Hands-On Machine Learning for Algorithmic Trading

Time series transformations

To satisfy the stationarity assumption of linear time series models, we need to transform the original time series, often in several steps. Common transformations include the application of the (natural) logarithm to convert an exponential growth pattern into a linear trend and stabilize the variance. Deflation implies dividing a time series by another series that causes trending behavior, for example dividing a nominal series by a price index to convert it into a real measure.

A series is trend-stationary if it reverts to a stable long-run linear trend. It can often be made stationary by fitting a trend line using linear regression and using the residuals, or by including the time index as an independent variable ...