Wold's decomposition, named after the Swedish mathematician Herman Wold, is a fundamental result in the field of time series analysis, particularly in the context of stationary processes. It essentially states that any stationary stochastic process can be represented as the sum of two components: a deterministic component and a stochastic component. Here's a more detailed explanation: 1. **Deterministic Component**: This part of the decomposition captures predictable patterns or trends in the data, which could include seasonal effects or long-term trends.
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