Handling Missing Data in Time Series Records (November 2024) скачать в хорошем качестве

Handling Missing Data in Time Series Records (November 2024)

In real-world datasets, like rainfall, streamflow, temperature, or pumping rates, missing data points are a common problem. When important observations are missing—especially during critical events like rainstorms—the gaps in our data can lead to misleading results, especially if unintended interpolations introduce false continuity. In this webinar, we’ll explore practical methods for addressing missing data in ways that help preserve the integrity of your model. I’ll demonstrate two key approaches using precipitation time series data: Simple Interpolation Techniques: See how best practices for the Time Series element can be applied to manage gaps without introducing artificial continuity. Stochastic Imputation: Discover how a stochastic model-based imputation can intelligently fill in missing values, informed by historical patterns.