RW (Random Walk)

Information

• The RW (Random Walk) algorithm is a time series forecasting technique that assumes that the future values of a time series are equal to the last observed value plus a random error term.
• The RW algorithm is based on the principle that a series of random shocks in a time series tends to maintain the direction of the series in the short term.
• The RW algorithm is widely used as a benchmark model for time series forecasting, especially for financial data, where it is used to test the predictive power of more advanced forecasting methods.
• The RW algorithm is easy to implement and requires no training or parameter tuning.
• The RW algorithm can be extended to the random walk with drift (RWD) model, which includes a non-zero constant term in the forecast equation.
• The RW algorithm can be used for both univariate and multivariate time series forecasting, as well as for forecasting seasonal or trend components of a time series.