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Autoregressive rollout and uncertainty propagation. Second in a series on probabilistic forecasting for physical signals. The post Your Model's MSE Is Lying to You: Part II appeare...
First in a series on probabilistic forecasting for physical signals. Next: what happens when you roll the forecast forward more than one step. The post Your Model’s MSE Is Lying to...
A diffusion head for probabilistic time series forecasting: why a forecast can get the mean and the variance right and still describe a future that never happens, and how to fix it...
Using a plain vanilla term spread model (spread, short rate), what remains? From notes for tomorrow’s lecture. Figure 1: Estimated recession probabilities from probit on 10yr-3mo t...
A fitted statsmodels model computes a more than just the array of numbers most code pulls out of it.
A single deep learning model trained across 528 stocks, built to forecast volatility.Continue reading on Medium »
When forecasting high-volatility financial assets like Bitcoin (BTC-USD), standard point-prediction models—whether classical ARIMA or deep neural networks—often fall short. A point...
It’s time for another one of Bob’s favorite sort of post, which is when I use simulation to demonstrate a statistical principle. 1. Flogging a dead horse The issue in question came...
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