The purpose of this reppository is learning to identify a random walk in a time series analysis. In order to do that, the definition and the process to identify this sort of series is presented. The next diagram shows the general process:
- Gather data: here we'll create a random walk with uniform random numbers.
- Stationary: an statistical tesy will be applied, it's called Augmented Dickey-Fuller (ADF) test.
- Transformations: the difference transformation will be used.
- ACF and autocorrelation: it means Autocorrelation Function. This plot will generate the correlation coefficients of the data. And they are used to evaluate correlation.
Bibliography:
- Peixeiro, M. (2022). Time Series Forecasting in Python (1st ed., Chapter 3, pp. 30-60). Manning Publications.