April 2019
Intermediate to advanced
426 pages
11h 13m
English
Content preview from Mastering Python for Finance - Second Edition
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Types of non-stationary processes
The following points help to identify non-stationary behavior in time series data for consideration in transforming stationary data:
- Pure random walk: A process with a unit root or a stochastic trend. It is a non-mean reverting process with a variance that evolves over time and goes to infinity.
- Random walk with drift: A process with a random walk and a constant drift.
- Deterministic trend: A process with a mean that grows around a fixed trend, which is constant and independent of time.
- Random walk with drift and deterministic trend: A process combining a random walk with a drift component, and a deterministic trend.
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