Uncertainty Quantification in Time Series Forecasting | by Jonte Dancker | Dec, 2024
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A deep dive into EnbPI, a Conformal Prediction approach for time series forecasting
10 min read
13 hours ago
Most of my recent articles revolved around knowing how sure a model is about its predictions. If we know the uncertainty of predictions, we can make well-informed decisions. I showed you how we can use Conformal Prediction to quantify a model’s uncertainty. I wrote about Conformal Prediction approaches for classification and regression problems.
For these approaches, we assume that the order of observation does not matter, i.e., that our data is exchangeable. This is reasonable for classification and regression problems. However, the assumption does not hold for time series problems. Here, the order of observations often contains important information, such as trends…
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