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The current EGMS model estimates velocity, acceleration and seasonality at once and may therefore be needlessly complex, while in other cases it is insufficient to describe the deformation, such as with jumps or trend breaks. However, the selection of the appropriate model requires testing of a great many different models iteratively at each location, at great computational cost.
We extended the EGMS model, which only includes offset, velocity, acceleration and periodicity, to over 5000 model variations, including step functions and trend breaks. Subsequently, a best model for each time series is selected using the Bayesian Information Criterion (BIC). The parallelized methodology is applied to all time series within the most recent EGMS dataset and is displayed here.
For more information and contact details, see our poster from the ESA Living Planet Symposium '25.
Hypothesis Testing on a Continental Scale: GPU-Based Time Series Classification
