Collection of articles on specific topics.
Probabilistic Time-Series Analysis and Forecasting
Many consider forecasting to be just a regression problem with lagged observations as features. Therefore, point forecasts are often the cookie cutter approach as they are in cross-sectional problems.
I tend to disagree with this notion and hence write quite a bit about the issues of point forecasts:
- When Point Forecasts Are Completely Useless
- Why I prefer Probabilistic Forecasts - Hitting Time Probabilities
Probably the most important classical time-series model for dealing with stock-price returns.
- Random Forests and Boosting for ARCH-like volatility forecasts
- Multivariate GARCH with Python and Tensorflow
- Let's make GARCH more flexible with Normalizing Flows
- Varying Coefficient GARCH
More Time-Series Analysis
A bin for ideas that are occasionally popping up - not necessarily complete or work in progress.
- Simple Time-Series Models (W.I.P.)