Blog

Notes on time series, reinforcement learning, and applied machine learning.

• Driving through underground rocks

  Steering a drill bit through rock you cannot see: why a good particle filter still drifts, and how simulation fixed the drift.

• Can we really get alpha from market data?

  The efficient market view, the micro alpha counter-argument, and why a weak signal only becomes a position once you know its uncertainty.

• What works for forecasting macro economic series with deep learning?

  Korean output and investment nowcasting with seven deep models: what the data allows, which families worked, and why it depends on the target.

• Could multivariate time series have their own representations?

  Why forecast embeddings are not factors, and how identifiable innovations with diagonal dynamics recover them without losing forecast quality.

• Can we make a more risk-aware portfolio agent from utility theory?

  Epstein–Zin recursive utility inside actor–critic RL: the Bellman backup that changes, and what it did on Korean ETF splits.

• Classifying bird sounds in the field

  Sound as a picture: what a spectrogram is, why the mel scale matches how we hear, and how a network finds a bird call in the image.

• Creating and Evaluating Synthetic Tabular Data

  Sequential synthesis for credit bureau data, and three checks: pMSE distinguishability, confidence interval overlap, and attribute disclosure.