Exploring the Mechanics Behind Local Series in Time‑Series Forecasting

26 Eyl 18:45

I'm digging into the idea of a 'local series' as a way to model subsets of a larger time‑series dataset. Unlike global models that try to capture the whole pattern, local series focus on specific windows or regimes, often adapting more quickly to changing dynamics. I'm curious how practitioners define the boundaries of these local segments, what criteria are common for switching between them, and which statistical or machine learning techniques work best in practice. Any papers, tutorials, or personal experiences you can share would help a lot. How do you approach this in your projects? 😊