0, 1, 0)), n=...

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